• Accel India
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Dr. Ajay Sethi

Venture Partner at Accel

As a Venture Partner in Accel, Ajay has worked with almost all portfolio companies to help them achieve deliberate and sustainable growth. He started his entrepreneurship journey in 2005; his first startup was amongst the earliest VC-backed companies in India.

As a part of the Product Growth team, he ideates on strategy apart from hosting Masterclass and contributing regularly to PG Stories.

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Masterclass
Product Management
April 11, 2020
Swiggy’s Growth Formula that Drove 30x in 3 years!

Anuj Rathi (VP for Product, Revenue and Growth) is a senior product and growth leader at Swiggy. Anuj joined Swiggy in Aug 2016 when...

Dr. Ajay Sethi
20
min read
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Anuj Rathi (VP for Product, Revenue and Growth) is a senior product and growth leader at Swiggy. Anuj joined Swiggy in Aug 2016 when Swiggy is handling approximately 1.5 million deliveries per month. In Oct 2019, Swiggy announced that they were handling 1.5 million deliveries per day. In other words, Anuj has been part of the journey when Swiggy achieved 30x growth in 3 years!

Swiggy has been able to achieve this rapid growth not only with the help of exceptional talent (across engineering, product, marketing, operations, etc. teams) but, more importantly, with the help of a culture that is strongly anchored on “customer-backward thinking” (with high customer empathy), first-principles thinking, fast execution focused on raw problem-solving skills (complemented by strong data-driven experimentation), insane passion, and grit” (in Anuj’s words).

To understand Swiggy’s product and growth formula, we invited Anuj for the 2nd Masterclass by ProductGrowth.org. Before the session, we requested community members to tell us to know what they wanted to hear from Anuj and got the following response:

Based on the community feedback (and specific questions from the community members — both before and during the session), we had a conversation around building habits, driving scale, and effective growth architecture. It was a power-packed conversation, full of insights and suggestions about how other companies can drive efficient and sustainable growth. You can listen to the full conversation below:

Or, if you prefer, you can scan through the summary below. If you have any questions or suggestions, please do let us know by responding to this story below.

Reacting to the Covid19 crisis

Mindful of the fact that the startup community (indeed, everyone across the world!) is grappling with the unprecedented Covid19 crisis, we started the conversation by discussing how Swiggy was responding to the crisis.

This is an important question because, in the times of crisis, both the JTBD (Jobs to be Done) and GTBA (Goals to be Achieved; the “why” behind the JTBD; for example, the importance a person attaches to JTBD and other deeper requirements; see here for more details) can change dramatically. Given this, how can a startup or company respond quickly?

In this context, it is useful to highlight that Swiggy has reacted very quickly during the current crisis: starting with “zero contact deliveries”, Swiggy has launched new services to meet evolving customer needs. For example, Swiggy Stores for groceries and Swiggy Genie for instant pickup/drop service (to get groceries and other essentials from nearby stores). Given this, it is interesting to understand what has enabled Swiggy to iterate quickly and respond effectively to the rapidly evolving Covid19 crisis?

Anuj provided a two-part answer to this question.

The first one was related to customers-backward thinking. Swiggy uses a framework called “Accepted Customer Belief” (ACB) for this purpose. ACB is typically used by the marketing teams to understand how customers look at a product (and the broader product category). ACB is used to understand whether the company understands customers or not — especially, the target consumers’ frustrations of their unmet needs.

Swiggy has found that ACB is a wonderful tool for imbibing customers-backward thinking in the product teams. ACB also enables product teams to capture evolving customer needs and wants quickly. For example, before the Covid19 crisis, ACB for college students (to pick a specific persona) included: need for not-expensive food, extremely fast delivery, and desire for discounts. However, after the spread of the Covid19 crisis, their ACBs changed and were instead focused on safety (of the delivered food and the delivery partners). In addition, the speed of delivery became less important though the need for not-expensive food continued. In other words, though JTBD remained the same, the underlying requirements (GTBAs / expectations) changed significantly. In some sense, the changing environment needed a “new product” and Swiggy, indeed, iterated quickly to validate the PMF for this new incarnation of the product.

ACB framework, therefore, provides a quick and methodical way of understanding customer requirements — esp. the non-functional requirements underlying the tasks/activities they want to fulfill.

The second part was related to how to structure and organize teams so that the startup has the agility required to react quickly to a crisis (as well as the changing market dynamics, in general). We will take a look at this towards the end of the article.

Habit

We started the conversation on habits by pointing out the inapplicability of the popular Hooked framework (by Nir Eyal) for building habits for a majority of companies. The Hooked framework suggests that there are four levers for creating habits: Trigger — Action — Variable rewards — Investment. However, some of its levers (such as “action” and “investment”) can’t really be tweaked around much for a majority of the usage scenarios. Moreover, “variable reward” is also not typically available — in a lot of use-cases, the certainty of results (for example, “food delivery within 40 minutes”) is more important than the variability of rewards. In fact, non-variability and consistency of delivery is the “reward” that is valued by the customers. So, one is left only with a “trigger” as the habit-building lever! Perhaps this is the reason why Hooked framework inadvertently triggered a deluge of much-too-frequent and bordering-on-spam push notifications (“external triggers”) that did more harm than good to the companies trying their best to create the elusive habits.

Anuj pointed out that “growth hacking” — another Silicon Valley fad — is not useful either; it is typically associated with the narrow definition of growth: tactical growth focused on increasing DAUs, MAUs, etc. by running incremental experiments.

So, how can one build habits?

The starting point for building habits is to understand customer requirements deeply. As discussed above, this needs the company to understand both the JTBD and the “core assumptions” (ACBs) of the customers.

Based on this foundation, the first step is to build a product that works for consumers. This is critically important because several companies rush to increase usage by 20–30% (via growth hacking, etc.) before doing this.

Once this has been achieved, the next step is to understand the natural frequency of the activities under consideration. Now, Swiggy can consider itself to be either in the business of delivering restaurant food to customers (for example, as a substitute to eating-in at a restaurant; this would happen, say, 5–10 times a month) or in the business of delivering food (which can happen much more often because customers eat meals 3–4 times per day or 90–100 times a month).

In fact, one needs to look at the natural frequency together with the target persona (or segment). Target persona can incorporate not only behaviors and segments but also the customer’s value (i.e., CLTV). Note that the CLTV should not be the value the persona is providing right now but the maximum value they can ever provide (which, itself, depends on the maximum frequency of usage for the persona). Swiggy, for example, understands that there is a large difference between students and young professionals who are Snapchat users versus those who are not — these two sets vary significantly in terms of their CLTV (or “aukaat”, the colorful term used by Anuj). Swiggy goes further and understands that the same user can have different behaviors at different times: a user can be “speed seeker” on weekdays (while ordering food from the office) or “discount seeker” on weekends (when ordering lots of food for the whole family)!

Once the natural frequency for each persona is understood, the next question becomes: how can we help each persona increase their usage so that they get close to their natural frequency (which is the maximum usage frequency for the persona)?

In this context, Nir Eyal’s “triggers” become just one way of getting there. Another way could be building the best possible product and consistently delivering on the brand promise. This, then, gets people to start talking about the product/service and help spread the word. This can help make using a company’s product a social norm and, therefore, lead to building a habit!

With this view, habit is all about increasing the frequency of product usage until it reaches natural frequency. Habit, therefore, becomes a means to an end; the end remains to deliver value to customers and making sure their needs and goals are fulfilled.

Anuj used the example of the Super subscription program to explain how Swiggy actually leverages the above approach to habit building. Let’s look at the journey in detail.

The first question Swiggy asked was: what is preventing customers from ordering at or near natural frequency? For this, they spoke to customers who were “super users” — that is, who had been ordering fairly frequently (say, 15–20 times a month). With the help of user research, they discovered that almost all of them hated the delivery fee: either they didn’t like the very idea of delivery fee (especially because they realized they were giving good business to Swiggy) or (worse) they hated the bill shock they got at the end of the ordering session — especially when Swiggy levied higher surge delivery prices.

Second, while starting to explore a subscription program, Swiggy also looked at several variants of subscription programs: from a single-tier (paid) membership program (like Amazon Prime), multi-tiered membership program (like airline miles programs), earn-and-burn programs (like Flipkart Plus), and so on.

Third, with the goal to increase the frequency of usage, Swiggy looked at possible benefits that the membership could offer. For example, these were: faster delivery promise, higher discounts from customer’s favorite restaurants, higher overall discounts, free delivery without surge fees, etc.

Fourth, to understand how customers would react to these, Swiggy designed landing pages for various options and used them to simulate experience. Swiggy then tested various options with a small set of representative customers. Swiggy not only looked at the quantitative data to analyze how super users engaged with the simulated options but also interviewed them to get a qualitative feel about their reactions.

Finally, to choose between these options, Swiggy applied the RICE framework (where R = Reach, I = Impact, C = Confidence, and E = Effort) to perform a cost-benefit analysis and to pick the best candidate. For example, RICE analysis revealed that “faster delivery promise” not only required a lot of effort but also implied that faster delivery for super users might worsen the experience customers (because the delivery partners would get allocated to serve the super users and, therefore, might increase the delivery times for other customers). This implied that this option not only had high ‘E’ (effort) but also low ‘C’ (confidence).

Based on the above sequence of steps, Swiggy designed and launched with the current version of Swiggy Super with unlimited free deliveries, no surge fee during rains or high demand, and “free delights”.

Scale

How was Swiggy able to scale 30x in 3 years? What are the frameworks and playbooks that might be useful for others?

For the sake of the context, it is worth recalling the market landscape for the “food delivery” business in 2016. Following on the heels of the rapid growth of e-commerce adoption in 2015 and 2016, “food-tech” industry was super hot in 2016. There was strong competition amongst several energetic and skillful players. For example, there were companies such as TinyOwl (which provided beautiful user experience), Ola Cafe (which had the reach of Ola), FoodPanda (which had strong financial backing and access to international knowhow), etc. Also, UberEats was around the corner. The “incumbent” was Zomato, which had built India’s largest platform for restaurant reviews and ratings (and, therefore, had built strong network effects with “insurmountable moats”, using Warren Buffet terminology).

Therefore, there was an immense pressure on Swiggy to scale fast — while also trying to carefully balance the growth of 3-sided marketplace consisting of customers, restaurants, and delivery partners.

So, how did Swiggy grow 30x in such an intense environment?

Anuj explained that Swiggy did this by first figuring out the growth drivers and then deeply understanding the growth equation of its business.

What does this mean?

For Swiggy, its growth equation indicated that supply-side capabilities needed to be built and stabilized before unlocking the demand. More specifically, Swiggy discovered that the demand (and conversion rates, for example) was correlated with the density of restaurants in an area. For example, conversion rates were low when there were less than 80 restaurants in an area, the conversion rate increased as the restaurants increased from 80 to 225 and beyond; and, interestingly, conversion rates started falling if there were too many restaurants in an area (due to additional cognitive load as a result of too many choices, it turns out)!

Therefore, to launch in a new zone, Swiggy needed to first onboard ‘x’ number of restaurants and ‘y’ number of delivery partners and then kickstart the marketing engine to generate demand (which needed to generate at least ‘z’ orders for the engine to continue working smoothly). Doing so ensured that the customers had a great experience right from the start!

For a new zone (and a new city) launch, Swiggy uses a framework that Anuj referred to as ADS where:

  • Stands for Availability (which corresponds to the density of restaurant)
  • D stands for Discoverability (of restaurants and dishes which, in turn, depends on the richness of restaurant menus)
  • S stands for Serviceability (which corresponds to fulfillment and depends on the number of delivery partners)

Other marketplaces might also find variants of ADS to be useful to connect the supply-side drivers with demand-side drivers. This is because ADS facilitates a combination of supply-side analysis with demand-side behavior. Swiggy has found that ADS is strongly correlated with business metrics and helps to predict consumer behavior.

Anuj emphasized that Swiggy has always taken a customer-centric approach while scaling; Swiggy had resolved to scale only when the company was sure that it would not provide a poor experience to customers. This is why Swiggy had limited its operations to only 7–8 cities during the first few years of its journey.

How can other startups mimic Swiggy’s exponential growth trajectory? Anuj mentioned that once the growth equation is understood by a startup, its scale equation and growth roadmap would become clear as well. It becomes clear which levers need to be pulled to achieve scale.

Anuj also pointed out that startups should keep going back to their growth equation in order to continuously improve it. A startup’s growth equation keeps evolving based on customer behavior changes, macro-economic changes, market dynamics changes, etc.

Brand

We didn’t explicitly discuss Swiggy’s product-led brand activities. However, as part of Habit and Scale discussions, a few things did emerge that are relevant from the brand perspective.

It is important to highlight that brand should not be treated as synonymous with the brand logo or brand marketing campaigns. Brand, instead, should be considered as the tangible and verifiable promise that a startup makes to its customers and, subsequently, the product, operations (logistics) and other activities that the startup does to ensure that it consistently delivers on the promise.

ACB, that Anuj referred to, is one of the tools used in the marketing function to gain insights into why consumers do what they do. By acknowledging consumers’ point of view with understanding and empathy, startups can put together a tangible promise that not only highlights the benefits of the product but also gives them Reasons To Believe (RTBs, in the marketing speak).

Swiggy, right at the start of its journey, realized that the food delivery product needed a clear and simple articulation of its brand promise. Swiggy realized that consistency of delivery (within the promised delivery times) would help it build an early differentiator. Towards this, Swiggy launched the “Swiggy Assured” guarantee with “on time or on us” promise backed by “25% cashback” if the food wasn’t delivered within the promised duration (typically 30–40 minutes).

By consistently delivering on its promise, Swiggy had laid the foundations for building a strong brand. By ensuring awesome customer experience, Swiggy was able to build trust and unlock growth in its initial years. Anuj pointed out that Swiggy didn’t need to do any digital marketing for the first few years.

One of the observations that can be made based on our conversation was that brands can be strengthened by understanding the ACBs of the “super users” (i.e., the most engaged users). Swiggy did this while developing the Swiggy Super program; Anuj pointed out Swiggy spoke with several loyal customers while exploring options to recognize and reward the super users in the most effective manner.

An insightful side story shared by Anuj during the conversation was the unexpected benefits of running an India-wide brand marketing campaign in 2017 (when Swiggy was present in only 7–8 cities). The highly visible campaign resulted in “market spillage” and the company received eyeballs and visibility in the cities where it was not present. As a result, the campaign triggered consumers across India to download the Swiggy app to explore the service. The number of downloads from various cities, in turn, revealed latent demand across different cities. This provided useful signals for Swiggy to decide the order and priority of launching its operations across cities! Combining this demand-side metadata with a replicable go-live playbook (based on the ADS framework), Swiggy rapidly expanded to more than 500 cities by the second half of 2019.

Growth Team Architecture

How can a company structure and organize its teams so that it not only it can unlock and achieve its growth potential but also be agile and respond quickly and thoughtfully to the changing market dynamics? What has helped Swiggy to react quickly to a crisis such as Covid19?

Anuj explained the team architecture that provides a strong growth-oriented foundation to Swiggy (despite having grown rapidly over the last few years) and helps Swiggy to be extremely agile. To build and organize the teams, Swiggy looks at five parameters:

1. Impact

2. Speed

3. Confidence

4. Quality

5. Agility

Depending on the variation of needs across these five parameters, Swiggy has five different types of teams within Swiggy:

1. Core teams: work on platform capabilities (for example, search and logistics capabilities, for example) and drive medium-term projects (such as Swiggy Pop, Swiggy Super, etc.).

2. X teams: churn out quick, incremental experiments (for example, Swiggy “takeaway counter” at airports).

3. Growth teams: explore adjacencies and other growth avenues. Growth, for example, can come from category adjacencies (for example, grocery delivery), geographical adjacencies (for example, international expansion), JTBD adjacencies (for example, bike taxis), and so on.

4. Big bets (new businesses): explore new business opportunities and respond to evolving (macro-economic, competitive, etc.) climate.

5. Labs: explore moonshot initiatives and very long-term strategic projects; for example, exploring using drones, noddle-making machines, etc.

Note that the above teams are not “two-pizza teams” or “agile pods” or “scrum teams”. Unlike these team structures (which are used to provide agility within product-tech teams), the above is more deliberate and thoughtful team architecture that provides both short-term agility and long-term competitive edge to the company.

Anuj highlighted the differences with two-pizza teams, et al by explaining that each of the above teams has different DNAs. For example:

  • The core team is geared towards medium to high impact, low to medium speed, medium to high confidence, high quality, and low agility.
  • Big bets team is geared towards high impact, high speed, low confidence, medium quality and high agility.

Core team, for example, works with lots of data and thorough while Big bets team has to work with much less data and lot of intuition. Due to different charters and different focus areas, each of these five teams, naturally, have different DNAs. Since there is clarity in each team’s charter and clear expectations from each team, it is possible to pick the right processes and right metrics for each team.

Swiggy Pop and Swiggy Super, which were handled by the Core team, were thought-through and tested products when they were launched. On the other hand, Swiggy Stores and Swiggy Genie were built and launched quickly so that the product/service offerings could be quickly iterated upon and improved based on customer needs and feedback.

This team architecture makes it easier to staff various types of teams appropriately. To allocate resources appropriately across different teams, Swiggy decides annual and quarterly goals/targets and, depending on that, figures out initiatives and projects needed to meet those goals. This, therefore, helps to decide how to allocate product-marketing-tech resources across various teams. Anuj recommended referring to “Team Topologies” (link) book for more details in this regard.

This team architecture is what enables Swiggy to respond quickly to evolving marketing situations. And, it is this team architecture that enabled Swiggy to move so swiftly during the Covid19 crisis: it was able to make sure that the relevant teams were resourced properly and supported appropriately.

There are only four levers of growth for any company [link]:

  • Scale: supply-side and demand-side scale. In Swiggy’s case, one needs to include the scale of delivery partners as well.
  • Habit: as discussed, we use this term to refer to product-based and other activities that help increase frequency of usage so that it gets close to the “natural frequency”.
  • Brand: we use this term to refer company’s ability to meet its tangible and verifiable brand promise (which, in turn, results in customers developing emotional connect with company’s products). Emotional connect helps customers to become more positive inclined to continue to use company’s services (and reduces price sensitivity, for example).
  • Network effects: network effects can help Swiggy to strengthen the 3-sided marketplace even further.

In order to devise a growth roadmap, first and foremost, a company needs to understand its growth equations. Anuj repeatedly emphasized that PMs should strive hard towards figuring out and refining their growth drivers/levers as well as the growth equation. Anuj explained how Swiggy uses ADS framework for this.

Next question is: how does one come up with the right growth roadmap?

Anuj pointed out that organizations where business goals are set by the “management” and then cascaded down to teams across the organization are not able to perform optimally. If the goals flow only from top to bottom, the company is not able to leverage full potential of PMs (as well as engineers and other operators in the company) that work in the trenches and have a more accurate understanding of the ground realities as well as more realistic view of future possibilities.

Anuj emphasized that the best teams (especially, product and growth teams) build roadmaps and set goals using a combination of top-down and bottom-up discussions. Swiggy, for example, starts with a bottom-up process where teams come up with their ideas and suggest possibilities. Product leaders collate these ideas and convert them into projects. The projects are used to devise various growth scenarios. These scenarios are discussed with the senior leaders where they are considered in the context of strategic directions (such as customer experience goals, future goals and targets, competitive pulls-and-pushes, fundamental requirements, etc.). Based on the strategic directions, all the proposed projects are classified into different buckets, their tradeoffs are discussed, and prioritized appropriately.

OKR is one of the most popular techniques used by companies worldwide to merge top-down cascading and bottom-up project propagation. In this context, we discussed Swiggy’s experience with the OKR process.

Anuj pointed out that OKR planning doesn’t work well for Swiggy. This is because the growth equation at Swiggy is not simple — various growth levers are dependent of each other; in fact, various growth levers are deeply intertwined with each other.

Given that Swiggy is a 3-sided marketplace, its growth equation depends on all three moving parts. The three sides of the marketplace themselves are highly interlinked — metrics of different sides are closely tied to each other and every metric has potentially large impact on the overall success metric.

Anuj suggested that OKRs work well if a company has simpler growth equation with independent growth levers. Independent growth levers make it possible for different teams to have independent and complementary goals. Independent goals and metrics — especially when the metrics are additive (and not interlinked and interdependent) — make it possible to split company-wide goals into sub-goals and to cleanly distribute the sub-goals to different teams. Recursively, each team is also able to map its goal (the sub-goal owned by it) into smaller goals (and subsequently into potential projects). Of course, if there are some dependencies, it is possible to ensure that the teams talk to each other and resolve the dependencies.

Marketplaces with interlinked parts (and interdependent metrics) render OKR process ineffective. Anuj referred to this as the “butterfly effects” and, as an example, mentioned that a small variation in earnings per hour of delivery executive has been observed to have an impact on conversion rate (on consumer side)!

Due to the intertwined nature of its products/projects, Swiggy looks for win-win-win while evaluating possible projects. Each project is evaluated from the perspective of whether it would provide a ‘win’ for consumers, a ‘win’ for restaurants, and a ‘win’ for delivery partners.

Consider the example of Swiggy Pop, which is Swiggy’s offering of single-serve meals. Customers ‘win’ because of fewer choices and the ease-of-use. Restaurants ‘win’ because Swiggy provides predictable order requirement with partner restaurants in advance. Delivery partners ‘win’ because they are able to batch more orders and do more deliveries. And, by delivering value to each of its stakeholders, Swiggy ‘wins’.

Anuj pointed out that it is not easy to pick one or two metrics for projects such as Swiggy Pop. This is because the success of Pop relies on all parts of the 3-sided marketplace. Moreover, it is not possible for any single PM to own such a product. Projects such as Swiggy Pop needed end-to-end teams (and not just ‘two-pizza teams’ spread across product and tech teams).

Finally, Anuj suggested that it is good to consider product roadmaps with three different horizons:

  • 18 months perspective about key levers in the business and how they might evolve (this is useful to figure out roadmap for new initiatives)
  • 6 months visibility on business metrics and goals
  • 3 months: a detailed quarterly product roadmap and the corresponding execution plan

April 11, 2020

Swiggy’s Growth Formula that Drove 30x in 3 years!

Anuj Rathi (VP for Product, Revenue and Growth) is a senior product and growth leader at Swiggy. Anuj joined Swiggy in Aug 2016 when...

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Masterclass
Marketing
June 27, 2020
How to build Digital-first Brands: The Flipkart Story

Everyone in the startup ecosystem has heard about the main Flipkart story… and it is a fascinating story because it had many interesting...

Dr. Ajay Sethi
18
min read
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Trishul, Kumaon range in Himalayas [credit: link]

Everyone in the startup ecosystem has heard about the main Flipkart story… and it is a fascinating story because it had many interesting characters, a lot of suspense and many sub-plots. Flipkart’s efforts to build a brand that was accessible to all Indians — not just to consumers in metros but also to consumers in Tier 2, 3, 4 cities across India — was a wonderful sub-plot. Shoumyan Biswas spent six fruitful years with Flipkart between 2013 and 2019 and was the Chief Marketing Officer (CMO) for Flipkart starting 2017 and CMO and Business Head of Loyalty, Partnerships & Advertising for Flipkart towards the end. We requested Shoumyan to share details about this aspect of Flipkart’s journey.

The insightful conversation in its entirety can be view below:

Or, you can scan through the main points from the conversation below.

At a high-level, Flipkart’s pre-Walmart acquisition journey can be divided into three phases:

  • Phase 1 was the foundational phase between 2008 and 2014. Flipkart initially focused on selling books, building India-wide distribution network, and providing great customer experience to early adopters of the service. After cracking that, Flipkart expanded to electronics in 2011 and acquired Myntra in 2014. Flipkart held its first Big Billion Day sale in 2014.
  • Phase 2 was the intense competition phase: Amazon launched its marketplace in India in 2013 and, in 2014, announced that it will invest at least $2B to grow its footprint in India. So, the race to dominate Indian e-commerce started in earnest in 2014. Between 2015 and 2017, there was an intense fight between Flipkart and Amazon.
  • Phase 3: By early 2018, it was becoming clear that Flipkart would be able to hold its ground against the much larger and much stronger competitor. This led to the consolidation phase for Flipkart. In this phase, it was important for Flipkart to strengthen its lead and to ensure that the advantage does not slip away.

Phase 1

In the first phase, Flipkart had laid for building a strong brand by focusing on providing great customer experience. Flipkart also used Cash-on-Delivery (CoD) to build trust with customers.

Shoumyan mentioned three lessons and guidelines from these early efforts:

[1]

For digital-first brands (and increasing for all brands), “brand” is what one consumer tells another about the company (as opposed to pre-social media days when “brand” used to be what companies proclaimed themselves to be).

What used to be one-way communication from brands to consumers two decades ago has evolved into two-way communication between brands and consumers. Social media has not only provided a voice to the consumers but have also increased their circle of influence. This implies that brands need to consciously shape consumer narrative around the brand.

Based on this understanding, Flipkart’s marketing team had dual charter:

1. Make people transact with the brand

2. Make people to talk about the brand

Flipkart consciously worked to drive conversations about Flipkart amongst consumers.

[2] Brands — just like two human beings — go through stages of evolution. These stages are:

Unknown → Known → Known for something → Trusted → Loved

While building brands, marketers should not use marketing activities only for tactical reasons (e.g., using TV commercials to build awareness or leveraging discounts to get volumes). A strategic approach is to align the actions with the stage of the brand.

How did Flipkart move across these stages?

  • Unknown → Known: Flipkart relied on superior quality of service and concomitant word-of-mouth in the initial stages. In the next stage, Flipkart leveraged performance marketing and communication channels (including advertising). This is when Flipkart identified the “kidult” theme (kids shown as grownup adults).
  • Known → Known for something: Flipkart worked on making itself a destination for online shopping that was as easy as a child’s play. It also promoted itself as the destination for buying the latest and the coolest smartphones.
  • Known for something → Trusted: Flipkart worked to communicate that consumers can’t go wrong with Flipkart. In order to make itself as a trusted destination for e-commerce across the whole country, Flipkart emphasized cash-on-delivery, original products, etc. This was reiterated by the “Flipkart Assured” program and supported by the “Flipkart matlab bilkul pakka” campaign.
  • Trusted → Loved: It is important to move beyond the transactional value for this phase. Flipkart did this by thoughtfully identifying the “purpose” context that was aligned with its mission. Flipkart emphasized that it was an Indian brand that stood for everything that makes India progressive. For example, Flipkart embraced diversity of India. Shoumyan mentioned that one of his favorite themes of this phase were the “Penguin dad” campaign that celebrated fathers helping out their wives by sharing the parenting responsibilities.

How can a brand measure whether or not it is making progress across these stages? Shoumyan mentioned the following metrics:

  • Unknown → Known: search volume, top-of-mind recall, and spontaneous recall (brand should be amongst the top 3–4 most aware players in the chosen category).
  • Known → Known for something: consumer perception on key attributes (in order to measure that Flipkart as the destination for the coolest/latest smartphones, the scores should be the highest amongst all players), social NPS (to reflect that consumer voice is building up), acquisition funnel measures (for example, CAC and cost-of-installs should decrease).
  • Known for something → Trusted: measurement about trust attributes and levers (whether they are going up or not; for example, what percentage of sales come from “Flipkart assured”, “original products”, etc. anchors). Also, in this stage, spontaneous recall should be in mid- to late-90s and almost everyone should knows what the brand stands for. In addition, the brand should get higher repeats leading to higher LTV (life-time value) of customers.
  • Trusted → Loved: customers should become more forgiving of mistakes and less price sensitive.

[3] Non-digital (“traditional”) brands operate with the think-do-think model; however, digital-first brands need to adopt do-think-do model of execution. This is because, unlike traditional brands (say, FMCG brands), launch of a product can take 18 months. Digital world moves much faster and, therefore, digital-first brands need to “fail fast and learn faster”.

Phase 2

In 2015, Flipkart had ~45% marketshare while Amazon had ~10% marketshare. This changed dramatically in 2016: Flipkart’s share reduced to ~35%, Amazon grew to ~25%. Flipkart was rapidly losing the marketshare to Amazon… and, from the outside, it looked like Amazon might win the war. But then something changed. Flipkart was not only able to hold on to its marketshare and but slowly increase it. What triggered this turnaround?

Shoumyan pointed out that Flipkart started by turnaround by fixing the mistakes that had crept into its consumer strategy. In order to improve unit economics (to move quicker towards profitability) and to enhance its reach, Flipkart had adopted the marketplace strategy for expansion. However, the marketplace strategy (due to lack of oversight and control) caused the user experience to degrade and for consumer trust to get eroded (to an extent). This is what provided a foothold to the competitors.

Flipkart recognized these mistakes and adopted three-pronged strategy to fix this and move forward:

  1. Getting focus back
  2. Get more out of less
  3. Relentless execution

[1] Getting focus back

First and foremost, Flipkart decided to focus on value-seeking, middle India consumers. As a result of this focus, Flipkart changed its focus on categories, products, etc. Shoumyan pointed out that any strategy has two parts: what you will do and what you will not do. As a consequence of focus on middle India consumers, Flipkart consciously decided to not fight the battle for experience-seeking affluent consumers in Tier 1 cities.

Even more importantly, Flipkart really understood what value meant: it meant more benefits for a given price. And, importantly, it didn’t mean the lowest price and it didn’t mean the biggest discount. As a result, Flipkart explore how to provide more benefits for a given price-point (instead of working towards reducing the price points).

This influenced not only the Flipkart product user experience but also helped identify the right promotional model. It also helped Flipkart prioritize entry into consumer finance and other Fintech products.

As an example, Shoumayn’s talked about “Itne mein itna” campaign during this phase, which emphasized more benefits for a given price point.

[2] Getting more out of less

To get more out of less, Flipkart used a 2x2 matrix with effectiveness and efficiency. This helped Flipkart track the effectiveness and efficiency of marketing spends across social media (engagement rate vs cost of engagement), performance marketing (cost-per install vs average revenue per install over 14 day period), etc.

Flipkart used this matrix to double-down on channels and activities that were yielding results while eliminating those that were not. It helped to figure out which sources maximized effectiveness while increasing efficiency.

It was also used for allocating marketing (Search Engine Marketing) budget across different categories. For each category, SEM spends were plotted against RPC (revenue per click). Flipkart found that for each category, there are specific levels of SEM spends for which RPC gets maximized; beyond this threshold, the marketing spends yield diminishing returns.

Flipkart used this mechanism to optimize its SEM spends. During this intense competition period, Flipkart found that the competitors — despite spending four times more advertising money — had got only 20% additional boost in revenue.

[3] Relentless execution

Core values in the marketing team: Creative excellence, Frugality, and Marketing Innovation.

For brand marketing perspective, creative excellence is critical because if creative is good, it helps the company to achieve its goal by spend less money. If the message is sharp, brand can get the same effect with lower budget. The other way to eliminate misattribution is to pick a unique “device” or theme. For Flipkart, kidadults device helped it to cut through all the clutter.

Frugality has two aspects: how to “get more out of less” and how to reduce ineffective spends. Frugality also meant being data-driven about measuring effectiveness of the marketing activities.

Flipkart really pushed the boundaries in marketing innovation and continuously experimented with new things. Shoumyan pointed out that, at one point, almost half of new innovations done by Google and Facebook were done in collaboration with Flipkart. As an example, Flipkart took personalization to the next level by creating more than 3 million personalized video assets. This helped to improve conversion by 45%!

Phase 3

After establishing and retaining lead over Amazon, it would have been tempting to take the foot off the pedal. But Flipkart didn’t do this. Flipkart, instead, consolidated its lead and ensured that its advantage didn’t slip away. What did Flipkart do to become more efficient and more effective during this period?

Flipkart ensured that the — though the victory was celebrated — it didn’t make Flipkart lose the focus or to let complacency set in.

First, Flipkart worked to strengthen the customer funnel and explored how it can engage users better to increase customer LTV.

This was done after segmenting the users and developing a clear understanding of customer requirements. Flipkart used behavioral customer segmentation process. Shoumyan pointed out that any customer segmentation mechanism (based on demographics, psychographics, etc.) can be used as long as it provides Mutually Exclusive and Collectively Exhaustive (MECE) segments. Another important requirement is that the segments should be targetable and actionable.

Flipkart used the behavioral segmentation to divide consumers into 5 segments:

1. Browsers,

2. Lapsers,

3. Light users,

4. Heavy users, and

5. Super-heavy users.

Behavioral segments were created on the basis of Frequency and AOV (Average Order Value). Shoumyan pointed out that it would be create behavioral segments using contribution margin (in addition to AOV) in order to assess whether the customers were value building or value eroding.

Based on customer segments, Flipkart knew how to engage with the customers and what were the key tasks they wanted to achieve. For example:

  • Browsers and lapsers: acquire more new users or re-acquire old users (and engage them to deliver the initial value)
  • Light users and Heavy users: drive more usage by increasing frequency of purchases
  • Heavy user and Super-heavy users: make them upgrade to higher order products and make them buy across categories

For each of these segments, Flipkart created separate marketing programs and defined the relevant metrics. For the browsers and lapsers, Flipkart marketing team designed acquisition programs and reactivation programs. For the light users and heavy users, Flipkart designed upsell/cross-sell program and created loyalty program. And so on. Overall, the goal was to make it easier for Flipkart to acquire users and then move them faster across the customer journey (from light to heavy to super-heavy users). In parallel, the goals was to make the customer bucket less leaky and to reduce the number of lapsers.

Second, Flipkart looked at vertical businesses to leverage them to complement the horizontal e-commerce platform. As part of this, Flipkart expanded to launch fashion, household goods, small and large appliances, baby, grocery, etc. verticals. The goal was to expand beyond the smartphones and electronics, apparel (esp. sarees), and branded goods (such as shoes) verticals.

Flipkart also forayed into non-ecommerce categories such as travel bookings, phone recharges, Flipkart videos, etc. The goal was to either maximize transactions or to maximize time spent with Flipkart. In other words, either more units sold or more DAU (Daily Active Users). Appliances, baby, grocery, etc. verticals were geared towards more units while videos, recharges, etc. were geared toward more DAUs.

Flipkart’s brand journey provides a good template for digital-first brands to chart out their journey. In addition to sharing the frameworks and principles for building digital-first brands, Shoumyan also answered a number of questions from the community members. We will share a summary of some of those in a separate post. In the mean time, if you have any comments and questions about Flipkart’s brand journey or the journey of any other digital-first brand, please do let us know.

June 27, 2020

How to build Digital-first Brands: The Flipkart Story

Everyone in the startup ecosystem has heard about the main Flipkart story… and it is a fascinating story because it had many interesting...

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Masterclass
Marketing
August 6, 2020
SEO Magic: How Housing grew more than 5x and overtook Magicbricks and 99Acres in 18 months

Ravi Bhushan is the founder of early-stage Ed-tech company, Brightchamps. Before venturing on his own, he was the Chief Product and...

Dr. Ajay Sethi
10
min read
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Ravi Bhushan is the founder of early-stage Ed-tech company, Brightchamps. Before venturing on his own, he was the Chief Product and Technology Officer at the PropTiger Group which runs three real-estate properties: Housing.com, Makaan, and PropTiger. Ravi also held the business responsibility of Makaan.

For the sake of context, Magicbricks and 99Acres, are the incumbents of the Indian real estate business. At the start of 2017, Magicbricks and 99Acres were roughly five to seven times larger than the combined traffic of Housing, Makaan and PropTiger. But, in less than two years, by Sept 2018, the PropTiger Group (for the sake of simplicity, referred to as Housing from now onwards — since Housing is the consumer-facing brand and the largest destination within the PropTiger Group) overtook both Magicbricks and 99Acres and became the largest real-estate destination (in terms of the combined traffic). More importantly, most of this traffic was organic traffic.

How did Housing achieve this? What SEO magic did they perform? Ravi shared his secrets during our conversation.

Ravi’s SEO journey into the SEO world started due to “a traffic accident involving Panda in India”! Housing’s traffic dropped by 70% overnight due to Panda penalty imposed by Google. This put paid to the product and engineering investments that the Housing had made over the previous several quarters. Ravi said, “it is painful to have created a beautiful painting and then realizing that someone has put the painting in the dark room and locked the door”! It is important to not only build good products but also to have a clear-cut strategy in terms of how the traffic, especially organic traffic, will come.

What is the most important SEO activity?

The most important and the most impactful SEO activity is “site structuring”. Site structuring ensures that the website is structured in such a way that Google bot crawling the website is provided with the right syntactic and semantic signals about the importance of different sections, pages and content of the website. Site structuring, therefore, focuses on getting the basics right.

First, company should come up with the right information architecture based on the semantic information that the company wants to communicate. The site should be structured such that not only a human being but Google Bot can also understand the importance of different topics / concepts. Each section (corresponding to a topic / concept) should be provided with the right weightage.

To do this, each section of the site should provide coherent meaning and different sections should not overlap too much with each other. For example, a car website can have hub-pages corresponding to the brands (such as Maruti, Tesla, Mercedes, etc.) and, under those pages, hub-pages for various models of the brand. In addition, car website can have a different set of hub-pages for different categories such as sports cars or electric / diesel cars. It is important to structure these pages carefully (as well as link them to the right product detail pages) so that users as well as Google bit can understand the semantic meaning of different sections.

Second, in order to give the right signals to Google and other search engines, it is important to link the pages in a thoughtful manner. Linking pages indiscriminately and ending up with a spaghetti of interlinked pages (including, for example, linking “Contact us” or “Disclaimer” pages from all pages — a common mistake) confuses Google bot and often leads to pages not getting indexed and ranked properly.

Ravi pointed out that linking two pages is nothing but a vote of trust that one passes from one page to another. It is important to link pages by double-checking them from the business priority point-of-view, keyword point of view (importance of specific topics that the company wants to cater to), and from traffic point of view (what opportunities areas are there and how does the company serve those opportunities).

Ravi pointed out that such site structuring experiments can have short-term negative impact (since Google has to decipher new structure and change the earlier set of indexed pages). For example, Housing experienced 30% drop in traffic in the first month after releasing website with proper site structure. However, the traffic increased by 350% in the second month — so, one can get really good results just by doing site structuring properly.

How can one prioritize across various SEO experiments?

After taking care of hygiene-level activities such as site structuring, it is good to use a prioritization framework that takes ROI into account. One way to compare various experiments is to measure them along four parameters:

  1. [Volume] Volume of opportunity: quantum of traffic available
  2. [Quality] Quality of opportunity: relevance of traffic to the business
  3. [Difficulty] Keyword difficulty: how much is the competition for specific keywords
  4. [Cost] Cost of experiment: effort required to run the experiment

First are foremost, one needs to be aware of the given keyword universe and the size of the opportunity — number of search traffic volume one can go after. Next, one needs to consider users’ intent — which helps one to understand the ability to engage with and convert the visitors into customers. Third, one needs to factor in the competitiveness for specific keywords — what’s the domain authority of the competitors, what keywords have they captured deeply, etc. And finally, the cost of the experiments corresponds to the effort (engineering, content, etc.) required to do the tasks.

Given the above, the experiments can be ranked using the following ROI metric:

(Volume * Quality) / (Difficulty * Cost)

This can be applied to various types of experiments — related to a set of keywords, adding platform features (such as images or videos on product pages; or starting initiative to collect UGC content, etc.), or even re-building the platform from scratch.

Unfortunately, almost 70% of time SEO activities are related to minor tweaks such as title changes, meta-tag changes, writing more content, etc. However, by taking the ROI-based approach, one can avoid dealing only with tactical stuff and start picking up more strategic initiatives. Of course, the ROI metric is just an indicator; the exact decision should be made by considering other qualitative aspects such as the business vertical, type of website (transactional or informational, e.g.), platform capabilities, etc. Together with the ROI metric, one can arrive at the most effective SEO roadmap.

Using the above framework, Housing was able to pick the right experiments that helped them surpass Magicbricks and 99Acres, both of which had much higher domain authority. Instead of going after every keyword related to Indian real estate and competing with the incumbents on the “head” keywords (that generate a lot of traffic; these were “property in Mumbai”, “property in Gurgaon”, etc.), Housing focused on the housing project related keywords. In addition, Housing realized that a lot of traffic (as well as higher intent traffic) was in the long-tail keywords such as “2BHK property in Goregaon”, “3BHK Sohna Road”, etc.

These decisions (to focus on specific set of keywords, e.g.) have an impact on the site structure and the information architecture. They also have implications on what kind of platform one needs to build — for example, how should search and navigation function, how should mapping and other infrastructure work, etc. These need to be factored in while calculating the cost of the experiment.

Ravi emphasized that the same analysis can be done by early-stage startups as well. For this, he gave an example of PDFdoctor.com, a passion project that Ravi started after leaving the PropTiger Group. PDFdoctor provides tools to work with PDF documents (like merge or split PDF documents, etc.). As one can imagine, there is a lot of traffic for online tools related to the PDF; also, this is a super-competitive space with several companies working on building tools (and doing SEO for them) for over a decade. However, by prioritizing various activities using the above-mentioned framework, PDFdoctor identified the best candidates to focus on and, as a result, started ranking amongst the top three results globally within 6–8 weeks! Therefore, by understanding what the competitors were focused on and what they were not, it is possible for any startup to compete globally (across multiple languages and countries) and come out as the winner.

SEO Building Blocks

Most early-stage founders know that SEO is important but a lot of them are not able to leverage it properly. What can startups do to benefit from the SEO magic?

Ravi suggested that startups have to create the right culture and the right infrastructure in the company to ensure that SEO gets the focus it deserves.

Culture requires understanding the importance of SEO and talking about in senior management meetings and in the company townhall discussions. It also requires empowering a leader to focus on SEO growth and creating a dedicated team or squad focused on SEO. The SEO team should have clear targets and goals (along with dashboards that provide measurability) and the team should be rewarded for both mini-successes and for achieving major milestones. A lot of companies miss this and don’t have anyone responsible for day-to-day handling and growth of organic traffic.

Infrastructure corresponds to infrastructure around log management, A/B testing, etc. This is because SEO should be a proactive game; it should not be played in a reactive manner (reacting to competitors or Google penalty).

Ravi pointed out that Housing had created a separate micro-service for SEO to take care of all technical SEO requirements such as title / header / meta handling, page redirection, content interlinking, content spreading, etc. In other words, all major aspects of technical SEO were supported by a micro-service.

The right infrastructure empowered the developers and PMs to do any experiment they wanted to do in isolation, without impacting the main product roadmap. This enabled the team to run their experiments very quickly and, therefore, become more effective.

The SEO team itself can be part of the product, tech or marketing team; the important part is that (a) it should have SEO-focused developers and product managers and (b) the company should work to ensure that the team doesn’t face roadblocks while taking care of the SEO tasks.

Difference between paid marketing and SEO mindset

Paid marketing requires understanding of various platforms (such as Google, Facebook, YouTube, Instagram, Linkedin, etc.) and the related marketing tools as well as planning (for example, budget planning, allocating spends across different platforms, etc.). Also, one can get instant results (within a few minutes or hours) regarding paid marketing experiments. Unlike paid marketing, SEO requires longer time period to show results and, therefore, more patience. More importantly, organic marketing and SEO requires a different mindset. This mindset demands product DNA and product-first thinking.

Product DNA is important because, ultimately, Google rewards those websites that are loved by users. So product efficacy is the core of organic marketing and SEO. In other words, as far as the organic traffic growth is concerned, a good product wins in the long term.

To imbibe product DNA, one needs to adopt user-centric mindset. As a result, it is important for the product managers (those who are responsible for the overall product roadmap) to start thinking about SEO items as well.

Treat Google Bot as a User Persona

To avoid the conflicts and to ensure that the SEO does not get Cinderella treatment within the organization, Ravi said that we must treat Google Bot as a user persona!

Why is this important? Ravi said, “imagine who would be the most frequent visitor of your just-recently-launched site? It is going to be Google Bot!” Therefore, one needs to take care of the Google Bot by presenting it with the right site structure, making the site easy to crawl and index, ensuring fast response time with high download rates, etc. If Google Bot finds the site friendly, it would rank the site higher and, therefore, tell the whole world about it and generate a lot of referral traffic!

At the later stages, when the site starts generating good traffic (direct and otherwise) and the product / platform has evolved, then the Google Bot user persona should be considered along with other user personas. At this stage, high quality product (that serves the end-users’ needs) is the most important. If the product quality is high and the end users like the product, Google Bot persona also ends up “liking” the product — even if you do a few mistakes. In other words, one can naturally start treating Google as a channel at this stage and focus on prioritizing user-specific activities (instead of resorting to SEO hacks to rank higher in Google results).

Treating Google Bot as a user persona can ensure that the team views SEO empathetically and applies user-centric mindset to SEO activities as well. It becomes easier to align different features and to consider SEO activities as part of the product and tech roadmaps. As a result, product/tech roadmap conflicts get sorted out.

This, Ravi hoped, would be one of the key points takes away from the conversation.

August 6, 2020

SEO Magic: How Housing grew more than 5x and overtook Magicbricks and 99Acres in 18 months

Ravi Bhushan is the founder of early-stage Ed-tech company, Brightchamps. Before venturing on his own, he was the Chief Product and...

Read More
Masterclass
Marketing
May 30, 2020
Chargebee: 20x Growth in 2 Years Without a Robust Marketing Plan!

Vikram Bhaskaran is a distinguished marketer. The Sr Director of Marketing at Chargebee, he headed marketing in Freshworks and FusionCharts.

Dr. Ajay Sethi
7
min read
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Mosi-oa-Tunya (“the smoke that thunders”), Zambezi River (link)

Chargebee is one of the largest subscription management platforms in the world, driving revenue operations and billing for SaaS and subscription-based businesses.

At the start of the Covid-19 crisis, Chargebee’s traffic dropped by 14% in two weeks — something that most companies across the world also experienced:

However, Chargebee was able to recover quickly in the next two weeks:

In fact, not only did the traffic recover quickly, Chargebee was able to grow its metrics and, in the subsequent two weeks, ended by 20% higher than the pre-Covid levels:

How did Chargebee do this?

To understand the marketing gears behind their metrics, we spoke with Vikram Bhaskaran, who is the Senior Director of Marketing at Chargebee. He has been with Chargebee since 2018, during which time the company has grown rapidly. Chargebee’s leading marketing metrics in this time have grown a whopping 20X.

Vikram is one of the most experienced SaaS marketers in India, with more than 15 years of SaaS marketing experience. He has handled marketing at some of the most successful SaaS startups in India. He was with Zoho in 2006 (when Zoho was known as AdventNet) and with Freshworks in 2012, when Freshworks — then known as Freshdesk — had just raised its first round of funding. He has been involved in the SaaS space during these formative years and has contributed to the evolution of SaaS marketing in India.


Conventional thinking naturally drives marketing organizations to design progressively more robust strategies as they grow. Vikram adopted a contrarian view and formulated an “Adaptive Marketing Strategy” that is able to evolve rapidly with company needs and market dynamics.

As he describes it, a Robust Strategy is one that is able to sufficiently weather external changes. An Adaptive Strategy, on the other hand, is designed to capitalize on these changes and turn them into opportunities for growth.

Vikram shared that there are 4 elements of the Adaptive Marketing strategy:

  1. Buyer
  2. Channels
  3. Team
  4. Market environment

Any customer acquisition strategy, let alone adaptive marketing strategy, depends on the buyer. Therefore it is critically important to understand buyer’s needs and goals, and map out their aspirations.

Second, channels correspond to distribution. All post-PMF startups must identify at least one channel that can scale. As the channels hit their capacity, marketing teams need to evolve and discover newer channels.

Third, interestingly, adaptive marketing is built to work with the diversity that is inherent in marketing teams. Marketing teams have artists, data analysts, creators, executors, iterators and optimizers. This is a heterogeneous set of people and adaptive marketing leverages this diversity.

Finally, adaptive marketing needs to be fluid (“flow like water”) in order to be able to quickly respond to market dynamic changes — either induced by customers, competitors or external factors.

[1] Buyer: Understanding Needs and Aspirations

The Buyer Persona is the most important aspect of the Adaptive Marketing strategy. Constantly evolving insights about customer requirements enables the companies to react quickly to adjust channels, reorient teams and respond to changing market dynamics.

But how can a company define buyers precisely? In particular, Chargebee has more than 15,000 customers across various industries in 53 countries. These include businesses ranging from early-stage startups just launching their products, all the way to established companies with sophisticated finance and revenue functions.

Within a customer, Chargebee has a multitude of stakeholders: finance teams involved in receivables, reconciliation and reporting; billing teams that need to automate their invoicing operations; product teams that need to drive pricing decisions; sales teams that need to close increasingly sophisticated negotiations; developers that need to implement the platform; Business heads; and CEOs.

With so much diversity across customer segments, roles, and geographies, how does Chargebee define its target customer or the buyer persona?

For Chargebee, this came from deep customer research that involved talking to users and existing customers, studying the first interaction of prospects in sales calls, and analyzing market behavior from search trends. This blog describes Chargebee’s approach to Continuous Customer Development & the steps involved in detail.

Vikram gave a brief overview of how Chargebee is building a brand around “RevOps” (Revenue Operations). The RevOps idea evolved while Chargebee was developing the buyer persona. Chargebee started this exercise by listening to the sales call — especially the first call between the prospects and the sales team. Vikram said that it was insightful to listen to how potential customers described their problems and their pain points. This initial understanding was refined by subsequent 1:1 conversations with customers and prospects to understand what their day looked like and what were their typical workflows. This helped develop a deeper understanding about customers:

  1. JTBD: What were the “jobs” they were employing Chargebee to do? Chargebee found this to be “billing and subscription management”.
  2. Zero Moment of Truth (ZMOT): this refers to the discovery and awareness stage in the buying cycle: what did the customer do just before they became aware of the problem? What was the itch they wanted to scratch? For Chargebee this was an “operational inefficiency” that the customer felt.
  3. Non-functional Why’s: Why did the customer want to do this job? What was their underlying goal? Chargebee found this to be “revenue growth”.
  4. Underlying Aspirations: What were the aspirations of the people involved in this process? Chargebee found this to be the “desire to be seen as a champion; someone who knows how to navigate a rocket towards higher growth”.

The end result was the realization that irrespective of their actual roles, prospects came to Chargebee with the specific pain of solving inefficiencies in their revenue operations just as they saw a hockey-stick growth potential in their horizon.

Defining personas not only helped the marketing team fine-tune positioning and messaging, but also made it possible for the whole organization to build, sell, support and engage the right customers.

But more important — clarity about the target persona also had a wonderful side effect: happiness! The org-wide focus and clarity made it even more satisfying to plan and visualize the value they were adding.

[2] Channels: Defining Scalable Pathways

Three pillars of any marketing team are: research, creation, and distribution. And within this, distribution comes down to the mix of channels, and how well the team is able to leverage them.

Within the first 6 months of deploying the Adaptive Marketing Strategy, Chargebee saw a 3X growth in demand. And, as mentioned earlier, over the next 2 years, these leading metrics have grown over 20X.

Vikram broke Chargebee’s approach to channels into 2 frameworks: Discovering Channel Fitment and Assessing Channel Capacity.

Channel Fitment corresponds to evaluating whether a channel is the right one for acquiring the target personas. Channel Capacity lets the team evaluate the continued potential left to tap within the channel.

Channel fitment

As an essential platform required by every high-growth SaaS business, Chargebee has the advantage of playing in a market with huge inbound demand. As a result, channels like Organic content and Paid search, in addition to inbound category creation through branding and thought leadership have been the most scalable channels.

As the business grew, Chargebee scaled into additional channels to augment specific customer segments (like Account Based Marketing for Enterprises), while beefing up its community focus for early stage businesses with high growth potential.

What is the best way for a startup to explore a new channel? Vikram said that Chargebee uses what he calls the 3S Framework for this: Scout, Scope, and Snipe.

In this Scout Phase, Chargebee runs broad low-investment campaigns to test a new channel. These could be paid ads, content-based activity, etc. The idea is to identify a few successes that can be explored further, without expending significant budgets.

As specific channels & campaigns demonstrate potential, the Scope Phasefocuses on improving the quality of the results. In paid search, for example, this is done by launching specialized campaigns with narrower focus on just keywords or activities yield better results.

Finally, the Snipe Phase serves to turn the activity into a repeatable process. In the context of paid search, this involves setting negatives and having exact matches that can be scaled for predictable results.

These three phases help continuously drive new campaigns & channel possibilities, while still ensuring quality & ROI.

Channel capacity

In the context of Channel Capacity, Vikram referred to the “Law of Shitty Clickthroughs” proposed by Andrew Chen [link], which refers to the diminishing returns provided by a channel as volume scales up.

Even as the quality goes up, how does one make sure that the quality doesn’t get compromised? How can businesses pick channels with sufficient capacity so that they can defer the curse of shitty clickthroughs?

Chargebee uses the following Channel ROI Framework for this purpose:

Clearly, the goal is to find channels that are “Leaders” (i.e. provide high quality leads / customers at low cost) and to stay away from high cost but low quality quadrant. Vikram mentioned that Bleeders (such as low-cost paid ads) are interesting because they can provide volume at low cost. However, marketing teams need to closely monitor their Bleeders, and measure their downstream ROI in dollar terms. Finally, Truffles are likely the largest segment — composed of high quality campaigns that also require more effort and budget. Typically, Truffle campaigns have to constantly out-bid competitors, but they are good indicators of market size and our position in it.

For Chargebee, Account-based Marketing (ABM) has grown from a Bleeder into a Truffle channel, while Community-based initiatives shift between Bleeders and Leaders.

Focus on Channel Fitment and Channel Capacity allows marketing teams to quickly double down or back away from initiatives.

[3] Team: Driving Individual Ownership

In a fast-growth business, the focus of the marketing team continuously shifts from Volume, to Velocity, Predictability, and Variability.

Vikram pointed out the moving targets that a marketing team needs to solve for, as it evolves from “Robust” to “Adaptive”:

  1. Marketing teams need to satisfy demand growth. Marketing teams need to invest in channels to feed the pipeline required to drive sales.
  2. Marketing has to enable sales to consume pipeline effectively — through intelligence, automation, messaging and enablement.
  3. As one of the biggest “expense” centers in a fast-growth organization, marketing teams have the responsibility to provide the organization with predictable and sustainable growth.
  4. Marketing is responsible for increasing the size and quality of the pie — by brand building to drive awareness, market creation to increase the size of pie, and differentiation to craft perceptions.
  5. The marketing team is responsible for driving adoption, engagement and advocacy within existing customers, to create a virtuous cycle of referrals.

Note that the first three stages correspond to linear growth (with respect to the marketing spends). Building brand and developing an emotional connection with customers provides some amount of non-linearity, while investing in customer engagement unlocks network effects. (We have earlier talked about how companies that don’t have inherent “networking” play can also unlock network effects [link].)

Each of these activities, clearly, are quite different from each other. In order to succeed, marketing leaders should structure their teams around specific focus areas, with metrics and inter-locking contracts. Vikram (elsewhere) illustrates this with the following representative example:


Given the evolving or moving target for the overall marketing team and differing success metrics for various marketing sub-teams, what holds the marketing team together? It is the clarity about the buyer persona (what are customers’ needs and goals) and the organization’s needs and goals.

Business needs are often captured by the “north star metric”. For Chargebee, the core indicator of success is “Net Retention Rate”. This goal is shared by the whole company and helps Chargebee track the speed at which the customers are growing. It helps the marketing team to also evaluate the leads, MQLs, SQLs, etc. in a more consistent manner.

In order to be truly Adaptive, marketing teams need to decentralize strategy. Vikram explained how every marketer in Chargebee is empowered to drive strategy with minimal oversight. He explained 2 systems that the team uses to ensure they stay aligned without stepping on each other:

  1. The PEAR Commander’s Intent: Every marketing activity and function has to satisfy Predictability (fitness to purpose), Excellence (exceeding expectations), Accountability (one problem — one owner) & Reporting (operational rigor).
  2. Socratic Method: Individual marketers are empowered to make decisions by documenting answers to 4 questions: What is X worrying about? What would X do? Why is this the right thing to do? How would X want to track progress? X here could be their manager, head, the CEO or customer.

[4] Market environment: Staying tuned to Changes

The final and most critical aspect of the Adaptive Marketing strategy is the ability to not resist the constantly evolving market dynamics but to rather turn it into an advantage.

Vikram builds on Bruce Lee’s quote (Be like water) to describe how an Adaptive Strategy must flow freely without resisting change in order to stay flexible and evolve rapidly.

For this, Chargebee built a system of Indicators, Triggers, and Strategies. Chargebee takes the org-wide look at the Indicators on a weekly basis (while the marketing team tracks the Indicators on a daily basis). As indicators hit different thresholds, they trigger a specific strategy set that the team operates on.

The dashboard with Indicators, Triggers, and Strategies is shown below:

Chargebee has four top-level indicators:

  1. Top-of-the-funnel (ToFu) opportunities: what is happening at the ToFu level? For example, early in the lockdown cycle, Chargebee detected that the interest for webinars and learning courses had gone up. As a result, Chargebee organized almost 10 webinars within the first two weeks of the lockdown.
  2. Demand / search intent: for Chargebee, inbound traffic continues to be an important channel. To track it, Vikram uses the Key-5 Search Index — an index of their top 5 search pillars.
  3. Pregnant python: this indicator helps Chargebee to identify if there are bottlenecks in any part of the pipeline. Chargebee then either works to resolve the bottleneck or, if needed, to rethink strategy.
  4. Leaky bucket: Chargebee uses this as the frth indicator to track customer and revenue churn. Chargebee qualifies the churn as voluntary / involuntary and tracks churn across verticals / industries.

These Indicators have different impact on the company’s north-star metric: NRR (Net Revenue Retention). Aligned with NRR, Chargebee has a number of Triggers. For example: Estimated Bookings, Cost of Estimated Bookings, Estimated Payback Period, etc.

As these triggers flip, Chargebee’s marketing team already has the strategic priorities and initiatives in place to quickly shift directions.

Chargebee’s marketing team further structures all their initiatives into 7 categories ranging from Critical Necessities to Discretionary by just answering 3 questions:

Depending on the Yes or No answer for each question, there are eight different scenarios. Each scenario provides guidance for the strategy:

Along with the operational rigor, Indicators, Triggers, and Strategies provide the mechanism for Chargebee to respond quickly to the evolving market dynamics.

Closing comments

Vikram emphasized that scaling marketing teams should not waste their time building robust marketing strategies. Robust market strategies attempt to withstand external disturbances and resist changes, which makes them quickly lose touch with reality.

First, in a fast-growth business internal teams, processes and priorities change so rapidly that forcing robustness could isolate the marketing function from the rest of the organization.

Second, while the Covid situation is unprecedented, markets and environments change directions even otherwise in unpredictable ways. While robust strategies might provide immediate insulation from these turbulences, the incremental shifts in the environment render the strategy obsolete quickly.

Eventually these deep, well thought strategies end up living only on the paper (or spreadsheets) while the actual marketing plan gets changed ad-hoc. Teams lose sight of the bigger picture, stresses run high, and marketing leaders find themselves struggling to plug the leaks in their strategy.

Instead, Vikram suggested organizations to focus on fluidity instead — by designing their marketing strategies to be Adaptive.

However, an Adaptive approach is as much an organizational mindset as a strategy — in order to be truly adaptive, marketing teams should account for

  • Constant research & understanding of the Buyer Context
  • Continuous discovery & expansion of Channels
  • Flexibility & individual ownership within the Team
  • Systematic tracking & reprioritization based on the Environment.

Based on these, an Adaptive Marketing Strategy provides a natural way to keep the marketing plan flexible to identify new realities and capture opportunities. Adaptive Marketing, therefore, is the ideal way to build the marketing function at every company: from an early-stage startup to growth-stage startup and even large enterprises!

May 30, 2020

Chargebee: 20x Growth in 2 Years Without a Robust Marketing Plan!

Vikram Bhaskaran is a distinguished marketer. The Sr Director of Marketing at Chargebee, he headed marketing in Freshworks and FusionCharts.

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Masterclass
Product Management
October 29, 2020
Driving 100 million users to adopt digital payments

With 22 years experience in building commerce, utility, gaming & financial products for consumers, Deepak has developed analytics & growth

Dr. Ajay Sethi
9
min read
Read More

PayTM: Growth Lessons from Driving 400M Indians to Adopt Digital Payments

Growth is the fuel that energizes startups to defy heavy odds. It sustains and drives startups in their quest to create value and to build successful businesses. But how should one navigate the growth journey? What are the levers that startups can use to drive and sustain both the quantity and the quality of growth? And what are the indicators that are the most useful in this journey?

In the startup world, growth is often discussed in terms of CAC (Customer Acquisition Cost), LTV (Life Time Value of a customer), UE (Unit Economics), and NSM (North Star Metric).

At a high-level, these terms are defined easily:

  • CAC is the cost of acquiring one additional customer, which can be calculated as (Sales Expense + Marketing Expense) / #new-customers.
  • LTV is the total revenue the company can make from an individual customer over the life its association with the company.
  • UE (Unit Economics) helps to calculate the “contribution margin” per customer; it corresponds to how much value each customer (“unit”) creates for the company. UE, therefore, helps to predict company’s revenue and profits. For the sake of simplicity, LTV to CAC ratio (i.e., LTV / CAC) is often used as a rough proxy for UE.
  • NSM (North Star Metric):  the metric that a startup uses to help it focus on growth.

However, the terms get complicated in reality.

For example:

  • CAC: what is the definition of “acquiring one additional customer”? Is it when customers download the app? Or, when they register or signup to start using the service? Or, when they perform the “core action” (which can be doing a transaction; or, it can be consuming the content or sending a message)? Or, when they become repeat / retained customers?
  • UE: should the UE be calculated at the aggregate level for the customers? Or, should UE be calculated at per-transaction level? Or, perhaps, per-product level? For example, if a customer uses company’s products for both payments and for buying gold, should one calculate UE for each transaction? Each product? Or, at the aggregate level for each customer?

In order to discuss how startups could work with these terms, we thought it would be good to discuss how one of the most highly valued startups in India looked at these.

PayTM is one of the most well known startups in India. It has more than 350M - 400M users and more than 50M Daily Active Users (DAUs). [link] In fact, if you exclude Google and Facebook properties, PayTM has the largest reach in India. Amongst Indian startups, PayTM (along with InMobi’s Glance) has the largest DAUs and MAUs [link]:

And to discuss growth at PayTM, there can be no better person that Deepak Abbot, who was the “Head of Growth” during his first stint with PayTM and the “SVP, Products” during the second stint. Recently, Deepak has ventured out to start on his own company.

In all, Deepak spent more than five years with PayTM. First stint was in 2012-14 – when PayTM was getting started as a mobile wallet company (from a web-centric company). It had less than 10M app downloads at that time. And the second stint was starting in 2016 – when PayTM was growing robustly. In 2017, PayTM crossed 100M downloads and grew to more than 400M downloads by 2019. [link]

As indicated above, CAC, LTV, UE and NSM can be calculated with different levels of sophistication; so, it is useful to discuss how an early-stage startup and a late-stage startup should look at them. Since Deepak was directly involved with PayTM’s growth during the early stage (2012 – 14 timeframe) and during the late stage (2016 onwards), he has the first-hand experience to share his experience with handling these growth metrics at different stages.

You can see the entire conversation here.

Or, alternatively, you can look at the following summary that is based on my conversation with Deepak.

CAC

When a company is getting started and the company wants many people to try out their app or website, it is good to keep the definition of CAC very simple. CAC can be

calculated based on app installs or based on unique website visitors.

For example, in the initial days, PayTM used to consider users who downloaded the app and performed some “core actions” as acquired users. In general, even if the users are not transacting but perform some core tasks, it is good to consider them as customers. This is because they generate data trails by using the product, which can help the startup to improve the product. At this stage Cost-per-Install (CPI) can be considered to be the CAC.

As the company grows, the definition of CAC should be gradually refined. CAC definition depends on the core focus of the company at a given time. From improving the product in the beginning, it evolves to improving value delivery; and subsequently to increasing usage and then to increasing revenue. So, CAC definition changes accordingly.

After a few hundred thousand users, PayTM started using “signed up” users for calculating CAC. For this, PayTM required that the users must have completed the onboarding flow by providing their email-id and doing mobile number verification. This helped PayTM to decipher that the users had the intent to use the product and provided PayTM with the ability to reach out to users, when needed. For PayTM, almost 90% people would complete the onboarding flow and register after downloading the app; as a result, CPI and the cost per registered user were not much different. As a result, PayTM moved to cost per registered user quickly. For the initial 18 months or so, PayTM used the cost per registered users for its organic marketing and (paid) digital marketing efforts.

As a payments company, PayTM’s core task was doing a transaction (i.e., making a payment using the PayTM wallet). As a result, PayTM graduated to using transacting users for calculating CAC within 6 – 7 months after launching the product. PayTM realized that if hundred users install the app, (let's say) 70 users typically registered; moreover, (say) 40% users would start transacting within five days (and do their first recharge). This made it possible to calculate the CAC for transacting users (“cost per transaction”; CPT). Based on this, PayTM started optimizing its campaigns to reduce CPT.

PayTM faced another problem in the initial days: most users didn’t want to use their debit cards or netbanking very frequently (for security / safety reasons). To alleviate these concerns, PayTM (and other companies such as Freecharge and Mobikwik) promoted the mobile wallet concept wherein users could load money once (into their wallet) and use it for recharges for their friends or family. Also, in order to increase the repeat usage, PayTM wanted users to load money for 3 – 4 weeks and do multiple recharges. In order to optimize for this, PayTM refined the CAC to correspond to the user who would load money into the wallet (and not the users who had done their first transaction). So, 6 – 7 months after the launch of PayTM wallet, PayTM’s CAC was focused on the “add money to wallet” core action.

After a year or two down the line, once the company had built multiple products and expanded its portfolio, it became possible for PayTM to cross-sell other products/services to users (such as top-ups, etc.). PayTM also added several merchants (such as Uber, Redbus, PVR Cinemas, Inox Cinemas, etc.) after getting the semi-closed loop wallet license (in 2014).

[link, link]

At this stage, PayTM started measuring CAC based on the retained users. For PayTM, this corresponded to users who were doing repeat transactions. This is because the first few transactions were incentivized but, subsequently, users needed to make their own decisions. PayTM was hopeful that users would continue using the product because they had liked the initial product experience. From 2019 onwards, PayTM started using cost per retained user for CAC.

This was also useful because as PayTM scaled, it became difficult to acquire more and more users. Therefore, to continue growing, it became important to retain users and to get them to perform multiple transactions. At this stage, PayTM also started focusing on internal marketing (to their existing user base; via in-app notifications, etc.) in order to not only inform them about various merchants (online and offline) where they could use PayTM as a payment instrument but also to get them to transact more frequently.

LTV

At an early-stage of the startup, LTV is difficult to calculate because startup doesn’t really know the retention duration and how many transactions (repeat usage) would users do during their lifetime with the product. Also, pricing (and take-rate, etc.) is fluid at this stage. Is such a scenario, how should early-stage startups work with LTV?

Deepak pointed out that PayTM didn’t calculate LTV for the first 2 – 3 years. PayTM was clear that it wanted to become a financial powerhouse (and not just a payments provider) in the long term. As a result, besides the initial phone recharge and bill payment services, PayTM had plans to offer other financial services. Therefore, it was clear that even if it takes time, various financial services would not only increase retention but would also result in higher ARPU.

In general, this is a right strategy for any category-creating company; it is better to focus on delivering value and increasing engagement touch-points during the initial phase. Monetization and LTV can be considered when customer clarity increases (with data about retention rates and the average lifetime duration of users) and the markets (around the new category) start taking shape.

PayTM started looking at LTV in 2014 – 2015 when the PayTM started supporting more usecases (and more merchants) as well as when PayTM started making money on payments (via use of mobile wallet across different merchants). By that time, PayTM had identified usage patterns and revenue potential from various types of transactions.

Unit Economics (UE)

At an early stage of a startup, it is clear that UE will be in the negative territory. PayTM initially focused on acquiring lots of transacting users without worrying about making money. This explains why PayTM started with recharges and bill payments, even though these services don’t provide much margin. For these services, PayTM focused on providing good user experience and building user habits so that, over time, PayTM could move towards positive UE territory (by cross selling and upselling various products and services).

For PayTM, UE was negative for 2 – 3 years for almost all the verticals it launched. Even then, it was important to be mindful about CAC (which, as discussed, was the cost to get users to start performing the core action; i.e., to start transacting) and to be aware of the levers available to turn UE positive at scale. As products started to mature – for example, prepaid and postpaid phone recharges, ticketing service, etc., PayTM focused on optimizing for UE.

Deepak suggested that companies should not attach too much importance to UE early in their journey because that can impact growth. However, even when a company goes after growth and defers worrying about UE (by deferring revenues), it is important to use UE-based thinking to maintain balance (and to cap the CAC and other costs related to acquiring and retaining transacting users).

In the initial 5 years (till 2017 or so), PayTM had set targets to ensure UE didn’t go too negative. Also, from 2018 onwards, PayTM started focusing on UE by focusing on optimizing costs. Deepak emphasized that, based on the PayTM experience, UE improvement should not rely too heavily on increasing margins. For example, PayTM’s margins from Telcos were the same when they were doing Rs. 10 crore per month worth of phone recharges as they were they started doing Rs. 5,000 crore per month of phone recharges. In fact, this is true across roughly 70 categories that PayTM has launched products for – for none of these categories, margins improved (because there were other external costs that didn’t go down).

Now, there are different ways to calculate UE. It can be done at the:

  1. Transaction level,
  2. Product level, which spans multiple transactions / interactions, or
  3. User (customer) level, which spans usage across several products.

In the early stage, PayTM used UE at the user (customer) level because payments is a high frequency (high repeat) activity and users are expected to do multiple transactions using the product.

By 2015, when PayTM had multiple products – especially some products in the more mature categories – PayTM started tracking UE at the product level (across several transactions via that product). And, subsequently, PayTM started tracking UE at the transaction level, which is the finest level of granularity. In the beginning, tracking UE at the transaction level is too harsh because the company could be incentivizing (initial) transactions and, if the user continues using the product, the UE would automatically improve.

By gradually looking at UE at finer and finer level of granularity, it becomes possible to track and improve UE at the company level – first in terms of contribution margin and then in terms of EBIDTA.

North Star Metrics (NSM)

Since CAC and LTV and, as a result, UE, are evolving indicators, are there any leading indicators that companies can use to track quantity and quality of growth? Are there some metrics that can be used to give direction to the company and to rally the whole team around the growth imperative? Also, how can the company figure out whether it is moving in the right direction or not?

PayTM has used the same NSM from 2012 to 2020! PayTM has used “number of unique transacting users” as the NSM both at the early stage and at the late stage!

The “unique” aspect of the North Star metric helped PayTM to focus on building a large base of users right from the start.

Also, “transacting users” aspect of the NSM helped PayTM focus on getting more and more users to transact. This automatically made PayTM focus on onboarding (as well as the D1 and D7 retention). It also helped PayTM focus on D30 (and longer-term) retention because it is impossible to signup a large number of new users every month. To do so, PayTM built multiple products and built various engagement hooks to retain users better.

This North Star Metric helped PayTM to grow to millions to transacting users because every product feature, every marketing campaign, every external communication, etc. was focused on driving transacting users. PayTM had different teams with micro-tasks or micro-targets that were aligned to the overall NSM.

In addition to the North Star metric, PayTM would select a different focus area (or “theme”) every year. Some of themes were: bring the CAC down, build a scalable or a secure architecture, increase revenues, etc. So every year PayTM focused on one additional parameter. More recently, PayTM is focused on increasing the revenues and reducing the costs, which would make the balance sheet healthy.

Conclusion

We can see how CAC, LTV, and UE are useful metrics for driving a startup’s growth. However, each of these terms need to be calculated with different levels of sophistication based on the startup’s stage. The terms should correspond to the core focus of the company at a given time. It is counterproductive to use onerous definitions of these terms earlier than necessary.

As the numbers above indicate [Feb 2020 data; link], PayTM was able to become one of the largest payments companies in India and then evolved into a full-fledged financial platform by judiciously refining CAC (Customer Acquisition Cost), LTV (Life Time Value), UE (Unit Economics), and NSM (North Star Metric) across different stages of their journey. Moreover, PayTM usage increased 3.5x during the Covid-19 pandemic despite PayTM discontinuing most of the cashbacks and incentives offered earlier. [link] This augurs well for PayTM’s vision of becoming a financial powerhouse in India.

October 29, 2020

Driving 100 million users to adopt digital payments

With 22 years experience in building commerce, utility, gaming & financial products for consumers, Deepak has developed analytics & growth

Read More
Frameworks
Product Management
May 22, 2020
Covid19 Crisis: Business Strategy Framework (Part 2)

Simple but comprehensive Crisis Response Framework that startups can use to respond to changing user requirements and expectations.

Dr. Ajay Sethi
7
min read
Read More

Business Strategy Framework

A crisis that profoundly impacts the prevalent outlook brings short-term and long-term user behavior changes along with it. We have found two parameters to be useful to understand and classify the behavior changes:

  1. Importance of an activity
  2. Frequency of an activity

Let’s consider the first parameter: importance of an activity. As an example, consider grooming services offered by UC (for example, waxing, threading, pedicure, manicure, spa, etc.). The importance of these activities has gone down after the lockdown not only because of fewer occasions to venture out of one’s house. On the other hand, consider the grocery or medicine delivery services – clearly, the importance of these activities have gone up after the lockdown.

The second parameter, frequency of an activity, refers to higher (or lower) frequency of an activity as a result of physical distancing and preference for lower human touch. For example: most tech companies use variants of daily standups and periodic meetings to ensure that everyone is one the same page and the blocking dependencies can be resolved. During the pandemic (with everyone working remotely), it has become more difficult to have impromptu and in-the-hallway mini discussions. As a result, the frequency of meetings about task progress and status updates has increased. On the other hand, customers are holding back on engaging in activities involved with buying a home or renting a new house because these are discretionary activities and users prefer to have a more stable future outlook before committing to these spends.

Visually, these changes can be represented as a 3x3 matrix shown below:

Impact Matrix

It is worth highlighting that, over time, importance of an activity gets translated into the demand for a solution for the activity. (In reality, frequency of an activity is also positively correlated with the demand; however, for the sake of simplicity, we will ignore this correlation. Of course, it should be taken this into account when you apply it to your own specific set of use-cases.) Therefore, concrete manifestation of increase (or decrease) of importance of an activity can be observed from the increase (or decrease) of online demand (and corresponding search traffic) for solutions corresponding to the activity.

Of course, there are activities that can have both higher demand (importance) and higher frequency. The most obvious example would be the increase in importance and frequency of PPE equipment and related health supply procurement. Likewise, demand (importance) for activities that keep young kids engaged and teach them something useful has skyrocketed in the last few months. Moreover, its usage amongst the people who were already using it has increased as well.

Given the changing personal goals and nature of daily routine of an individual (and companies), there are activities whose importance as well as demand has decreased. For example, group fitness classes (at a fitness club) as well as in-person yoga or fitness classes (at home) have gone down in importance as well as frequency.

Research had shown that it takes a minimum of 21 days for a new habit to take root. Moreover, when we look across a large category of habits, its takes approximately 66 days to build a new habit. When done repeatedly, an activity gets ingrained in the brain (and new neural pathways to get formed) in approximately two months. [link] Covid19, by either confining people to their homes or by severely restricting their movements for an extended period of time will give rise to new behaviors that will change the normal response patterns of the people across the world.

Impact Matrix

Based on these two parameters, a company (or, for multi-product companies, a specific activity/task supported by the company) would encounter one of the seven distinct scenarios shown earlier.

We refer to the above as the “Impact matrix”. Impact matrix highlights that companies that find themselves in different quadrants face different challenges. Based on this, companies (or products / features) can be classified into Green, Yellow, or Red zone, as shown below:

Impact Matrix

The companies in the Green zone are facing an overall positive impact and, therefore, have an opportunity to grow faster in the post-Covid world. The companies in the Red zone are facing an overall negative impact and, therefore, have the challenge to find avenues for continued growth (or, minimally, to avoid contraction). Finally, the companies in the Yellow zone are not heavily impacted by Covid19 crisis. However, even these companies have to constantly track the evolving user needs and goals to ensure that they can continue to serve their customers.

Response Matrix

So, how should companies respond to the changes in Importance and Frequency? Once a company identifies the increase or decrease in Importance and/or Frequency (for each activity and, therefore, for various products/features) for each distinct persona, the responses are fairly intuitive. This is because company’s goal would be to counter the changes in customer’s requirements and expectations. The proposed responses are shown below:

Response Matrix

We had provided seven concrete examples of business strategies in the Part 1 of the article [see here]. Those seven strategies correspond to seven different quadrants of the Response Matrix. Here’s a outline of how each of them corresponds to a thoughtful response to different types of impact of the Covid19 crisis:

  1. Decrease in Importance & Decrease in Frequency: if a company finds itself (or some of its products) in this tough situation, it is important to explore if there are alternative ways by which the company can serve its customers. CultFit’s focus on online video classes is a wonderful example of this strategy.
  2. Decrease in Importance: if an activity’s importance has reduced, it is important for the company to change its products, processes, positioning, etc. to reflect changed customer requirements and expectations. UC’s focus on process and product changes (to reduce human touch and to emphasize safety) is a great example of this strategy.
  3. Decrease in Frequency: introduce new products / features that help to drive engagement with users. Housing’s “Pay Rent” is a good example of this strategy.
  4. Increase in Frequency: if changed circumstances increase the frequency of usage, it is important to modify the product not only to support the higher usage imperative. It is also useful to explore how company can drive repeats further so that the company’s product gets close to the natural frequency of the usage. AgroStar’s focus on community-driven engagement is a beautiful example of this strategy.
  5. Increase in Importance: increase in importance is a great opportunity for a company to acquire new customers. Moreover, higher importance can help to improve the quality of customer acquisition: it should be possible to acquire large and higher LTV customers due to higher demand for company’s products. Blackbuck’s open marketplace experiment is an impressive example of this strategy.
  6. Increase in Importance & Increase in Frequency: when increase in importance (and, therefore, demand) is coupled with increase in frequency, it is important to seize the opportunity! Moglix’s international expansion to serve the needs to UK and European customers (starting with PPE, masks, and other health-related requirements) is a noteworthy example of this strategy.
  7. No changes: even if the company wasn’t positively or negatively impacted by Covid19 crisis, it is important to keep a close watch on user requirements (needs) and expectations (goals) and respond quickly to the evolving needs and goals. Rapid-fire product launches and enhancements done by Swiggy over the last two months are an excellent example of this strategy.

Why does the Response Matrix only include Scale, Habit (which we use loosely to refer to repeat usage as well as continued engagement), and Brand? What about other strategies to respond to the crisis? We have written about this earlier [here] but it is worth reiterating that there are only four mechanisms by which companies can create value. These four mechanisms are:

  • Scale (to refer to both supply-side and demand-side scale),
  • Habit (includes stickiness and retention for categories such as health & finance),
  • Brand (includes intangible assets such as patents, regulatory approvals, etc. — esp. for pharmaceuticals, finance, etc. categories), and
  • Network effects.

We have also discussed how Network effects can be unlocked via direct user involvement: if company can design their product / service such that it gets users directly involved in the Scale, Habit, or Brand related activities, it super-charges these three and provides ongoing compounding benefits. [here]

Given this, all business strategies will eventually boil down to one of these four value creation drivers. (If you have don’t agree with this and have examples that prove otherwise, please let us know in the comments section below!) The Response Matrix covers all the three primary value creation engines and, therefore, provides comprehensive business strategy guidance.

Summary

The Crisis Framework presented here are useful for companies to respond in most appropriate manner to the changes in the market dynamics due to Covid19 crisis. Impact Matrix is a good way to analyze the impact of the crisis is a granular manner. By analyzing the impact of Covid19 on each persona and for each user activity, the Impact Matrix can be used to evaluate and understand the impact in a granular manner. Subsequently, the Response Matrix can be used to respond to the changes in a thoughtful manner.

May 22, 2020

Covid19 Crisis: Business Strategy Framework (Part 2)

Simple but comprehensive Crisis Response Framework that startups can use to respond to changing user requirements and expectations.

Read More
Case Studies
Product Management
May 22, 2020
Covid19 Crisis: Business Strategy Framework (Part 1)

Crisis Response Strategies from leading Indian Startups: AgroStar, Blackbuck, CultFit, Housing/PropTiger, Moglix, Swiggy, and Urban Company.

Dr. Ajay Sethi
10
min read
Read More

Creativity Unleashed

For generations, people across the world will remember the havoc wrecked by the Covid19 crisis. Unlike the financial crisis of 2008 or the Twin Tower terrorist attacks of 2001, this crisis has not spared anyone. The homogeneity and simultaneity of the challenges faced by people due to lockdowns (or “shelter in place”) has created a shared consciousness that will have a profound impact on the humanity with across-the-board revision of outlook towards health and lives, livelihoods, social interactions, office and social spaces, travel, entertainment, spirituality, etc. – in essence, almost all aspects of life that are fundamental to human nature.

Several countries, including India, took proactive steps to contain Coronavirus spread and started imposing lockdowns by late March 2020. In a lot of countries, lockdowns continued for several weeks. (In India, lockdown was enforced from the last week of March till the first week of May 2020 – in other words, for more than 6 weeks.) During this time, people across the world had to radically change their daily routines. Also, even after the lockdown is lifted (or, perhaps, as we enter a “rolling lockdown” period), the practices of physical distancing, wearing masks in public spaces, avoiding enclosed crowded places (such as malls, movie theaters, etc.), etc. are likely to sustain – at least till the war against Covid19 is won.

While the societies reels under the constraints imposed by Covid19, entrepreneurs – who, in “normal” times go through a rollercoaster journey encompassing innumerable highs and lows of emotions – have had to handle a crisis for which they had never bargained for! However, the challenging times often bring out the best in people and we have observed the same with entrepreneurs. Accel India has a portfolio of 150+ companies and we have observed an extraordinary and exemplary spurt in creativity across the portfolio companies.

This creative energy has had an invigorating effect on the Accel India team and we felt that it is important to share these strategies with everyone because they can help startups across the world to gain a new perspective and, therefore, provide directional guidelines to handle the post-lockdown scenarios.

In the first part of the article, we will share seven concrete examples of different strategic responses to the crisis. In the second part of the article, we will provide an outline of a simple Crisis Response framework that can be used by entrepreneurs to pick the most relevant strategy for their companies.

Seven Successful Business Strategies

1. Reimagine the category

Since its launch in Nov 2016, CultFit had grown at a rapid pace to become the largest health and fitness brand in India. In Mar 2020, CultFit had more than 150 centers and  almost 80,000 users were working out in these centers on a daily basis. CultFit had grown 7x in the previous year and was on road to grow 4-5x in 2020 as well.

Covid19-led lockdown, however, completely disrupted the dynamics. Fitness centers had to be shut down resulting in the offline operations coming to a complete standstill. Revenues from the fitness business (which was around $10M per month) went down to zero overnight. Worse, it was not clear when CultFit would be able to go back to “business as usual” – there were concerns that Covid19-related side-effects would last for several quarters even after lockdowns were lifted. With offline operations grinding to a halt, CultFit, however, quickly pivoted to live video classes by adding them to its CultFit app. Earlier, CultFit app had only DIY fitness classes and played a minor supplemental role.

Live video classes that users could join from their homes turned out to be quite popular and attracted a large number of non-members as well. Within six weeks of the lockdown, CultFit live classes were clocking more than 500,000 daily active users and had served more than 5 million customers! In other words, CultFit grew 6x in the first 6 weeks of the lockdown.

Given the success of the video classes, CultFit is now exploring digital-first strategy more broadly and hopes to re-start the revenue engine!


2. Engagement & Retention: Drive Higher Usage and Retain Best Users

Housing (part of the Proptiger group that also manages Makaan.com) is amongst the largest real-estate sites in India. Real-estate industry has had to suffer a double-whammy due to Covid19 crisis: a lot of customers at the “top-of-the-funnel” (that is, those that were in the initial phases of their real-estate buying journey) have deferred their journey; also, customers at the “bottom-of-the-funnel” (that is, those that were close to making the final decision) are unable to visit the shortlisted properties and, as a result, are unable to make the final decision. Even otherwise, the uncertainty created by Covid19 has increased reluctance to make large commitments.

In such a scenario, the best thing a company can do is to keep the customer engaged by providing them with all the relevant information so that the customers can make the right decisions. Housing.com is doing this not only with the help of webinars (who isn’t?) but also by offering video-based consultations. Housing.com is also helping builders and brokers gear up to offer video-based virtual tours to simulate site visit experience (as opposed to static video walk-throughs, slideshows or 3D models of a property – which was done earlier).

In addition, Housing.com worked on adding support to enable tenants to “Pay Rent” via their credit cards. This helps people facing short-term liquidity issues during this Covid-19 pandemic. NoBroker, another real-estate firm, had earlier tied up with HDFC PayZapp to offer similar feature. Pay Rent not only provides additional 30 – 45 days of credit to customers but also reduces the dependence of cash and other physical payment instruments. Moreover, by providing ongoing service to customers, it helps these companies to stay engaged with customers.

3. Strengthen Brand: Alleviate Concerns & Deepen Connect

Urban Company (UC) is a managed marketplace that provides beauty-related and home-related services to customers. Beauty services (such as salon, grooming, spa as well as fitness and yoga) have been the mainstay of the company. Home related services include repairs of appliances as well as cleaning, painting, etc.

UC was handling more than 50,000 service orders per day in Mar 2020. India-wide lockdown brought this down to zero. Even worse, the quick spread and extreme virality of the disease ruptured user confidence in any contact with the outside world – whether it was in the form of newspaper, paper money, parcels, or people.

Recognizing the dramatic change in health and safety outlook, Urban Company launched “Mission Shakti” in mid-April to “protect the health, safety and well-being of its customers, service partners & employees.” UC provided masks, gloves, eye goggles, sanitizers, etc. to all service partners in order to protect themselves. UC also provided health insurance and income protection program to all service partners.

UC recognized that in the new world, users will have a difficult choice between going to a salon for haircut (with potentially large set of unknown people) or calling a “stranger” (service partner) home. The latter option has lesser variables – especially if the UC service can be made as safe as possible. UC has introduced new Standard Operating Procedures (that includes sanitizing tools and using single-use sachets and disposables) as well as providing contactless service in categories such as repairs, cleaning, etc. UC also introduced services such as sanitation and disinfection to cater to customer requirements.

All these safety-related process changes were appreciated by the service partners and were reflected in a dramatic 40% increase in the partner NPS scores since the start of the crisis!


4. Rapid Experiments: Match Evolving User Needs and Goals

Swiggy reacted very quickly during the Covid19 crisis. As the Covid19 concerns were increasing (before the lockdown, that is), Swiggy introduced "zero contact deliveries". Later, to help customers order from safer restaurants, Swiggy added “Best Safety Standards” badge to restaurants that have introduction additional safety measures to minimize the spread of the disease – these include temperature checks, frequent sanitation, self packing mechanisms, etc.

Second, Swiggy had grocery service support on its platform for more than a year. To match increased customer requirements, grocery services were scaled up rapidly after the pandemic spread. It was also expanded to serve more than 125 cities across India.

Third, Another service that existed before the pandemic was Swiggy Go, which provided instant pick-up and drop service to users. It was renamed to Swiggy Genie and expanded across more than 15 cities to enable family members, friends, etc. to send necessary items to each other without having to step out of their homes.

Fourth, Swiggy Stores service was introduced to help users buy groceries and other essential items from the nearby stores. Swiggy also partnered with several FMCG brands and retailers such as Adani Wilmers, Cipla, Dabur, HUL, Godrej, Marico, Nivea, Procter & Gamble (P&G), Vishal Mega Mart, etc. to supply food items and branded essential products direct to consumers.

Finally, Swiggy started to explore meal-kit delivery service. Swiggy introduced DIY (Do It Yourself) meal kits that let users to order ingredients of a specific dish from well-known partner restaurants so that users can cook the meal themselves at home.

Swiggy’s “customer-backwards thinking” has helped it to launch these services in rapid-fire manner. Swiggy uses a number of interesting frameworks (such as “Accepted Customer Beliefs”) to quickly understand evolving customer needs and wants (goals) and an efficient Growth Team Architecture to build and roll out these products rapidly. You can read more about these here.


5. Build Habit: Increase repeat usage

AgroStar is one of the largest agri-tech companies that offers complete range of agri solutions to the farmers. AgroStar is also the largest online farming community (known as “Krishi Charcha”) that provides crop-related and other inputs to farmers. AgroStar provides a combination of agronomy advice coupled with service and agri input products that enable farmers to significantly improve their productivity and income.

The e-commerce aspect of AgroStar had to get suspended in the initial days of the lockdown. In order to continue to deliver value to customers, AgroStar focused on improving community engagement. For this, AgroStar improved both the onboarding and increased customer engagement touch-points. These not only improved customer engagement and retention by 2x (in 6 weeks) but also helped increase number of e-commerce transactions subsequently!

Increasing the number of repeats and engagement helped deliver upfront value to customers, which helped establish trust with the customers. Higher trust, in turn, resulted in more than 2x growth in the transaction funnel. This was all the more impressive because AgroStar had significantly reduced its marketing budget during this period!


6. Scale Faster: Improve Quantity and/or Quality of Acquisition

While the coronavirus pandemic has hit most sectors, logistics is amongst the hardest hit because “essential items” (corresponding to agricultural produce, etc. for example) constitute only 15 – 20% of the whole market. The crisis brought almost all the 4 million trucks and the whole industry, literally and figuratively, to a standstill.

In order to get the industry moving again, Blackbuck launched “Move India” initiative that had two main elements [link]:

  • First, Blackbuck waived off the commission fees in order to enable any manufacturer or trader (in addition to the 30,000 customers that it already works with) to find the trucks that they need. This has become important due to non-availability of trucks – especially because a lot of truck drivers are reluctant to get back on the road (both to avoid contracting the virus and because petrol pumps, eateries (dhabas), etc. are mostly closed).
  • Second, Blackbuck worked to support the supply-side partners (which include includes five lakh fleet owners with ten lakh trucks) to discover demand and procure FASTags (for toll payment), fuel cards, etc. Blackbuck has waived off fee for supply-side players as well. In addition, BlackBuck is offering an added incentive of Rs 2,000 to Rs 3,000 to truckers for every trip they undertake as well as Rs. 50,000 trip insurance that covers hospitalization expenses either due to accident or Covid-related treatment.

Blackbuck, in other words, opened up its marketplace to both demand-side and supply-side participants. The open marketplace helped drive more than 120,000 matches to be made within the first three weeks of its launch and helped get more than 10,000 truckers back on the roads!

7. Unlock new opportunities!

Moglix is a B2B e-commerce marketplace that helps manufacturers and other businesses purchase ongoing supplies related to maintenance, repair, and operations of their factories and workplaces. Moglix is leveraging technology to improve the B2B supply chain.

The Covid19 pandemic has seen Indian textiles industry move quickly move to start producing Protective Personal Equipment (PPE) kits. At the start of the pandemic, India was manufacturing no PPE kits that were suitable for Covid19 and all the needs were being met by imports. In subsequent two months, India had 400 accredited manufacturers who were producing 300,000 kits per day! And the manufacturing capacity was projected to double over the next 6 weeks. [link] Globally, however, there is major shortage of PPEs. After achieving self-reliance by mid-May, Indian apparel exporters were ready to serve the global demand. [link]

Despite the disruptions introduced by the lockdown, Moglix actively worked to ensure that the manufacturers and customers (State governments, hospitals, etc.) were able to discover and transact with each other using Moglix’s online e-commerce platform. Moglix also worked to ensure that the consignments reached the customers.

Given that there is a global demand for PPEs and the realignment of the global supply chain, Moglix launched its operations in UK and European markets. Despite the constraints imposed by the pandemic, Moglix has, in fact, been able to satisfy the needs of several countries as well!

Summary

We have provided seven examples of distinct responses to the Covid19 crisis across a wide spectrum of B2C and B2B companies. There are countless more examples across the world. We will love to hear from you: please let us know other examples of companies that have responded quickly to the Covid19 crisis!

In the Part 2 of the article [see here], we provide the framework that helps to understand the “why” behind these responses and how you can apply them to your startup or company.

May 22, 2020

Covid19 Crisis: Business Strategy Framework (Part 1)

Crisis Response Strategies from leading Indian Startups: AgroStar, Blackbuck, CultFit, Housing/PropTiger, Moglix, Swiggy, and Urban Company.

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Frameworks
Customer Success
February 21, 2020
Spiral Effects or Economic Moats?

Warren Buffet of Berkshire Hathaway has helped popularize the concept of “economic moats” over the last 25 years Morningstar, an investment

Dr. Ajay Sethi
18
min read
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Warren Buffet of Berkshire Hathaway has helped popularize the concept of “economic moats” over the last 25 years. Morningstar, an investment research firm, was amongst the first to formalize and systematically leverage economic moats as an investment strategy (in mid 2000s). Based on their research, Morningstar identified five sources of economic moats (in descending order of their importance):

(1) Intangible assets (patents, brands, etc.),

(2) Sustainable cost advantage,

(3) Switching costs,

(4) Network effects,

(5) Efficient scale.

These economic moats mechanisms have been investigated and studied deeply from capital allocation perspective — esp. for investment into mature and late-stage companies. It has been shown that moats-based capital allocation strategy (esp. when combined for stock valuation) provides higher return on invested capital (RoIC).

Are economic moats (“defensive strategies”) relevant for the digital companies? Or, does the speed of innovation (“offensive strategies”) suffice? Not surprisingly, there are differences of opinions amongst the practitioners. This was brought out sharply by the altercation between Elon Musk and Warren Buffet after Musk referred to economic moats as “lame” and “like nice in a sort of quaint, vestigial way” during a Tesla’s quarterly earnings call [May 2018; link]. Musk asserted that “the pace of innovation” is a stronger predictor of long-term success because “if your only defense against invading armies is a moat, you will not last long”.

Not everyone agrees with Musk’s assertions. For example, analysis done by some Venture Capital firms (VCs) has indicated that digital firms do benefit from the defensibility provided by various mechanisms. Interestingly, it turns out that the digital firms also have the similar four mechanisms for defensibility: network effects, scale, brand, and lock-in; however, the order of importance of these four differs vis-à-vis non-digital firms.

In the first three decades of the Internet era, network effects were found to provide the best defensibility. For example, consider Visa and Mastercard, which provide a payment network for customers, merchants, card “issuers” (credit / debit cards issued to customers) and payment “acquirers” (payment devices provided to merchants to accept card payments). This is a lucrative business: Visa generated $23B in revenue on $8.8T total payment volume (TPV) in 2019 — which is 0.25% of the payment volume. Mastercard is roughly half the size of Visa but with similar service fee structure [link].

There have been several attempts to disrupt this network — for example, AT&T, T-Mobile, and Verizon tried to build a payment network along with the large mobile phone manufacturers such as Samsung, Motorola, HTC, LG, etc. (called Softcard; originally, Isis Mobile Wallet; link) starting 2010 using the NFC technology (along with a secure hardware component). It failed to disrupt Visa and Mastercard networks and was acquired by Google Wallet in 2015. (As an aside: Alipay and WePay have been able to disrupt Visa and Mastercard networks in China (with combined TPV of $36T, which is 2.5x of Visa and Mastercard combined global TPV; link). More impressively, UPI in India has also been able to disrupt these networks — both in terms of number of transactions and TPV — within 3 years of its launch [link].)

This can be seen observed from Google’s ability to defend its search engine dominance despite spirited effort by Microsoft Bing; or, Facebook’s ability to defend and grow its social network despite Google’s numerous efforts to build independent social networks. This can also be seen from the continued relevance of services such as Craigslist and Monster.com despite limited innovation and upgradation in their services.

What about other three defensibility mechanisms? There are several examples for each of these: examples of defensibility provided by scale would be companies such as Expedia and Booking.com; brand examples would be companies such as eBay and Flickr; and lock-in examples would be Sybase (part of SAP) and COBOL (more than 25,000 companies still use COBOL).

Though investors (especially public markets and late-stage investors) might desire defensibility, entrepreneurs crave for growth. This is because growth is the magic potion that energizes startups and drives them to innovate faster during their journey. Therefore, till startups achieve maturity, one can visualize that growth is more important than building moats for defensibility for startups and entrepreneurs.

Given this, the question becomes: what are good drivers for growth? How can startups create value faster? How can entrepreneurs identify the most suitable growth strategies for their startups?

Spiral Effects

Based on our experience with hundreds of category-creating and category-dominating companies across consumer, business, health, finance, etc. categories, we have found that there are four value creation drivers:

  • Scale (to refer to both supply-side and demand-side scale),
  • Habit (includes stickiness and retention for categories such as health & finance),
  • Brand (includes intangible assets such as patents, regulatory approvals, etc. — esp. for pharmaceuticals, finance, etc. categories), and
  • Network effects.

You would notice that these look almost identical to the economic moats! So, are we just playing around with words? No — when we look at these from value creation perspective (instead of defensibility lens), we must look at these from product-first perspective. In other words, we must ask: how can startup build relevant products that directly help drive scale, create habit, build brand, and unlock network effects?

Here’s the key insight: the first three growth drivers can be unlocked by adding products with distinct characteristics to the product mix:

  • Scale growth enablers require products/features that have high frequency of usage and low importance.
  • Habit growth enablers require products/features that have medium to high frequency of usage and low to medium importance of activity.
  • Brand growth enablers require products/features that have high importance and low frequency of usage.

We refer to this product-led growth enablers that help build Scale, Habit and Brand as “Spiral Effects”. Why Spiral Effects? Products with the right characteristics, once built, provide ongoing benefits to the company. Not only the value of these products increases as number of users/customers increase, but the products themselves can be improved as well. The additive nature of these product-led mechanisms is acknowledged by referring to them as Spiral Effects.

Spiral Effects are product-led growth enablers for scale, habit, and brand.

Spiral Effects are highly prevalent in nature, as can be seen below [link]:

The above spirals are known as Hemachandra-Fibonacci Spirals corresponding to eponymous number series. Hemachandra number series is additive as can be see from the first few numbers:

1, 1, 2, 3, 5, 8, 13, 21, 34, and so on.

After the initial two numbers, each number in the series is the sum of the previous two numbers. There are fascinating stories about the origins of these numbers in Ancient India and how Pingala, Varahamihira and Hemachandra — Indian mathematicians — used them to define rules of Sanskrit poetry, music, art, astronomy, etc. (and, thereby, providing mathematical foundations to art; one example of this can be seen from the use of “Golden Ratio” in design and arts even now) [link].

Coming back to Spiral Effects, these product-led engagement boosters are not targeted towards building moats; instead, these product-led boosters help companies to create value and benefit from the ongoing additive nature of the products.

Engagement Graph

How can companies build products that help them unlock Spiral Effects? Is there a framework that can be used by startups to explore and build these value creation engines in a systematic manner?

Towards this, we first define Engagement Graph that depends on two parameters that underlie the value creation drivers. We then use the Engagement Graph to outline how startups can build Spiral Effects in a systematic manner.

All activities done by people — whether in personal or work context — can be viewed from the perspective of the “Frequency of activity” and “Importance of activity”. Let’s start by defining the scale for “frequency of activity”. Tasks that correspond to daily (or a few times a week) use-cases are considered to have “high” frequency of activity; weekly (or a few times a month) use-cases have “medium” frequency of activity; all other use-cases have “low” frequency of activity. The scale for “importance of activity” can be defined likewise. Tasks that have large implication and, therefore, require consultation with other stakeholders (such as family members or corporate committees) can be classified to have “high” importance of activity; tasks that trigger users to diligently evaluate pros/cons amongst alternatives as “medium” importance; utility-like tasks that can be performed without much thought are “low” importance tasks.

Following diagram shows the Engagement Graph with the characteristics of the three Spiral Effects:

The figure above provides an indicator towards how startups can unlock Spiral Effects: products/features are more amenable to scale-based, habit-based, or brand-based defensibility mechanisms based on the characteristics of user activities they cater to. There are different zones corresponding to different activity characteristics; the figure above shows the Scale, Habit, and Brand zones.

Conversely, a company can scale faster, increase stickiness or strengthen brand by consciously building products/features that cater to user activities with the desired frequency and importance of activity characteristics. In other words, by adding products/features with the relevant characteristics to their product mix, companies can strengthen and sustain their growth using scale, habit, and brand value drivers.

Let’s consider a few examples to make this more concrete.

Let’s look at Amazon to look at the first two Spiral Effects. Amazon’s primary focus area (i.e., commerce) has a weekly or bi-weekly usage frequency. Typical purchases range from low to medium importance — from buying household goods to purchase of fashion (say, apparel) products. In other words, Amazon’s e-commerce product falls under the “Habit Zone”.

In strengthen its position in the Habit Zone, Amazon launched Amazon Prime in 2005 to address two primary concerns: delivery charges and the speed of delivery. Amazon Prime removed minimum basket size requirement and promised “two day shipping” — that is, any product order that is covered under Prime gets delivered within two business days. (Amazon Prime removed 2-day shipping promise in 2015 — approximately, 10 years after launching the program. However, most people still believe that Amazon Prime guarantees free 2-day shipping!) By reducing both mental and emotional effort associated with ordering online, Amazon was able to increase frequency of usage. The graph below highlights not only the growth of Amazon Prime but, more importantly, the fact that Amazon Prime customers spend more than twice as much as the non-Prime customers ($1400 versus $600 per year). Amazon Prime, therefore, is a great example of Habit Spiral Effects.

Amazon Prime Videos is a wonderful example of Scale Spiral Effects. Amazon Prime Video addresses a user activity that has daily usage frequency and has low importance. By making thousands of Prime Video available to users at zero cost, Prime Videos is able to acquire customers. Subsequently, customers can not only consume free videos but also see TV episodes and move on a pay-per-use basis. Moreover, customers can subscribe to more than 100 premium channels with Prime Video Channels.

Prime Video increases customer’s engagement with Amazon platform, which, inevitably, results in higher frequency of e-commerce purchases from Amazon stores. This is highlighted in the graph below: 6% of Prime customers make daily purchases; 18% of Prime customers purchase 2+ times per week; another 22% purchase once a week. In other words, 46% of Prime customers (as opposed to 13% of non-Prime customers) purchase at least once a week!

Another beautiful example of Habit Spiral Effects is the Zestimate tool/product launched by Zillow in 2006. Zestimate provides an estimate of the value of every house that is listed on the Zillow website. The goal is to enable users to assess not only how their own home is trending but also to provide voyeuristic pleasure of assessing the wealth of their friends and colleagues (based on the value of their properties).

Zestimate attracted more than 1 million visitors with the first three days of its launch. Subsequently, Zestimate helped Zillow grow its traffic to more than 200 million visitors per month. More than 80% of US houses have been viewed on Zestimate; in order words, Zestimate has helped Zillow attract users even if they are not actively looking to buy or sell a real-estate property. If we assume Zillow’s target audience to be around 200 million (out of the total population of approximately 350 million in USA), we can see that (on average) every person amongst the audience visits Zillow once a month.

It is interesting to note that Zestimate unlocks Brand Spiral Effects from homeowner’s perspective,. This is because home ownership is (clearly) a high importance activity from customer’s perspective. Towards this, Zestimate encourages homeowners to provide data about major upgrades and repairs to their homes so that Zestimate algorithm can compute the price more accurately. Homeowners have provided details about prices and upgrades for more than 80 million homes. Zestimate, therefore, has contributed in a significant way to build Zillow into the largest and the most well known real estate brand in the USA

As another example of Brand Spiral Effects, let’s look at AirBnB. AirBnB (short-from of AirBed & Breakfast) started off as an organized and better version of the traditional Bed & Breakfast lodging entities. The AirBnB brand took shape when the team productized the tourist’s desire to “travel like a human”. Towards this, they not only facilitated emotional connect between the hosts and guests (via rich host & guest profiles) but also worked to get hosts involved to provide personalized local experience to the guests. By enabling guests to get authentic local experience (instead of shallower and commercialized touristy experience), AirBnB leveraged the product itself to amplify the emotional connect between guests and the hosts. This was captured brilliantly in the company’s “Belong Anywhere” brand marketing campaign.

In this context, emotional design or emotion-aware design can be treated as an element of Brand Spiral Effects — products that reflect / complement customers’ emotional and non-functional needs are able to connect better with the customers. AirBnB has been a leading proponent of emotional design and supported “Wish List” feature to capture the aspirational aspect of the customers’ wants. Within four months of its launch, AirBnB reported that 45% of AirBnB users were engaging with it! A small A/B testing experiment reiterated and emphasized the importance of emotional design: changing Wish List icon from “star” to “heart” resulted in 30% increase in engagement! [link] Therefore, it is important to leverage products to establish emotional connect with customers (instead of limiting products only to functional aspects by focusing on features and tasks).

At this point (and using AirBnB’s successful “Belong Anywhere” campaign as an example), it is important to emphasize that efficient and sustainable brand marketing campaigns are often based on the Brand Spiral Effects. In other words, before running brand campaigns, it is important to ensure that the product (or product mix) supports high importance activity. Since brand marketing is a “linear” activity (reach / awareness increases in direct proportion to the spends), it is important to build non-linear boosters within the product so that the company can maximize the impact of the brand marketing spends. In the absence of Brand Spiral Effects, companies need to sustain brand-marketing campaigns to ensure that the brand recall doesn’t atrophy quickly. For example, Nike spent approximately $3.7 billion on advertising and promotion costs in 2019 while Coca-Cola spent $5.8 billion on global advertising and marketing in 2018! (How can Nike and Coco Cola build Brand Spiral Effects? This is an interesting question — but, for the sake of brevity, we defer exploring this right now.)

To summarize, companies can build different types of Spiral Effects in a systematic, efficient and sustainable way by building products/features with relevant frequency and importance characteristics. Spiral Effects are self-sustaining: once products with the right frequency and importance characteristics are built, they yield results whenever they are used. In other words, product-led approach helps to continuously support and grow the Spiral Effects.

Network Effects

We have talked about Scale, Habit, and Brand so far. What about Network Effects, which have helped create the most value in the Internet era?

Here’s an interesting insight we have uncovered: Network Effects are Spiral Effects with one important addition — direct user involvement. If product can get users directly involved in the Scale, Habit, or Brand Spiral Effects (i.e., in the products/features that correspond to these aspects of the product), it super-charges the three Spiral Effects! This provides the compounding benefit: not only the scale/habit/brand improve due to the use of Spiral Effects but the Spiral Effects themselves improve due to direct user involvement. As a result, Network Effects become stronger as a result of user growth. This is the lure and the strength of the network effects: they promise ever-improving product and customer experience!

Network Effect helps to increase the importance of frequent activities and/or helps to increase the frequency of medium importance activities. Figure above shows the “Network Effect Zone” in the Engagement Graph.

Direct users involvement in the three Spiral Effects results in three different kinds of network effects:

  • Direct user involvement in Scale Spiral Effects gives rise to “Viral Networks”
  • Direct user involvement in Habit Spiral Effects gives rise to “Exchange Networks”
  • Direct user involvement in Brand Spiral Effects gives rise to “Connected Networks”

We will look at them each of these in more depth subsequently; for the time being, we outline their main characteristics:

Viral Networks are built when current users invite new users to join the network. There are two types of viral networks: (1) acquisition-based viral loops and (2) engagement-based Viral Networks.

Exchange Networks are built when current users engage with each other to improve the experience for everyone. There are three types of Exchange Networks: (1) marketplaces & market networks, (2) platform-based networks (including metadata networks and SaaS-enable Marketplaces — SeMs), and (3) platforms with n-sided network effects (including content & data networks).

Connected networks are built when current users help to build and deepen emotional connect for everyone. There are three types of Connected Networks: (1) social & collaboration networks, (2) community-based networks, and (3) marketplaces with collaboration & same-side network effects.

Even in the absence of direct user involvement, weaker forms of Network Effects are possible. For example, users generate valuable metadata during the course of their engagement with products. This metadata (aggregated over current and past users) can be used to provide better experience to new users. For example, based on past buyer journeys, companies can improve their ability to attract customers, manage leads more efficiently, and to onboard them more effectively.

Network Effects that arise due to indirect involvement of users correspond to the weakest form of network effects and can be referred to as “Indirect Networks”.

Viral Networks, Exchange Networks, and Connected Networks are progressively stronger forms of Network Effects. This is because these Network Effects correspond to three levels of users’ direct involvement in the product. Across these three kinds of Network Effects, user involvement progressively becomes deeper — resulting in increasingly strong Network Effects.

Viral Network Effects

Dropbox grew rapidly due to Viral Network Effect that was based on getting current users involved to invite new users. Dropbox had a very effective two-sided referral program that augmented the inherent virality with additional referral incentives. If a user got a new user to signup, both benefited by getting additional free storage (25MB). In any case, all non-users received a URL that pointed to the files uploaded into Dropbox by the sender. Also, as additional users signed up to use Dropbox, the frequency of engagement increased — users were accessing Dropbox more often (and, therefore, had some elements of Exchange Network Effects).

Viral Network Effects helped Dropbox to quickly acquire more than 500 million users after launching the initial version of the product in 2008. This enabled Dropbox to generate $1B ARR within 8 years of its launch — at that time, it was the fastest SaaS company to hit $1B ARR. [link]

Exchange Network Effects

Google Waze, a navigation app, provides real-time traffic updates and directions to users (travelers) based on inputs provided by fellow travelers. Google Waze was visualized as an Exchange Network right from the inception.

Is it possible to add Exchange Networks on top of existing products? The answer is yes: by adding Habit Spiral Effects and getting users involved in them. For example, Intuit’s TurboTax consciously added community support to make it more interactive. Scott Cook, chairman and cofounder of Intuit, mentions: “With TurboTax, we’re getting customers to answer people’s tax questions. We’ve created the largest and best source of answers on taxes — if you go to Google and put in a tax question, the link at the top will often be our answer. This is tapping a newer habit from the digital age: participating in online communities.” [link]

Brand Network Effects

PinDuoDuo stands for “shop more together” [link, link, link]. PinDuoDuo launched e-commerce service in September 2015 in the competitive Chinese market to compete with market leaders such as Taobao (Alibaba’s China-focused e-commerce platform), JD.com (part of the Tencent group), Vip.com, etc. The markets had grown rapidly over the last 7+ years to become the largest in the world (at $600B GMV). As a result of hyper-growth over the last several years, the growth-rate of e-commerce was tampering down a bit (though still growing at 30–40% year-on-year rate).

Despite entering a competitive market, PinDuoDuo was able to become the 2nd largest e-commerce player in China within 3 years. It IPOed in Jul 2018 in the US markets with almost $24 billion valuation.

How was PinDuoDuo able to crack open the Chinese e-commerce market? How was it able to compete with Taobao and JD.com? This is because it layered in Viral Network Effects and Brand Network Effects natively in the user experience.

PinDuoDuo focused on making it easier for users to create a new group for purchasing specific items. PinDuoDuo encourages users to form “shopping teams” (a new group) by prepaying for the selected items; after this, they can send a link to invite their friends and family members and encourage them to participate in order to buy the products at a group-purchase price, which is much lower than the normal price. To enrich the shared shopping experience, PinDuoDuo’s product has added many elements of gamification to commerce; for example, users can play games with friends and family to win a shopping coupon. Users are also provided with discounts if they pay for a friend.

This mechanism also allowed PinDuoDuo to unlock powerful Viral Network Effects. PinDuoDuo incentivized social sharing via WeChat, which resulted in rapid adoption and wide reach of the platform. This is the reason why PinDuoDuo’s CAC is $2 (vis-à-vis $18 for Vip.com, $39 for JD.com, and $41 for Taobao). In fact, PinDuoDuo’s CAC has reduced from $5 to $3 to $2 as they grew from 110M active buyers to 240M to 340M [link].

Note that, unlike Groupon, PinDuoDuo prefers teams of people known to one other (instead of teaming up with strangers just to get discount prices). Since “tech-savvy” early-adopters were able to onboard their friends and family members (often in Tier 3 or lower cities), PinDuoDuo succeeded in onboarding a large number of users in Tier 3 and lower cities. Almost 57% of PinDuoDuo users are from Tier 3 or lower cities (compared to 44% for Taobao and 53% for JD.com).

PinDuoDuo also worked to add Habit Spiral Effects: there are limited-time offers and lucky draws that seek to get users to visit the app every day. Users can spin a wheel in order to win shopping coupons. Users are provided with cash rewards for checking-in daily.

Amazon has built strong Exchange Network Effects with the help of the user reviews and ratings platform. PinDuoDuo took the network effects to a different level by unlocking Viral and Brand network effects. Together, these mechanisms helped PinDuoDuo to unlock the elusive network effects in the e-commerce experience.

It is important to emphasize that, so far, it has been assumed that Network Effects are dependent on product’s category in the sense that a company can leverage Network Effects if and only if the category intrinsically is dependent on marketplace dynamics, community-based interactions, etc. By identifying different types of Network Effects and their dependence on different types of direct user involvement, we have explained how companies — even those that don’t have natural marketplace mechanics or social networking dynamics — can thoughtfully and systematically craft various types of Network Effects into their products/services. In other words, any company can overlay Network Effects (over their core products) to amplify value creation and to strengthen defensibility.

Summary

We have observed that the three Spiral Effects (Scale, Habit, and Brand) have distinct product characteristics and, therefore, can be unlocked by adding relevant features / products to the product mix. Spiral Effects not only enable startups to create more value in a sustainable way but also help to strengthen their defensibility.

We have shown that product-based Spiral Effects can be built by identifying relevant category-specific tasks (undertaken by target personas) with specific frequency and importance of engagement characteristics: high frequency and low importance tasks for the Scale Spiral Effects, medium frequency and medium importance tasks for the Habit Spiral Effects, and high importance and low/medium frequency tasks for the Brand Spiral Effects.

In addition, we have highlighted that Network Effects are variants of Spiral Effects that can be unlocked via direct users involvement. Product-led user involvement in the three Spiral Effects gives rise to three different types of explicit network effects. Direct user involvement helps convert Spiral Effects into Network Effects, wherein the product itself continually becomes more energized and stronger with growing number of users.

Entrepreneurs can use the framework to build the Spiral Effects and Network Effects in a structured way and, thereby, create more value and build sustainable competitive advantage in a systematic, efficient, and sustainable manner.

February 21, 2020

Spiral Effects or Economic Moats?

Warren Buffet of Berkshire Hathaway has helped popularize the concept of “economic moats” over the last 25 years Morningstar, an investment

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Frameworks
Design
February 18, 2020
Creative Innovation & Disruptive Innovation

Over the last 25 years (since the start of the Internet era, i.e.), entrepreneurs have created more value by innovating and building new...

Dr. Ajay Sethi
18
min read
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Introduction

Over the last 25 years (since the start of the Internet era, i.e.), entrepreneurs have created more value by innovating and building new solutions than by disrupting existing products/services. In this regard, Clayton Christensen’s “Disruptive Innovation” theory (introduced in 1995 — just before the start of the Internet era, coincidentally) needs a close examination and evaluated whether it is relevant for digital-first startups and companies.

This is how Christensen, Raynor, and McDonald explain their theory of Disruptive Innovation [link]:

“Disruption” describes a process whereby a smaller company with fewer resources is able to successfully challenge established incumbent businesses. Specifically, as incumbents focus on improving their products and services for their most demanding (and usually most profitable) customers, they exceed the needs of some segments and ignore the needs of others. Entrants that prove disruptive begin by successfully targeting those overlooked segments, gaining a foothold by delivering more-suitable functionality — frequently at a lower price. Incumbents, chasing higher profitability in more-demanding segments, tend not to respond vigorously. Entrants then move upmarket, delivering the performance that incumbents’ mainstream customers require, while preserving the advantages that drove their early success. When mainstream customers start adopting the entrants’ offerings in volume, disruption has occurred.

Christensen’s theory was highly influential because it provided a systematic way for startups to create value and for the incumbents to innovate in order to avoid getting disrupted. The theory also helped popularize concepts such as JTBD (Jobs To Be Done; JTBD helps to understand the needs of the overlooked segments), MVP (Minimum Viable Product; the light-weight product that has more-suitable functionality), etc.

Disruption-from-below was a necessity in the era that required high amount of upfront investment costs in order to build products and then required some more capital to build distribution networks. The examples used by Christensen reveal the scenarios that fitted this paradigm: for example, how mainframe computers were disrupted by mini-computers and subsequently by personal computers; how disk drive industry innovated and evolved; or, how the mini steel mills disrupted the larger steel mills; or, how Intel worked to avoid getting disrupted by developing lower-power Celeron chips; etc. [link]

However, in the Internet-first era, these are no longer valid concerns. Increasingly, the infrastructure needed to build the products is available with usage-based payment model that enables startups to avoid the upfront capital expenditure required earlier. With the availability of all layers of the infrastructure (from storage to compute; from database to middle-tier; from backend tools or frontend tools; myriad of online distribution channels; etc.), the costs of building and launching a product has drastically come down. Moreover, building offline distribution networks can be deferred (or avoided completely) due to the availability of plethora of online distribution channels.

For example, Google, famously, started with less than $1M investment from four angel investors ($200k by David Cheriton, $100k Andy Bechtolsheim, $250k by Ram Shriram, and $250k by Jeff Bezos). Facebook started with $500k initial investment from Peter Thiel. Both Google and Facebook, incidentally, started before the cloud infrastructure revolution had started.

Evolving customer requirements and expectations, shifting competitive patterns in an industry, technological breakthroughs, etc. help trigger innovations, often creating value by uncovering new opportunities. In this context, disruption-from-below is just a piece of the overall innovation and value creation jigsaw. Given this, there is a need for a new and comprehensive theory of innovation that provides a framework to the startups and companies looking to create value in the Internet era.

By analyzing innovation since the start of the Internet era, we propose a new theory that not only subsumes the scenario covered by Christensen, et al but also two new mechanisms that have driven much of the innovation in the Internet era.

Engagement Graph

All the activities done by people — whether in personal or work context — can be viewed from the perspective of the “Frequency of activity” and “Importance of activity”. Let’s start by defining the scale for “frequency of activity”. Tasks that correspond to daily (or a few times a week) use-cases are considered to have “high” frequency of activity; weekly (or a few times a month) use-cases have “medium” frequency of activity; all other use-cases have “low” frequency of activity. The scale for “importance of activity” can be defined likewise. Tasks that have large implication and, therefore, require consultation with other stakeholders (such as family members or corporate committees) can be classified to have “high” importance of activity; tasks that trigger users to diligently evaluate pros/cons amongst alternatives as “medium” importance; utility-like tasks that can be performed without much thought are “low” importance tasks.

Engagement Graph below shows various personal activities. First-mile and last-mile commute, food ordering, cab service are amongst the most frequent activities (with more than once-a-day frequency). E-commerce has approximately once-a-week frequency and lower importance. Education-related activities also have once-a-week frequency but higher importance while activities such as personal finances (including investments and lending), travel for leisure, real-estate transactions, and healthcare-related activities have much lower frequency but high importance.

Value Creation: Three Strategies

Let’s consider that an entrepreneur wants to build a new product in the Education category (or, the “EdTech” sector, as is popularly known today). Before getting started, one has to look at the incumbents in the market and their products. To make this concrete, let’s consider the largest EdTech company (as of Feb 2020) in India: Byju’s. Byju’s has raised $1.2B at $8B valuation. The valuation is based on more than $200M revenue that the company generated last year and, more impressively, the 3x revenue growth during last year. Byju’s has 40M registered users and approximately 3M paying customers. Last but not the least, it has reported 85% annual renewal rate.

So, how can a startup compete with this behemoth?

It can do so by identifying the target personas being addressed by Byju’s and by understanding the “frequency of activity” and “importance of activity” for each of these personas. Based on this analysis, the startup will be able to explore three options:

For the sake of illustration, these three different personas might benefit from services mentioned below:

And, indeed, there are companies that are building solutions along these three dimensions in the Indian market. These companies have demonstrated significant growth over the last few years and, therefore, shown that it is possible to create value even while competing with a highly-funded and fast-growing incumbent.

Innovation & Disruption

Based on this, we can divide the Engagement Graph into different zones, which we refer to as Innovation Zone 1 (Frequency-led Innovation), Innovation Zone 2 (Importance-led Innovation), and Disruption Zone.

Companies in Innovation Zone 1 create value by providing an easier and more convenient solution to a more frequent activity.

Companies in Innovation Zone 2 create value by providing a better solution to a problem and, thereby, increase the reliability and trust in the offerings. Better quality of product/service provided by these companies helps to match customer expectation in terms of functional needs as well as non-functional goals.

Companies in the Disruption Zone create value by providing a not-as-good solution to a problem at a lower price. These companies don’t offer cheaper or inferior products — they, instead, build products that offer higher “value for money” to the target personas.

Let’s look at each of these three Zones in more details.

Innovation Zone 1: Frequency-led Innovation

Companies that fall in Innovation Zone 1 have, inevitably, chosen a more frequent problem to solve. In order to do so, it was important for these companies to understand which needs of the target personas were not being met efficiently and effectively by current solutions. Innovation is centered on building a product that caters to the under-served frequent activities.

A more frequent problem demands a simpler solution — a lower-effort product that users can start using quickly and derive value almost instantaneously. It is often the case that these solutions are made possible by the increasing availability of new technologies at affordable costs.

Consider the example showcased above. In the urban mobility space, we can see that a lot of innovation has happened over the last two decades. Zipcar (following Mobility Cooperative’s footsteps in Europe) was amongst the first set of companies that attempted to tackle car sharing opportunity in the USA market. [link] Zipcar’s typical usecase was once- or twice-a-week car rental (where users paid hourly usage fee along with a membership fee). By offering a lower effort solution, Zipcar created a new market and, eventually, started disrupting the car rental companies (it was acquired by Avis for approximately $500 million in 2012).

Zipcar, however, was not suitable for frequent, short-distance commute in and around the central business district areas in the larger cities in the USA. This need was served by taxicabs that operated with permits that were artificially restricted (to limit the supply and to ensure that the prices remained high). Uber and Lyft tackled this problem by building a service that made it easier to book and get cabs (via an easy-to-use app that provided cabs within 5 minutes; moreover, users could track the assigned cab from their office instead of standing on the road side). Uber & Lyft assiduously worked to signup drivers and increase the cab supply in the beginning — which not only helped to reduce the waiting time but also helped to reduce the cab fares. By offering a lower effort solution, Uber and Lyft created new markets across the world and, eventually, started disrupting Zipcar and the car rental companies. Together, Uber and Lyft created more than $90 billion, based on IPOs market caps.

Typically, users take a few cab rides in a week. However, there is an even more frequent problem in urban mobility and this is related to the first-mile and last-mile commutes. Uber & Lyft are not suitable for this because of the five minutes wait time (and, to some extent, the price points of these services). Bird and Lime are tackling these even more frequent problems via dock-less bikes that can be picked up and dropped off at any location. Users typically take such rides a couple of times every day. As we can see, these companies innovated within the urban mobility space by tackling a more frequent problem. We have noticed that more frequent products inevitably disrupt less frequent products in the same category. In this regard, Uber’s investment in Lime and Ola’s investment in Vogo makes eminent sense.

Tackling a more frequent problem allows companies to even overcome strong network effects established by incumbents. For example, WhatsApp started its operations in Feb 2009 and raised $250,000 seed round of funding in October 2009 (and released WhatsApp 2.0 to iPhone App Store in Aug 2009). For the sake of reference, during the same time (i.e., from February 2009 to November 2009), Facebook grew from 175 million active users to 300 million active users (and to 350 million active users by end of 2009) [link].

Unlike Facebook, WhatsApp focused on a more frequent problem: short messages amongst a network of closely connected people. This resulted in WhatsApp being used more frequently than any other social networking or social communications app. As a result, WhatsApp is used more frequently than Facebook — almost 60% of WhatsApp users use the product more than once every day (as compared to 50% of Facebook users)!

By focusing on a more frequent problem, WhatsApp was able to beat Facebook at its own game: building a stronger social network while competing with a behemoth with incredibly strong network effects! This also helped WhatsApp to grow faster than every social networking and messaging apps (such as Facebook, Gmail, Twitter, and Skype) [link]:

Innovation Zone 2: Importance-led Innovation

Companies that fall in Innovation Zone 2 come up with a better solution to a problem. They compete with and beat the incumbents by offering superior end-to-end user experience that satisfies either the functional needs or non-functional goals of the under-served customer personas (or both). In order to do so, it is important for the companies to understand what is important to the target personas.

There are two possibilities: (1) the current products don’t fully or satisfactorily cater to the functional needs of the customers or (2) the current products don’t fully or satisfactorily cater to the non-functional goals of the customers. A startup can innovate by improving the products along either (or both) of these dimensions. In the first case, startups build products that offer better quality of service to customers; in the second case, startups build products that establish better emotional connect with customers.

An example of the first case is Urban Company — a company that provides consistent home and beauty services via managed marketplace in India and globally. Urban Company not only short-lists partners to work with very selectively but also trains them extensively to ensure that they are able to delivery better quality of service. In addition, Urban Company is constantly innovating to identify newer ways to measure the quality of service in order to ensure consistent quality of service. This innovation has helped Urban Company to create a new category and, in the process, disrupt the earlier market leader — JustDial — that provided a marketplace to match customers with relevant service providers. JustDial, itself, had earlier disrupted the “yellow-page” companies by enriching the listings with customer ratings and reviews as well as by verifying the service providers.

AirBnB is a great example of the second case: establishing better emotional connects with customers (in addition to building a better functional product). Initially, AirBnB (short-from of AirBed & Breakfast) started as an organized and better version of the traditional Bed & Breakfast lodging units. After the initial validation (partially by timing their launch to coincide with high demand periods and Craigslist growth hacks), AirBnB found it difficult to drive growth. AirBnB fixed it by focusing on activities that helped generate higher trust: better quality and consistent pictures of the properties (and paying for the professional photographers to achieve this) and by emphasizing the need for detailed host and guest profiles (which help to build trust amongst hosts and guests).

The next phase of evolution (leading to the “Belong Anywhere” brand campaign) happened when AirBnB focused on helping tourists “travel like a human” and to allow hosts to connect with the guest while providing personalized local experience to them. By enabling guests to get authentic local experience (instead of shallower and commercialized touristy experience), AirBnB increased the quality of solution offered to the customers. More importantly, AirBnB catered to the non-functional and emotional needs of the guests and the hosts to connect with each other as humans and to learn about different cultures and races. This was captured brilliantly in the company’s “Belong Anywhere” brand marketing campaign.

Both AirBnB and Urban Company, therefore, disrupted the incumbents by making the services more consistent, reliable and trustworthy as well as by enriching the experience provided to the customers. As we can see, these companies innovated within the hospitality and home services space by tackling a more important problem and by building better products.

February 18, 2020

Creative Innovation & Disruptive Innovation

Over the last 25 years (since the start of the Internet era, i.e.), entrepreneurs have created more value by innovating and building new...

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Masterclass
Product Management
April 11, 2020
Swiggy’s Growth Formula that Drove 30x in 3 years!

Anuj Rathi (VP for Product, Revenue and Growth) is a senior product and growth leader at Swiggy. Anuj joined Swiggy in Aug 2016 when...

Dr. Ajay Sethi
20
min read
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Anuj Rathi (VP for Product, Revenue and Growth) is a senior product and growth leader at Swiggy. Anuj joined Swiggy in Aug 2016 when Swiggy is handling approximately 1.5 million deliveries per month. In Oct 2019, Swiggy announced that they were handling 1.5 million deliveries per day. In other words, Anuj has been part of the journey when Swiggy achieved 30x growth in 3 years!

Swiggy has been able to achieve this rapid growth not only with the help of exceptional talent (across engineering, product, marketing, operations, etc. teams) but, more importantly, with the help of a culture that is strongly anchored on “customer-backward thinking” (with high customer empathy), first-principles thinking, fast execution focused on raw problem-solving skills (complemented by strong data-driven experimentation), insane passion, and grit” (in Anuj’s words).

To understand Swiggy’s product and growth formula, we invited Anuj for the 2nd Masterclass by ProductGrowth.org. Before the session, we requested community members to tell us to know what they wanted to hear from Anuj and got the following response:

Based on the community feedback (and specific questions from the community members — both before and during the session), we had a conversation around building habits, driving scale, and effective growth architecture. It was a power-packed conversation, full of insights and suggestions about how other companies can drive efficient and sustainable growth. You can listen to the full conversation below:

Or, if you prefer, you can scan through the summary below. If you have any questions or suggestions, please do let us know by responding to this story below.

Reacting to the Covid19 crisis

Mindful of the fact that the startup community (indeed, everyone across the world!) is grappling with the unprecedented Covid19 crisis, we started the conversation by discussing how Swiggy was responding to the crisis.

This is an important question because, in the times of crisis, both the JTBD (Jobs to be Done) and GTBA (Goals to be Achieved; the “why” behind the JTBD; for example, the importance a person attaches to JTBD and other deeper requirements; see here for more details) can change dramatically. Given this, how can a startup or company respond quickly?

In this context, it is useful to highlight that Swiggy has reacted very quickly during the current crisis: starting with “zero contact deliveries”, Swiggy has launched new services to meet evolving customer needs. For example, Swiggy Stores for groceries and Swiggy Genie for instant pickup/drop service (to get groceries and other essentials from nearby stores). Given this, it is interesting to understand what has enabled Swiggy to iterate quickly and respond effectively to the rapidly evolving Covid19 crisis?

Anuj provided a two-part answer to this question.

The first one was related to customers-backward thinking. Swiggy uses a framework called “Accepted Customer Belief” (ACB) for this purpose. ACB is typically used by the marketing teams to understand how customers look at a product (and the broader product category). ACB is used to understand whether the company understands customers or not — especially, the target consumers’ frustrations of their unmet needs.

Swiggy has found that ACB is a wonderful tool for imbibing customers-backward thinking in the product teams. ACB also enables product teams to capture evolving customer needs and wants quickly. For example, before the Covid19 crisis, ACB for college students (to pick a specific persona) included: need for not-expensive food, extremely fast delivery, and desire for discounts. However, after the spread of the Covid19 crisis, their ACBs changed and were instead focused on safety (of the delivered food and the delivery partners). In addition, the speed of delivery became less important though the need for not-expensive food continued. In other words, though JTBD remained the same, the underlying requirements (GTBAs / expectations) changed significantly. In some sense, the changing environment needed a “new product” and Swiggy, indeed, iterated quickly to validate the PMF for this new incarnation of the product.

ACB framework, therefore, provides a quick and methodical way of understanding customer requirements — esp. the non-functional requirements underlying the tasks/activities they want to fulfill.

The second part was related to how to structure and organize teams so that the startup has the agility required to react quickly to a crisis (as well as the changing market dynamics, in general). We will take a look at this towards the end of the article.

Habit

We started the conversation on habits by pointing out the inapplicability of the popular Hooked framework (by Nir Eyal) for building habits for a majority of companies. The Hooked framework suggests that there are four levers for creating habits: Trigger — Action — Variable rewards — Investment. However, some of its levers (such as “action” and “investment”) can’t really be tweaked around much for a majority of the usage scenarios. Moreover, “variable reward” is also not typically available — in a lot of use-cases, the certainty of results (for example, “food delivery within 40 minutes”) is more important than the variability of rewards. In fact, non-variability and consistency of delivery is the “reward” that is valued by the customers. So, one is left only with a “trigger” as the habit-building lever! Perhaps this is the reason why Hooked framework inadvertently triggered a deluge of much-too-frequent and bordering-on-spam push notifications (“external triggers”) that did more harm than good to the companies trying their best to create the elusive habits.

Anuj pointed out that “growth hacking” — another Silicon Valley fad — is not useful either; it is typically associated with the narrow definition of growth: tactical growth focused on increasing DAUs, MAUs, etc. by running incremental experiments.

So, how can one build habits?

The starting point for building habits is to understand customer requirements deeply. As discussed above, this needs the company to understand both the JTBD and the “core assumptions” (ACBs) of the customers.

Based on this foundation, the first step is to build a product that works for consumers. This is critically important because several companies rush to increase usage by 20–30% (via growth hacking, etc.) before doing this.

Once this has been achieved, the next step is to understand the natural frequency of the activities under consideration. Now, Swiggy can consider itself to be either in the business of delivering restaurant food to customers (for example, as a substitute to eating-in at a restaurant; this would happen, say, 5–10 times a month) or in the business of delivering food (which can happen much more often because customers eat meals 3–4 times per day or 90–100 times a month).

In fact, one needs to look at the natural frequency together with the target persona (or segment). Target persona can incorporate not only behaviors and segments but also the customer’s value (i.e., CLTV). Note that the CLTV should not be the value the persona is providing right now but the maximum value they can ever provide (which, itself, depends on the maximum frequency of usage for the persona). Swiggy, for example, understands that there is a large difference between students and young professionals who are Snapchat users versus those who are not — these two sets vary significantly in terms of their CLTV (or “aukaat”, the colorful term used by Anuj). Swiggy goes further and understands that the same user can have different behaviors at different times: a user can be “speed seeker” on weekdays (while ordering food from the office) or “discount seeker” on weekends (when ordering lots of food for the whole family)!

Once the natural frequency for each persona is understood, the next question becomes: how can we help each persona increase their usage so that they get close to their natural frequency (which is the maximum usage frequency for the persona)?

In this context, Nir Eyal’s “triggers” become just one way of getting there. Another way could be building the best possible product and consistently delivering on the brand promise. This, then, gets people to start talking about the product/service and help spread the word. This can help make using a company’s product a social norm and, therefore, lead to building a habit!

With this view, habit is all about increasing the frequency of product usage until it reaches natural frequency. Habit, therefore, becomes a means to an end; the end remains to deliver value to customers and making sure their needs and goals are fulfilled.

Anuj used the example of the Super subscription program to explain how Swiggy actually leverages the above approach to habit building. Let’s look at the journey in detail.

The first question Swiggy asked was: what is preventing customers from ordering at or near natural frequency? For this, they spoke to customers who were “super users” — that is, who had been ordering fairly frequently (say, 15–20 times a month). With the help of user research, they discovered that almost all of them hated the delivery fee: either they didn’t like the very idea of delivery fee (especially because they realized they were giving good business to Swiggy) or (worse) they hated the bill shock they got at the end of the ordering session — especially when Swiggy levied higher surge delivery prices.

Second, while starting to explore a subscription program, Swiggy also looked at several variants of subscription programs: from a single-tier (paid) membership program (like Amazon Prime), multi-tiered membership program (like airline miles programs), earn-and-burn programs (like Flipkart Plus), and so on.

Third, with the goal to increase the frequency of usage, Swiggy looked at possible benefits that the membership could offer. For example, these were: faster delivery promise, higher discounts from customer’s favorite restaurants, higher overall discounts, free delivery without surge fees, etc.

Fourth, to understand how customers would react to these, Swiggy designed landing pages for various options and used them to simulate experience. Swiggy then tested various options with a small set of representative customers. Swiggy not only looked at the quantitative data to analyze how super users engaged with the simulated options but also interviewed them to get a qualitative feel about their reactions.

Finally, to choose between these options, Swiggy applied the RICE framework (where R = Reach, I = Impact, C = Confidence, and E = Effort) to perform a cost-benefit analysis and to pick the best candidate. For example, RICE analysis revealed that “faster delivery promise” not only required a lot of effort but also implied that faster delivery for super users might worsen the experience customers (because the delivery partners would get allocated to serve the super users and, therefore, might increase the delivery times for other customers). This implied that this option not only had high ‘E’ (effort) but also low ‘C’ (confidence).

Based on the above sequence of steps, Swiggy designed and launched with the current version of Swiggy Super with unlimited free deliveries, no surge fee during rains or high demand, and “free delights”.

Scale

How was Swiggy able to scale 30x in 3 years? What are the frameworks and playbooks that might be useful for others?

For the sake of the context, it is worth recalling the market landscape for the “food delivery” business in 2016. Following on the heels of the rapid growth of e-commerce adoption in 2015 and 2016, “food-tech” industry was super hot in 2016. There was strong competition amongst several energetic and skillful players. For example, there were companies such as TinyOwl (which provided beautiful user experience), Ola Cafe (which had the reach of Ola), FoodPanda (which had strong financial backing and access to international knowhow), etc. Also, UberEats was around the corner. The “incumbent” was Zomato, which had built India’s largest platform for restaurant reviews and ratings (and, therefore, had built strong network effects with “insurmountable moats”, using Warren Buffet terminology).

Therefore, there was an immense pressure on Swiggy to scale fast — while also trying to carefully balance the growth of 3-sided marketplace consisting of customers, restaurants, and delivery partners.

So, how did Swiggy grow 30x in such an intense environment?

Anuj explained that Swiggy did this by first figuring out the growth drivers and then deeply understanding the growth equation of its business.

What does this mean?

For Swiggy, its growth equation indicated that supply-side capabilities needed to be built and stabilized before unlocking the demand. More specifically, Swiggy discovered that the demand (and conversion rates, for example) was correlated with the density of restaurants in an area. For example, conversion rates were low when there were less than 80 restaurants in an area, the conversion rate increased as the restaurants increased from 80 to 225 and beyond; and, interestingly, conversion rates started falling if there were too many restaurants in an area (due to additional cognitive load as a result of too many choices, it turns out)!

Therefore, to launch in a new zone, Swiggy needed to first onboard ‘x’ number of restaurants and ‘y’ number of delivery partners and then kickstart the marketing engine to generate demand (which needed to generate at least ‘z’ orders for the engine to continue working smoothly). Doing so ensured that the customers had a great experience right from the start!

For a new zone (and a new city) launch, Swiggy uses a framework that Anuj referred to as ADS where:

  • Stands for Availability (which corresponds to the density of restaurant)
  • D stands for Discoverability (of restaurants and dishes which, in turn, depends on the richness of restaurant menus)
  • S stands for Serviceability (which corresponds to fulfillment and depends on the number of delivery partners)

Other marketplaces might also find variants of ADS to be useful to connect the supply-side drivers with demand-side drivers. This is because ADS facilitates a combination of supply-side analysis with demand-side behavior. Swiggy has found that ADS is strongly correlated with business metrics and helps to predict consumer behavior.

Anuj emphasized that Swiggy has always taken a customer-centric approach while scaling; Swiggy had resolved to scale only when the company was sure that it would not provide a poor experience to customers. This is why Swiggy had limited its operations to only 7–8 cities during the first few years of its journey.

How can other startups mimic Swiggy’s exponential growth trajectory? Anuj mentioned that once the growth equation is understood by a startup, its scale equation and growth roadmap would become clear as well. It becomes clear which levers need to be pulled to achieve scale.

Anuj also pointed out that startups should keep going back to their growth equation in order to continuously improve it. A startup’s growth equation keeps evolving based on customer behavior changes, macro-economic changes, market dynamics changes, etc.

Brand

We didn’t explicitly discuss Swiggy’s product-led brand activities. However, as part of Habit and Scale discussions, a few things did emerge that are relevant from the brand perspective.

It is important to highlight that brand should not be treated as synonymous with the brand logo or brand marketing campaigns. Brand, instead, should be considered as the tangible and verifiable promise that a startup makes to its customers and, subsequently, the product, operations (logistics) and other activities that the startup does to ensure that it consistently delivers on the promise.

ACB, that Anuj referred to, is one of the tools used in the marketing function to gain insights into why consumers do what they do. By acknowledging consumers’ point of view with understanding and empathy, startups can put together a tangible promise that not only highlights the benefits of the product but also gives them Reasons To Believe (RTBs, in the marketing speak).

Swiggy, right at the start of its journey, realized that the food delivery product needed a clear and simple articulation of its brand promise. Swiggy realized that consistency of delivery (within the promised delivery times) would help it build an early differentiator. Towards this, Swiggy launched the “Swiggy Assured” guarantee with “on time or on us” promise backed by “25% cashback” if the food wasn’t delivered within the promised duration (typically 30–40 minutes).

By consistently delivering on its promise, Swiggy had laid the foundations for building a strong brand. By ensuring awesome customer experience, Swiggy was able to build trust and unlock growth in its initial years. Anuj pointed out that Swiggy didn’t need to do any digital marketing for the first few years.

One of the observations that can be made based on our conversation was that brands can be strengthened by understanding the ACBs of the “super users” (i.e., the most engaged users). Swiggy did this while developing the Swiggy Super program; Anuj pointed out Swiggy spoke with several loyal customers while exploring options to recognize and reward the super users in the most effective manner.

An insightful side story shared by Anuj during the conversation was the unexpected benefits of running an India-wide brand marketing campaign in 2017 (when Swiggy was present in only 7–8 cities). The highly visible campaign resulted in “market spillage” and the company received eyeballs and visibility in the cities where it was not present. As a result, the campaign triggered consumers across India to download the Swiggy app to explore the service. The number of downloads from various cities, in turn, revealed latent demand across different cities. This provided useful signals for Swiggy to decide the order and priority of launching its operations across cities! Combining this demand-side metadata with a replicable go-live playbook (based on the ADS framework), Swiggy rapidly expanded to more than 500 cities by the second half of 2019.

Growth Team Architecture

How can a company structure and organize its teams so that it not only it can unlock and achieve its growth potential but also be agile and respond quickly and thoughtfully to the changing market dynamics? What has helped Swiggy to react quickly to a crisis such as Covid19?

Anuj explained the team architecture that provides a strong growth-oriented foundation to Swiggy (despite having grown rapidly over the last few years) and helps Swiggy to be extremely agile. To build and organize the teams, Swiggy looks at five parameters:

1. Impact

2. Speed

3. Confidence

4. Quality

5. Agility

Depending on the variation of needs across these five parameters, Swiggy has five different types of teams within Swiggy:

1. Core teams: work on platform capabilities (for example, search and logistics capabilities, for example) and drive medium-term projects (such as Swiggy Pop, Swiggy Super, etc.).

2. X teams: churn out quick, incremental experiments (for example, Swiggy “takeaway counter” at airports).

3. Growth teams: explore adjacencies and other growth avenues. Growth, for example, can come from category adjacencies (for example, grocery delivery), geographical adjacencies (for example, international expansion), JTBD adjacencies (for example, bike taxis), and so on.

4. Big bets (new businesses): explore new business opportunities and respond to evolving (macro-economic, competitive, etc.) climate.

5. Labs: explore moonshot initiatives and very long-term strategic projects; for example, exploring using drones, noddle-making machines, etc.

Note that the above teams are not “two-pizza teams” or “agile pods” or “scrum teams”. Unlike these team structures (which are used to provide agility within product-tech teams), the above is more deliberate and thoughtful team architecture that provides both short-term agility and long-term competitive edge to the company.

Anuj highlighted the differences with two-pizza teams, et al by explaining that each of the above teams has different DNAs. For example:

  • The core team is geared towards medium to high impact, low to medium speed, medium to high confidence, high quality, and low agility.
  • Big bets team is geared towards high impact, high speed, low confidence, medium quality and high agility.

Core team, for example, works with lots of data and thorough while Big bets team has to work with much less data and lot of intuition. Due to different charters and different focus areas, each of these five teams, naturally, have different DNAs. Since there is clarity in each team’s charter and clear expectations from each team, it is possible to pick the right processes and right metrics for each team.

Swiggy Pop and Swiggy Super, which were handled by the Core team, were thought-through and tested products when they were launched. On the other hand, Swiggy Stores and Swiggy Genie were built and launched quickly so that the product/service offerings could be quickly iterated upon and improved based on customer needs and feedback.

This team architecture makes it easier to staff various types of teams appropriately. To allocate resources appropriately across different teams, Swiggy decides annual and quarterly goals/targets and, depending on that, figures out initiatives and projects needed to meet those goals. This, therefore, helps to decide how to allocate product-marketing-tech resources across various teams. Anuj recommended referring to “Team Topologies” (link) book for more details in this regard.

This team architecture is what enables Swiggy to respond quickly to evolving marketing situations. And, it is this team architecture that enabled Swiggy to move so swiftly during the Covid19 crisis: it was able to make sure that the relevant teams were resourced properly and supported appropriately.

There are only four levers of growth for any company [link]:

  • Scale: supply-side and demand-side scale. In Swiggy’s case, one needs to include the scale of delivery partners as well.
  • Habit: as discussed, we use this term to refer to product-based and other activities that help increase frequency of usage so that it gets close to the “natural frequency”.
  • Brand: we use this term to refer company’s ability to meet its tangible and verifiable brand promise (which, in turn, results in customers developing emotional connect with company’s products). Emotional connect helps customers to become more positive inclined to continue to use company’s services (and reduces price sensitivity, for example).
  • Network effects: network effects can help Swiggy to strengthen the 3-sided marketplace even further.

In order to devise a growth roadmap, first and foremost, a company needs to understand its growth equations. Anuj repeatedly emphasized that PMs should strive hard towards figuring out and refining their growth drivers/levers as well as the growth equation. Anuj explained how Swiggy uses ADS framework for this.

Next question is: how does one come up with the right growth roadmap?

Anuj pointed out that organizations where business goals are set by the “management” and then cascaded down to teams across the organization are not able to perform optimally. If the goals flow only from top to bottom, the company is not able to leverage full potential of PMs (as well as engineers and other operators in the company) that work in the trenches and have a more accurate understanding of the ground realities as well as more realistic view of future possibilities.

Anuj emphasized that the best teams (especially, product and growth teams) build roadmaps and set goals using a combination of top-down and bottom-up discussions. Swiggy, for example, starts with a bottom-up process where teams come up with their ideas and suggest possibilities. Product leaders collate these ideas and convert them into projects. The projects are used to devise various growth scenarios. These scenarios are discussed with the senior leaders where they are considered in the context of strategic directions (such as customer experience goals, future goals and targets, competitive pulls-and-pushes, fundamental requirements, etc.). Based on the strategic directions, all the proposed projects are classified into different buckets, their tradeoffs are discussed, and prioritized appropriately.

OKR is one of the most popular techniques used by companies worldwide to merge top-down cascading and bottom-up project propagation. In this context, we discussed Swiggy’s experience with the OKR process.

Anuj pointed out that OKR planning doesn’t work well for Swiggy. This is because the growth equation at Swiggy is not simple — various growth levers are dependent of each other; in fact, various growth levers are deeply intertwined with each other.

Given that Swiggy is a 3-sided marketplace, its growth equation depends on all three moving parts. The three sides of the marketplace themselves are highly interlinked — metrics of different sides are closely tied to each other and every metric has potentially large impact on the overall success metric.

Anuj suggested that OKRs work well if a company has simpler growth equation with independent growth levers. Independent growth levers make it possible for different teams to have independent and complementary goals. Independent goals and metrics — especially when the metrics are additive (and not interlinked and interdependent) — make it possible to split company-wide goals into sub-goals and to cleanly distribute the sub-goals to different teams. Recursively, each team is also able to map its goal (the sub-goal owned by it) into smaller goals (and subsequently into potential projects). Of course, if there are some dependencies, it is possible to ensure that the teams talk to each other and resolve the dependencies.

Marketplaces with interlinked parts (and interdependent metrics) render OKR process ineffective. Anuj referred to this as the “butterfly effects” and, as an example, mentioned that a small variation in earnings per hour of delivery executive has been observed to have an impact on conversion rate (on consumer side)!

Due to the intertwined nature of its products/projects, Swiggy looks for win-win-win while evaluating possible projects. Each project is evaluated from the perspective of whether it would provide a ‘win’ for consumers, a ‘win’ for restaurants, and a ‘win’ for delivery partners.

Consider the example of Swiggy Pop, which is Swiggy’s offering of single-serve meals. Customers ‘win’ because of fewer choices and the ease-of-use. Restaurants ‘win’ because Swiggy provides predictable order requirement with partner restaurants in advance. Delivery partners ‘win’ because they are able to batch more orders and do more deliveries. And, by delivering value to each of its stakeholders, Swiggy ‘wins’.

Anuj pointed out that it is not easy to pick one or two metrics for projects such as Swiggy Pop. This is because the success of Pop relies on all parts of the 3-sided marketplace. Moreover, it is not possible for any single PM to own such a product. Projects such as Swiggy Pop needed end-to-end teams (and not just ‘two-pizza teams’ spread across product and tech teams).

Finally, Anuj suggested that it is good to consider product roadmaps with three different horizons:

  • 18 months perspective about key levers in the business and how they might evolve (this is useful to figure out roadmap for new initiatives)
  • 6 months visibility on business metrics and goals
  • 3 months: a detailed quarterly product roadmap and the corresponding execution plan

April 11, 2020

Swiggy’s Growth Formula that Drove 30x in 3 years!

Anuj Rathi (VP for Product, Revenue and Growth) is a senior product and growth leader at Swiggy. Anuj joined Swiggy in Aug 2016 when...

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Masterclass
Marketing
June 27, 2020
How to build Digital-first Brands: The Flipkart Story

Everyone in the startup ecosystem has heard about the main Flipkart story… and it is a fascinating story because it had many interesting...

Dr. Ajay Sethi
18
min read
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Trishul, Kumaon range in Himalayas [credit: link]

Everyone in the startup ecosystem has heard about the main Flipkart story… and it is a fascinating story because it had many interesting characters, a lot of suspense and many sub-plots. Flipkart’s efforts to build a brand that was accessible to all Indians — not just to consumers in metros but also to consumers in Tier 2, 3, 4 cities across India — was a wonderful sub-plot. Shoumyan Biswas spent six fruitful years with Flipkart between 2013 and 2019 and was the Chief Marketing Officer (CMO) for Flipkart starting 2017 and CMO and Business Head of Loyalty, Partnerships & Advertising for Flipkart towards the end. We requested Shoumyan to share details about this aspect of Flipkart’s journey.

The insightful conversation in its entirety can be view below:

Or, you can scan through the main points from the conversation below.

At a high-level, Flipkart’s pre-Walmart acquisition journey can be divided into three phases:

  • Phase 1 was the foundational phase between 2008 and 2014. Flipkart initially focused on selling books, building India-wide distribution network, and providing great customer experience to early adopters of the service. After cracking that, Flipkart expanded to electronics in 2011 and acquired Myntra in 2014. Flipkart held its first Big Billion Day sale in 2014.
  • Phase 2 was the intense competition phase: Amazon launched its marketplace in India in 2013 and, in 2014, announced that it will invest at least $2B to grow its footprint in India. So, the race to dominate Indian e-commerce started in earnest in 2014. Between 2015 and 2017, there was an intense fight between Flipkart and Amazon.
  • Phase 3: By early 2018, it was becoming clear that Flipkart would be able to hold its ground against the much larger and much stronger competitor. This led to the consolidation phase for Flipkart. In this phase, it was important for Flipkart to strengthen its lead and to ensure that the advantage does not slip away.

Phase 1

In the first phase, Flipkart had laid for building a strong brand by focusing on providing great customer experience. Flipkart also used Cash-on-Delivery (CoD) to build trust with customers.

Shoumyan mentioned three lessons and guidelines from these early efforts:

[1]

For digital-first brands (and increasing for all brands), “brand” is what one consumer tells another about the company (as opposed to pre-social media days when “brand” used to be what companies proclaimed themselves to be).

What used to be one-way communication from brands to consumers two decades ago has evolved into two-way communication between brands and consumers. Social media has not only provided a voice to the consumers but have also increased their circle of influence. This implies that brands need to consciously shape consumer narrative around the brand.

Based on this understanding, Flipkart’s marketing team had dual charter:

1. Make people transact with the brand

2. Make people to talk about the brand

Flipkart consciously worked to drive conversations about Flipkart amongst consumers.

[2] Brands — just like two human beings — go through stages of evolution. These stages are:

Unknown → Known → Known for something → Trusted → Loved

While building brands, marketers should not use marketing activities only for tactical reasons (e.g., using TV commercials to build awareness or leveraging discounts to get volumes). A strategic approach is to align the actions with the stage of the brand.

How did Flipkart move across these stages?

  • Unknown → Known: Flipkart relied on superior quality of service and concomitant word-of-mouth in the initial stages. In the next stage, Flipkart leveraged performance marketing and communication channels (including advertising). This is when Flipkart identified the “kidult” theme (kids shown as grownup adults).
  • Known → Known for something: Flipkart worked on making itself a destination for online shopping that was as easy as a child’s play. It also promoted itself as the destination for buying the latest and the coolest smartphones.
  • Known for something → Trusted: Flipkart worked to communicate that consumers can’t go wrong with Flipkart. In order to make itself as a trusted destination for e-commerce across the whole country, Flipkart emphasized cash-on-delivery, original products, etc. This was reiterated by the “Flipkart Assured” program and supported by the “Flipkart matlab bilkul pakka” campaign.
  • Trusted → Loved: It is important to move beyond the transactional value for this phase. Flipkart did this by thoughtfully identifying the “purpose” context that was aligned with its mission. Flipkart emphasized that it was an Indian brand that stood for everything that makes India progressive. For example, Flipkart embraced diversity of India. Shoumyan mentioned that one of his favorite themes of this phase were the “Penguin dad” campaign that celebrated fathers helping out their wives by sharing the parenting responsibilities.

How can a brand measure whether or not it is making progress across these stages? Shoumyan mentioned the following metrics:

  • Unknown → Known: search volume, top-of-mind recall, and spontaneous recall (brand should be amongst the top 3–4 most aware players in the chosen category).
  • Known → Known for something: consumer perception on key attributes (in order to measure that Flipkart as the destination for the coolest/latest smartphones, the scores should be the highest amongst all players), social NPS (to reflect that consumer voice is building up), acquisition funnel measures (for example, CAC and cost-of-installs should decrease).
  • Known for something → Trusted: measurement about trust attributes and levers (whether they are going up or not; for example, what percentage of sales come from “Flipkart assured”, “original products”, etc. anchors). Also, in this stage, spontaneous recall should be in mid- to late-90s and almost everyone should knows what the brand stands for. In addition, the brand should get higher repeats leading to higher LTV (life-time value) of customers.
  • Trusted → Loved: customers should become more forgiving of mistakes and less price sensitive.

[3] Non-digital (“traditional”) brands operate with the think-do-think model; however, digital-first brands need to adopt do-think-do model of execution. This is because, unlike traditional brands (say, FMCG brands), launch of a product can take 18 months. Digital world moves much faster and, therefore, digital-first brands need to “fail fast and learn faster”.

Phase 2

In 2015, Flipkart had ~45% marketshare while Amazon had ~10% marketshare. This changed dramatically in 2016: Flipkart’s share reduced to ~35%, Amazon grew to ~25%. Flipkart was rapidly losing the marketshare to Amazon… and, from the outside, it looked like Amazon might win the war. But then something changed. Flipkart was not only able to hold on to its marketshare and but slowly increase it. What triggered this turnaround?

Shoumyan pointed out that Flipkart started by turnaround by fixing the mistakes that had crept into its consumer strategy. In order to improve unit economics (to move quicker towards profitability) and to enhance its reach, Flipkart had adopted the marketplace strategy for expansion. However, the marketplace strategy (due to lack of oversight and control) caused the user experience to degrade and for consumer trust to get eroded (to an extent). This is what provided a foothold to the competitors.

Flipkart recognized these mistakes and adopted three-pronged strategy to fix this and move forward:

  1. Getting focus back
  2. Get more out of less
  3. Relentless execution

[1] Getting focus back

First and foremost, Flipkart decided to focus on value-seeking, middle India consumers. As a result of this focus, Flipkart changed its focus on categories, products, etc. Shoumyan pointed out that any strategy has two parts: what you will do and what you will not do. As a consequence of focus on middle India consumers, Flipkart consciously decided to not fight the battle for experience-seeking affluent consumers in Tier 1 cities.

Even more importantly, Flipkart really understood what value meant: it meant more benefits for a given price. And, importantly, it didn’t mean the lowest price and it didn’t mean the biggest discount. As a result, Flipkart explore how to provide more benefits for a given price-point (instead of working towards reducing the price points).

This influenced not only the Flipkart product user experience but also helped identify the right promotional model. It also helped Flipkart prioritize entry into consumer finance and other Fintech products.

As an example, Shoumayn’s talked about “Itne mein itna” campaign during this phase, which emphasized more benefits for a given price point.

[2] Getting more out of less

To get more out of less, Flipkart used a 2x2 matrix with effectiveness and efficiency. This helped Flipkart track the effectiveness and efficiency of marketing spends across social media (engagement rate vs cost of engagement), performance marketing (cost-per install vs average revenue per install over 14 day period), etc.

Flipkart used this matrix to double-down on channels and activities that were yielding results while eliminating those that were not. It helped to figure out which sources maximized effectiveness while increasing efficiency.

It was also used for allocating marketing (Search Engine Marketing) budget across different categories. For each category, SEM spends were plotted against RPC (revenue per click). Flipkart found that for each category, there are specific levels of SEM spends for which RPC gets maximized; beyond this threshold, the marketing spends yield diminishing returns.

Flipkart used this mechanism to optimize its SEM spends. During this intense competition period, Flipkart found that the competitors — despite spending four times more advertising money — had got only 20% additional boost in revenue.

[3] Relentless execution

Core values in the marketing team: Creative excellence, Frugality, and Marketing Innovation.

For brand marketing perspective, creative excellence is critical because if creative is good, it helps the company to achieve its goal by spend less money. If the message is sharp, brand can get the same effect with lower budget. The other way to eliminate misattribution is to pick a unique “device” or theme. For Flipkart, kidadults device helped it to cut through all the clutter.

Frugality has two aspects: how to “get more out of less” and how to reduce ineffective spends. Frugality also meant being data-driven about measuring effectiveness of the marketing activities.

Flipkart really pushed the boundaries in marketing innovation and continuously experimented with new things. Shoumyan pointed out that, at one point, almost half of new innovations done by Google and Facebook were done in collaboration with Flipkart. As an example, Flipkart took personalization to the next level by creating more than 3 million personalized video assets. This helped to improve conversion by 45%!

Phase 3

After establishing and retaining lead over Amazon, it would have been tempting to take the foot off the pedal. But Flipkart didn’t do this. Flipkart, instead, consolidated its lead and ensured that its advantage didn’t slip away. What did Flipkart do to become more efficient and more effective during this period?

Flipkart ensured that the — though the victory was celebrated — it didn’t make Flipkart lose the focus or to let complacency set in.

First, Flipkart worked to strengthen the customer funnel and explored how it can engage users better to increase customer LTV.

This was done after segmenting the users and developing a clear understanding of customer requirements. Flipkart used behavioral customer segmentation process. Shoumyan pointed out that any customer segmentation mechanism (based on demographics, psychographics, etc.) can be used as long as it provides Mutually Exclusive and Collectively Exhaustive (MECE) segments. Another important requirement is that the segments should be targetable and actionable.

Flipkart used the behavioral segmentation to divide consumers into 5 segments:

1. Browsers,

2. Lapsers,

3. Light users,

4. Heavy users, and

5. Super-heavy users.

Behavioral segments were created on the basis of Frequency and AOV (Average Order Value). Shoumyan pointed out that it would be create behavioral segments using contribution margin (in addition to AOV) in order to assess whether the customers were value building or value eroding.

Based on customer segments, Flipkart knew how to engage with the customers and what were the key tasks they wanted to achieve. For example:

  • Browsers and lapsers: acquire more new users or re-acquire old users (and engage them to deliver the initial value)
  • Light users and Heavy users: drive more usage by increasing frequency of purchases
  • Heavy user and Super-heavy users: make them upgrade to higher order products and make them buy across categories

For each of these segments, Flipkart created separate marketing programs and defined the relevant metrics. For the browsers and lapsers, Flipkart marketing team designed acquisition programs and reactivation programs. For the light users and heavy users, Flipkart designed upsell/cross-sell program and created loyalty program. And so on. Overall, the goal was to make it easier for Flipkart to acquire users and then move them faster across the customer journey (from light to heavy to super-heavy users). In parallel, the goals was to make the customer bucket less leaky and to reduce the number of lapsers.

Second, Flipkart looked at vertical businesses to leverage them to complement the horizontal e-commerce platform. As part of this, Flipkart expanded to launch fashion, household goods, small and large appliances, baby, grocery, etc. verticals. The goal was to expand beyond the smartphones and electronics, apparel (esp. sarees), and branded goods (such as shoes) verticals.

Flipkart also forayed into non-ecommerce categories such as travel bookings, phone recharges, Flipkart videos, etc. The goal was to either maximize transactions or to maximize time spent with Flipkart. In other words, either more units sold or more DAU (Daily Active Users). Appliances, baby, grocery, etc. verticals were geared towards more units while videos, recharges, etc. were geared toward more DAUs.

Flipkart’s brand journey provides a good template for digital-first brands to chart out their journey. In addition to sharing the frameworks and principles for building digital-first brands, Shoumyan also answered a number of questions from the community members. We will share a summary of some of those in a separate post. In the mean time, if you have any comments and questions about Flipkart’s brand journey or the journey of any other digital-first brand, please do let us know.

June 27, 2020

How to build Digital-first Brands: The Flipkart Story

Everyone in the startup ecosystem has heard about the main Flipkart story… and it is a fascinating story because it had many interesting...

Read More
Masterclass
Marketing
August 6, 2020
SEO Magic: How Housing grew more than 5x and overtook Magicbricks and 99Acres in 18 months

Ravi Bhushan is the founder of early-stage Ed-tech company, Brightchamps. Before venturing on his own, he was the Chief Product and...

Dr. Ajay Sethi
10
min read
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Ravi Bhushan is the founder of early-stage Ed-tech company, Brightchamps. Before venturing on his own, he was the Chief Product and Technology Officer at the PropTiger Group which runs three real-estate properties: Housing.com, Makaan, and PropTiger. Ravi also held the business responsibility of Makaan.

For the sake of context, Magicbricks and 99Acres, are the incumbents of the Indian real estate business. At the start of 2017, Magicbricks and 99Acres were roughly five to seven times larger than the combined traffic of Housing, Makaan and PropTiger. But, in less than two years, by Sept 2018, the PropTiger Group (for the sake of simplicity, referred to as Housing from now onwards — since Housing is the consumer-facing brand and the largest destination within the PropTiger Group) overtook both Magicbricks and 99Acres and became the largest real-estate destination (in terms of the combined traffic). More importantly, most of this traffic was organic traffic.

How did Housing achieve this? What SEO magic did they perform? Ravi shared his secrets during our conversation.

Ravi’s SEO journey into the SEO world started due to “a traffic accident involving Panda in India”! Housing’s traffic dropped by 70% overnight due to Panda penalty imposed by Google. This put paid to the product and engineering investments that the Housing had made over the previous several quarters. Ravi said, “it is painful to have created a beautiful painting and then realizing that someone has put the painting in the dark room and locked the door”! It is important to not only build good products but also to have a clear-cut strategy in terms of how the traffic, especially organic traffic, will come.

What is the most important SEO activity?

The most important and the most impactful SEO activity is “site structuring”. Site structuring ensures that the website is structured in such a way that Google bot crawling the website is provided with the right syntactic and semantic signals about the importance of different sections, pages and content of the website. Site structuring, therefore, focuses on getting the basics right.

First, company should come up with the right information architecture based on the semantic information that the company wants to communicate. The site should be structured such that not only a human being but Google Bot can also understand the importance of different topics / concepts. Each section (corresponding to a topic / concept) should be provided with the right weightage.

To do this, each section of the site should provide coherent meaning and different sections should not overlap too much with each other. For example, a car website can have hub-pages corresponding to the brands (such as Maruti, Tesla, Mercedes, etc.) and, under those pages, hub-pages for various models of the brand. In addition, car website can have a different set of hub-pages for different categories such as sports cars or electric / diesel cars. It is important to structure these pages carefully (as well as link them to the right product detail pages) so that users as well as Google bit can understand the semantic meaning of different sections.

Second, in order to give the right signals to Google and other search engines, it is important to link the pages in a thoughtful manner. Linking pages indiscriminately and ending up with a spaghetti of interlinked pages (including, for example, linking “Contact us” or “Disclaimer” pages from all pages — a common mistake) confuses Google bot and often leads to pages not getting indexed and ranked properly.

Ravi pointed out that linking two pages is nothing but a vote of trust that one passes from one page to another. It is important to link pages by double-checking them from the business priority point-of-view, keyword point of view (importance of specific topics that the company wants to cater to), and from traffic point of view (what opportunities areas are there and how does the company serve those opportunities).

Ravi pointed out that such site structuring experiments can have short-term negative impact (since Google has to decipher new structure and change the earlier set of indexed pages). For example, Housing experienced 30% drop in traffic in the first month after releasing website with proper site structure. However, the traffic increased by 350% in the second month — so, one can get really good results just by doing site structuring properly.

How can one prioritize across various SEO experiments?

After taking care of hygiene-level activities such as site structuring, it is good to use a prioritization framework that takes ROI into account. One way to compare various experiments is to measure them along four parameters:

  1. [Volume] Volume of opportunity: quantum of traffic available
  2. [Quality] Quality of opportunity: relevance of traffic to the business
  3. [Difficulty] Keyword difficulty: how much is the competition for specific keywords
  4. [Cost] Cost of experiment: effort required to run the experiment

First are foremost, one needs to be aware of the given keyword universe and the size of the opportunity — number of search traffic volume one can go after. Next, one needs to consider users’ intent — which helps one to understand the ability to engage with and convert the visitors into customers. Third, one needs to factor in the competitiveness for specific keywords — what’s the domain authority of the competitors, what keywords have they captured deeply, etc. And finally, the cost of the experiments corresponds to the effort (engineering, content, etc.) required to do the tasks.

Given the above, the experiments can be ranked using the following ROI metric:

(Volume * Quality) / (Difficulty * Cost)

This can be applied to various types of experiments — related to a set of keywords, adding platform features (such as images or videos on product pages; or starting initiative to collect UGC content, etc.), or even re-building the platform from scratch.

Unfortunately, almost 70% of time SEO activities are related to minor tweaks such as title changes, meta-tag changes, writing more content, etc. However, by taking the ROI-based approach, one can avoid dealing only with tactical stuff and start picking up more strategic initiatives. Of course, the ROI metric is just an indicator; the exact decision should be made by considering other qualitative aspects such as the business vertical, type of website (transactional or informational, e.g.), platform capabilities, etc. Together with the ROI metric, one can arrive at the most effective SEO roadmap.

Using the above framework, Housing was able to pick the right experiments that helped them surpass Magicbricks and 99Acres, both of which had much higher domain authority. Instead of going after every keyword related to Indian real estate and competing with the incumbents on the “head” keywords (that generate a lot of traffic; these were “property in Mumbai”, “property in Gurgaon”, etc.), Housing focused on the housing project related keywords. In addition, Housing realized that a lot of traffic (as well as higher intent traffic) was in the long-tail keywords such as “2BHK property in Goregaon”, “3BHK Sohna Road”, etc.

These decisions (to focus on specific set of keywords, e.g.) have an impact on the site structure and the information architecture. They also have implications on what kind of platform one needs to build — for example, how should search and navigation function, how should mapping and other infrastructure work, etc. These need to be factored in while calculating the cost of the experiment.

Ravi emphasized that the same analysis can be done by early-stage startups as well. For this, he gave an example of PDFdoctor.com, a passion project that Ravi started after leaving the PropTiger Group. PDFdoctor provides tools to work with PDF documents (like merge or split PDF documents, etc.). As one can imagine, there is a lot of traffic for online tools related to the PDF; also, this is a super-competitive space with several companies working on building tools (and doing SEO for them) for over a decade. However, by prioritizing various activities using the above-mentioned framework, PDFdoctor identified the best candidates to focus on and, as a result, started ranking amongst the top three results globally within 6–8 weeks! Therefore, by understanding what the competitors were focused on and what they were not, it is possible for any startup to compete globally (across multiple languages and countries) and come out as the winner.

SEO Building Blocks

Most early-stage founders know that SEO is important but a lot of them are not able to leverage it properly. What can startups do to benefit from the SEO magic?

Ravi suggested that startups have to create the right culture and the right infrastructure in the company to ensure that SEO gets the focus it deserves.

Culture requires understanding the importance of SEO and talking about in senior management meetings and in the company townhall discussions. It also requires empowering a leader to focus on SEO growth and creating a dedicated team or squad focused on SEO. The SEO team should have clear targets and goals (along with dashboards that provide measurability) and the team should be rewarded for both mini-successes and for achieving major milestones. A lot of companies miss this and don’t have anyone responsible for day-to-day handling and growth of organic traffic.

Infrastructure corresponds to infrastructure around log management, A/B testing, etc. This is because SEO should be a proactive game; it should not be played in a reactive manner (reacting to competitors or Google penalty).

Ravi pointed out that Housing had created a separate micro-service for SEO to take care of all technical SEO requirements such as title / header / meta handling, page redirection, content interlinking, content spreading, etc. In other words, all major aspects of technical SEO were supported by a micro-service.

The right infrastructure empowered the developers and PMs to do any experiment they wanted to do in isolation, without impacting the main product roadmap. This enabled the team to run their experiments very quickly and, therefore, become more effective.

The SEO team itself can be part of the product, tech or marketing team; the important part is that (a) it should have SEO-focused developers and product managers and (b) the company should work to ensure that the team doesn’t face roadblocks while taking care of the SEO tasks.

Difference between paid marketing and SEO mindset

Paid marketing requires understanding of various platforms (such as Google, Facebook, YouTube, Instagram, Linkedin, etc.) and the related marketing tools as well as planning (for example, budget planning, allocating spends across different platforms, etc.). Also, one can get instant results (within a few minutes or hours) regarding paid marketing experiments. Unlike paid marketing, SEO requires longer time period to show results and, therefore, more patience. More importantly, organic marketing and SEO requires a different mindset. This mindset demands product DNA and product-first thinking.

Product DNA is important because, ultimately, Google rewards those websites that are loved by users. So product efficacy is the core of organic marketing and SEO. In other words, as far as the organic traffic growth is concerned, a good product wins in the long term.

To imbibe product DNA, one needs to adopt user-centric mindset. As a result, it is important for the product managers (those who are responsible for the overall product roadmap) to start thinking about SEO items as well.

Treat Google Bot as a User Persona

To avoid the conflicts and to ensure that the SEO does not get Cinderella treatment within the organization, Ravi said that we must treat Google Bot as a user persona!

Why is this important? Ravi said, “imagine who would be the most frequent visitor of your just-recently-launched site? It is going to be Google Bot!” Therefore, one needs to take care of the Google Bot by presenting it with the right site structure, making the site easy to crawl and index, ensuring fast response time with high download rates, etc. If Google Bot finds the site friendly, it would rank the site higher and, therefore, tell the whole world about it and generate a lot of referral traffic!

At the later stages, when the site starts generating good traffic (direct and otherwise) and the product / platform has evolved, then the Google Bot user persona should be considered along with other user personas. At this stage, high quality product (that serves the end-users’ needs) is the most important. If the product quality is high and the end users like the product, Google Bot persona also ends up “liking” the product — even if you do a few mistakes. In other words, one can naturally start treating Google as a channel at this stage and focus on prioritizing user-specific activities (instead of resorting to SEO hacks to rank higher in Google results).

Treating Google Bot as a user persona can ensure that the team views SEO empathetically and applies user-centric mindset to SEO activities as well. It becomes easier to align different features and to consider SEO activities as part of the product and tech roadmaps. As a result, product/tech roadmap conflicts get sorted out.

This, Ravi hoped, would be one of the key points takes away from the conversation.

August 6, 2020

SEO Magic: How Housing grew more than 5x and overtook Magicbricks and 99Acres in 18 months

Ravi Bhushan is the founder of early-stage Ed-tech company, Brightchamps. Before venturing on his own, he was the Chief Product and...

Read More
Masterclass
Marketing
May 30, 2020
Chargebee: 20x Growth in 2 Years Without a Robust Marketing Plan!

Vikram Bhaskaran is a distinguished marketer. The Sr Director of Marketing at Chargebee, he headed marketing in Freshworks and FusionCharts.

Dr. Ajay Sethi
7
min read
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Mosi-oa-Tunya (“the smoke that thunders”), Zambezi River (link)

Chargebee is one of the largest subscription management platforms in the world, driving revenue operations and billing for SaaS and subscription-based businesses.

At the start of the Covid-19 crisis, Chargebee’s traffic dropped by 14% in two weeks — something that most companies across the world also experienced:

However, Chargebee was able to recover quickly in the next two weeks:

In fact, not only did the traffic recover quickly, Chargebee was able to grow its metrics and, in the subsequent two weeks, ended by 20% higher than the pre-Covid levels:

How did Chargebee do this?

To understand the marketing gears behind their metrics, we spoke with Vikram Bhaskaran, who is the Senior Director of Marketing at Chargebee. He has been with Chargebee since 2018, during which time the company has grown rapidly. Chargebee’s leading marketing metrics in this time have grown a whopping 20X.

Vikram is one of the most experienced SaaS marketers in India, with more than 15 years of SaaS marketing experience. He has handled marketing at some of the most successful SaaS startups in India. He was with Zoho in 2006 (when Zoho was known as AdventNet) and with Freshworks in 2012, when Freshworks — then known as Freshdesk — had just raised its first round of funding. He has been involved in the SaaS space during these formative years and has contributed to the evolution of SaaS marketing in India.


Conventional thinking naturally drives marketing organizations to design progressively more robust strategies as they grow. Vikram adopted a contrarian view and formulated an “Adaptive Marketing Strategy” that is able to evolve rapidly with company needs and market dynamics.

As he describes it, a Robust Strategy is one that is able to sufficiently weather external changes. An Adaptive Strategy, on the other hand, is designed to capitalize on these changes and turn them into opportunities for growth.

Vikram shared that there are 4 elements of the Adaptive Marketing strategy:

  1. Buyer
  2. Channels
  3. Team
  4. Market environment

Any customer acquisition strategy, let alone adaptive marketing strategy, depends on the buyer. Therefore it is critically important to understand buyer’s needs and goals, and map out their aspirations.

Second, channels correspond to distribution. All post-PMF startups must identify at least one channel that can scale. As the channels hit their capacity, marketing teams need to evolve and discover newer channels.

Third, interestingly, adaptive marketing is built to work with the diversity that is inherent in marketing teams. Marketing teams have artists, data analysts, creators, executors, iterators and optimizers. This is a heterogeneous set of people and adaptive marketing leverages this diversity.

Finally, adaptive marketing needs to be fluid (“flow like water”) in order to be able to quickly respond to market dynamic changes — either induced by customers, competitors or external factors.

[1] Buyer: Understanding Needs and Aspirations

The Buyer Persona is the most important aspect of the Adaptive Marketing strategy. Constantly evolving insights about customer requirements enables the companies to react quickly to adjust channels, reorient teams and respond to changing market dynamics.

But how can a company define buyers precisely? In particular, Chargebee has more than 15,000 customers across various industries in 53 countries. These include businesses ranging from early-stage startups just launching their products, all the way to established companies with sophisticated finance and revenue functions.

Within a customer, Chargebee has a multitude of stakeholders: finance teams involved in receivables, reconciliation and reporting; billing teams that need to automate their invoicing operations; product teams that need to drive pricing decisions; sales teams that need to close increasingly sophisticated negotiations; developers that need to implement the platform; Business heads; and CEOs.

With so much diversity across customer segments, roles, and geographies, how does Chargebee define its target customer or the buyer persona?

For Chargebee, this came from deep customer research that involved talking to users and existing customers, studying the first interaction of prospects in sales calls, and analyzing market behavior from search trends. This blog describes Chargebee’s approach to Continuous Customer Development & the steps involved in detail.

Vikram gave a brief overview of how Chargebee is building a brand around “RevOps” (Revenue Operations). The RevOps idea evolved while Chargebee was developing the buyer persona. Chargebee started this exercise by listening to the sales call — especially the first call between the prospects and the sales team. Vikram said that it was insightful to listen to how potential customers described their problems and their pain points. This initial understanding was refined by subsequent 1:1 conversations with customers and prospects to understand what their day looked like and what were their typical workflows. This helped develop a deeper understanding about customers:

  1. JTBD: What were the “jobs” they were employing Chargebee to do? Chargebee found this to be “billing and subscription management”.
  2. Zero Moment of Truth (ZMOT): this refers to the discovery and awareness stage in the buying cycle: what did the customer do just before they became aware of the problem? What was the itch they wanted to scratch? For Chargebee this was an “operational inefficiency” that the customer felt.
  3. Non-functional Why’s: Why did the customer want to do this job? What was their underlying goal? Chargebee found this to be “revenue growth”.
  4. Underlying Aspirations: What were the aspirations of the people involved in this process? Chargebee found this to be the “desire to be seen as a champion; someone who knows how to navigate a rocket towards higher growth”.

The end result was the realization that irrespective of their actual roles, prospects came to Chargebee with the specific pain of solving inefficiencies in their revenue operations just as they saw a hockey-stick growth potential in their horizon.

Defining personas not only helped the marketing team fine-tune positioning and messaging, but also made it possible for the whole organization to build, sell, support and engage the right customers.

But more important — clarity about the target persona also had a wonderful side effect: happiness! The org-wide focus and clarity made it even more satisfying to plan and visualize the value they were adding.

[2] Channels: Defining Scalable Pathways

Three pillars of any marketing team are: research, creation, and distribution. And within this, distribution comes down to the mix of channels, and how well the team is able to leverage them.

Within the first 6 months of deploying the Adaptive Marketing Strategy, Chargebee saw a 3X growth in demand. And, as mentioned earlier, over the next 2 years, these leading metrics have grown over 20X.

Vikram broke Chargebee’s approach to channels into 2 frameworks: Discovering Channel Fitment and Assessing Channel Capacity.

Channel Fitment corresponds to evaluating whether a channel is the right one for acquiring the target personas. Channel Capacity lets the team evaluate the continued potential left to tap within the channel.

Channel fitment

As an essential platform required by every high-growth SaaS business, Chargebee has the advantage of playing in a market with huge inbound demand. As a result, channels like Organic content and Paid search, in addition to inbound category creation through branding and thought leadership have been the most scalable channels.

As the business grew, Chargebee scaled into additional channels to augment specific customer segments (like Account Based Marketing for Enterprises), while beefing up its community focus for early stage businesses with high growth potential.

What is the best way for a startup to explore a new channel? Vikram said that Chargebee uses what he calls the 3S Framework for this: Scout, Scope, and Snipe.

In this Scout Phase, Chargebee runs broad low-investment campaigns to test a new channel. These could be paid ads, content-based activity, etc. The idea is to identify a few successes that can be explored further, without expending significant budgets.

As specific channels & campaigns demonstrate potential, the Scope Phasefocuses on improving the quality of the results. In paid search, for example, this is done by launching specialized campaigns with narrower focus on just keywords or activities yield better results.

Finally, the Snipe Phase serves to turn the activity into a repeatable process. In the context of paid search, this involves setting negatives and having exact matches that can be scaled for predictable results.

These three phases help continuously drive new campaigns & channel possibilities, while still ensuring quality & ROI.

Channel capacity

In the context of Channel Capacity, Vikram referred to the “Law of Shitty Clickthroughs” proposed by Andrew Chen [link], which refers to the diminishing returns provided by a channel as volume scales up.

Even as the quality goes up, how does one make sure that the quality doesn’t get compromised? How can businesses pick channels with sufficient capacity so that they can defer the curse of shitty clickthroughs?

Chargebee uses the following Channel ROI Framework for this purpose:

Clearly, the goal is to find channels that are “Leaders” (i.e. provide high quality leads / customers at low cost) and to stay away from high cost but low quality quadrant. Vikram mentioned that Bleeders (such as low-cost paid ads) are interesting because they can provide volume at low cost. However, marketing teams need to closely monitor their Bleeders, and measure their downstream ROI in dollar terms. Finally, Truffles are likely the largest segment — composed of high quality campaigns that also require more effort and budget. Typically, Truffle campaigns have to constantly out-bid competitors, but they are good indicators of market size and our position in it.

For Chargebee, Account-based Marketing (ABM) has grown from a Bleeder into a Truffle channel, while Community-based initiatives shift between Bleeders and Leaders.

Focus on Channel Fitment and Channel Capacity allows marketing teams to quickly double down or back away from initiatives.

[3] Team: Driving Individual Ownership

In a fast-growth business, the focus of the marketing team continuously shifts from Volume, to Velocity, Predictability, and Variability.

Vikram pointed out the moving targets that a marketing team needs to solve for, as it evolves from “Robust” to “Adaptive”:

  1. Marketing teams need to satisfy demand growth. Marketing teams need to invest in channels to feed the pipeline required to drive sales.
  2. Marketing has to enable sales to consume pipeline effectively — through intelligence, automation, messaging and enablement.
  3. As one of the biggest “expense” centers in a fast-growth organization, marketing teams have the responsibility to provide the organization with predictable and sustainable growth.
  4. Marketing is responsible for increasing the size and quality of the pie — by brand building to drive awareness, market creation to increase the size of pie, and differentiation to craft perceptions.
  5. The marketing team is responsible for driving adoption, engagement and advocacy within existing customers, to create a virtuous cycle of referrals.

Note that the first three stages correspond to linear growth (with respect to the marketing spends). Building brand and developing an emotional connection with customers provides some amount of non-linearity, while investing in customer engagement unlocks network effects. (We have earlier talked about how companies that don’t have inherent “networking” play can also unlock network effects [link].)

Each of these activities, clearly, are quite different from each other. In order to succeed, marketing leaders should structure their teams around specific focus areas, with metrics and inter-locking contracts. Vikram (elsewhere) illustrates this with the following representative example:


Given the evolving or moving target for the overall marketing team and differing success metrics for various marketing sub-teams, what holds the marketing team together? It is the clarity about the buyer persona (what are customers’ needs and goals) and the organization’s needs and goals.

Business needs are often captured by the “north star metric”. For Chargebee, the core indicator of success is “Net Retention Rate”. This goal is shared by the whole company and helps Chargebee track the speed at which the customers are growing. It helps the marketing team to also evaluate the leads, MQLs, SQLs, etc. in a more consistent manner.

In order to be truly Adaptive, marketing teams need to decentralize strategy. Vikram explained how every marketer in Chargebee is empowered to drive strategy with minimal oversight. He explained 2 systems that the team uses to ensure they stay aligned without stepping on each other:

  1. The PEAR Commander’s Intent: Every marketing activity and function has to satisfy Predictability (fitness to purpose), Excellence (exceeding expectations), Accountability (one problem — one owner) & Reporting (operational rigor).
  2. Socratic Method: Individual marketers are empowered to make decisions by documenting answers to 4 questions: What is X worrying about? What would X do? Why is this the right thing to do? How would X want to track progress? X here could be their manager, head, the CEO or customer.

[4] Market environment: Staying tuned to Changes

The final and most critical aspect of the Adaptive Marketing strategy is the ability to not resist the constantly evolving market dynamics but to rather turn it into an advantage.

Vikram builds on Bruce Lee’s quote (Be like water) to describe how an Adaptive Strategy must flow freely without resisting change in order to stay flexible and evolve rapidly.

For this, Chargebee built a system of Indicators, Triggers, and Strategies. Chargebee takes the org-wide look at the Indicators on a weekly basis (while the marketing team tracks the Indicators on a daily basis). As indicators hit different thresholds, they trigger a specific strategy set that the team operates on.

The dashboard with Indicators, Triggers, and Strategies is shown below:

Chargebee has four top-level indicators:

  1. Top-of-the-funnel (ToFu) opportunities: what is happening at the ToFu level? For example, early in the lockdown cycle, Chargebee detected that the interest for webinars and learning courses had gone up. As a result, Chargebee organized almost 10 webinars within the first two weeks of the lockdown.
  2. Demand / search intent: for Chargebee, inbound traffic continues to be an important channel. To track it, Vikram uses the Key-5 Search Index — an index of their top 5 search pillars.
  3. Pregnant python: this indicator helps Chargebee to identify if there are bottlenecks in any part of the pipeline. Chargebee then either works to resolve the bottleneck or, if needed, to rethink strategy.
  4. Leaky bucket: Chargebee uses this as the frth indicator to track customer and revenue churn. Chargebee qualifies the churn as voluntary / involuntary and tracks churn across verticals / industries.

These Indicators have different impact on the company’s north-star metric: NRR (Net Revenue Retention). Aligned with NRR, Chargebee has a number of Triggers. For example: Estimated Bookings, Cost of Estimated Bookings, Estimated Payback Period, etc.

As these triggers flip, Chargebee’s marketing team already has the strategic priorities and initiatives in place to quickly shift directions.

Chargebee’s marketing team further structures all their initiatives into 7 categories ranging from Critical Necessities to Discretionary by just answering 3 questions:

Depending on the Yes or No answer for each question, there are eight different scenarios. Each scenario provides guidance for the strategy:

Along with the operational rigor, Indicators, Triggers, and Strategies provide the mechanism for Chargebee to respond quickly to the evolving market dynamics.

Closing comments

Vikram emphasized that scaling marketing teams should not waste their time building robust marketing strategies. Robust market strategies attempt to withstand external disturbances and resist changes, which makes them quickly lose touch with reality.

First, in a fast-growth business internal teams, processes and priorities change so rapidly that forcing robustness could isolate the marketing function from the rest of the organization.

Second, while the Covid situation is unprecedented, markets and environments change directions even otherwise in unpredictable ways. While robust strategies might provide immediate insulation from these turbulences, the incremental shifts in the environment render the strategy obsolete quickly.

Eventually these deep, well thought strategies end up living only on the paper (or spreadsheets) while the actual marketing plan gets changed ad-hoc. Teams lose sight of the bigger picture, stresses run high, and marketing leaders find themselves struggling to plug the leaks in their strategy.

Instead, Vikram suggested organizations to focus on fluidity instead — by designing their marketing strategies to be Adaptive.

However, an Adaptive approach is as much an organizational mindset as a strategy — in order to be truly adaptive, marketing teams should account for

  • Constant research & understanding of the Buyer Context
  • Continuous discovery & expansion of Channels
  • Flexibility & individual ownership within the Team
  • Systematic tracking & reprioritization based on the Environment.

Based on these, an Adaptive Marketing Strategy provides a natural way to keep the marketing plan flexible to identify new realities and capture opportunities. Adaptive Marketing, therefore, is the ideal way to build the marketing function at every company: from an early-stage startup to growth-stage startup and even large enterprises!

May 30, 2020

Chargebee: 20x Growth in 2 Years Without a Robust Marketing Plan!

Vikram Bhaskaran is a distinguished marketer. The Sr Director of Marketing at Chargebee, he headed marketing in Freshworks and FusionCharts.

Read More
Masterclass
Product Management
October 29, 2020
Driving 100 million users to adopt digital payments

With 22 years experience in building commerce, utility, gaming & financial products for consumers, Deepak has developed analytics & growth

Dr. Ajay Sethi
9
min read
Read More

PayTM: Growth Lessons from Driving 400M Indians to Adopt Digital Payments

Growth is the fuel that energizes startups to defy heavy odds. It sustains and drives startups in their quest to create value and to build successful businesses. But how should one navigate the growth journey? What are the levers that startups can use to drive and sustain both the quantity and the quality of growth? And what are the indicators that are the most useful in this journey?

In the startup world, growth is often discussed in terms of CAC (Customer Acquisition Cost), LTV (Life Time Value of a customer), UE (Unit Economics), and NSM (North Star Metric).

At a high-level, these terms are defined easily:

  • CAC is the cost of acquiring one additional customer, which can be calculated as (Sales Expense + Marketing Expense) / #new-customers.
  • LTV is the total revenue the company can make from an individual customer over the life its association with the company.
  • UE (Unit Economics) helps to calculate the “contribution margin” per customer; it corresponds to how much value each customer (“unit”) creates for the company. UE, therefore, helps to predict company’s revenue and profits. For the sake of simplicity, LTV to CAC ratio (i.e., LTV / CAC) is often used as a rough proxy for UE.
  • NSM (North Star Metric):  the metric that a startup uses to help it focus on growth.

However, the terms get complicated in reality.

For example:

  • CAC: what is the definition of “acquiring one additional customer”? Is it when customers download the app? Or, when they register or signup to start using the service? Or, when they perform the “core action” (which can be doing a transaction; or, it can be consuming the content or sending a message)? Or, when they become repeat / retained customers?
  • UE: should the UE be calculated at the aggregate level for the customers? Or, should UE be calculated at per-transaction level? Or, perhaps, per-product level? For example, if a customer uses company’s products for both payments and for buying gold, should one calculate UE for each transaction? Each product? Or, at the aggregate level for each customer?

In order to discuss how startups could work with these terms, we thought it would be good to discuss how one of the most highly valued startups in India looked at these.

PayTM is one of the most well known startups in India. It has more than 350M - 400M users and more than 50M Daily Active Users (DAUs). [link] In fact, if you exclude Google and Facebook properties, PayTM has the largest reach in India. Amongst Indian startups, PayTM (along with InMobi’s Glance) has the largest DAUs and MAUs [link]:

And to discuss growth at PayTM, there can be no better person that Deepak Abbot, who was the “Head of Growth” during his first stint with PayTM and the “SVP, Products” during the second stint. Recently, Deepak has ventured out to start on his own company.

In all, Deepak spent more than five years with PayTM. First stint was in 2012-14 – when PayTM was getting started as a mobile wallet company (from a web-centric company). It had less than 10M app downloads at that time. And the second stint was starting in 2016 – when PayTM was growing robustly. In 2017, PayTM crossed 100M downloads and grew to more than 400M downloads by 2019. [link]

As indicated above, CAC, LTV, UE and NSM can be calculated with different levels of sophistication; so, it is useful to discuss how an early-stage startup and a late-stage startup should look at them. Since Deepak was directly involved with PayTM’s growth during the early stage (2012 – 14 timeframe) and during the late stage (2016 onwards), he has the first-hand experience to share his experience with handling these growth metrics at different stages.

You can see the entire conversation here.

Or, alternatively, you can look at the following summary that is based on my conversation with Deepak.

CAC

When a company is getting started and the company wants many people to try out their app or website, it is good to keep the definition of CAC very simple. CAC can be

calculated based on app installs or based on unique website visitors.

For example, in the initial days, PayTM used to consider users who downloaded the app and performed some “core actions” as acquired users. In general, even if the users are not transacting but perform some core tasks, it is good to consider them as customers. This is because they generate data trails by using the product, which can help the startup to improve the product. At this stage Cost-per-Install (CPI) can be considered to be the CAC.

As the company grows, the definition of CAC should be gradually refined. CAC definition depends on the core focus of the company at a given time. From improving the product in the beginning, it evolves to improving value delivery; and subsequently to increasing usage and then to increasing revenue. So, CAC definition changes accordingly.

After a few hundred thousand users, PayTM started using “signed up” users for calculating CAC. For this, PayTM required that the users must have completed the onboarding flow by providing their email-id and doing mobile number verification. This helped PayTM to decipher that the users had the intent to use the product and provided PayTM with the ability to reach out to users, when needed. For PayTM, almost 90% people would complete the onboarding flow and register after downloading the app; as a result, CPI and the cost per registered user were not much different. As a result, PayTM moved to cost per registered user quickly. For the initial 18 months or so, PayTM used the cost per registered users for its organic marketing and (paid) digital marketing efforts.

As a payments company, PayTM’s core task was doing a transaction (i.e., making a payment using the PayTM wallet). As a result, PayTM graduated to using transacting users for calculating CAC within 6 – 7 months after launching the product. PayTM realized that if hundred users install the app, (let's say) 70 users typically registered; moreover, (say) 40% users would start transacting within five days (and do their first recharge). This made it possible to calculate the CAC for transacting users (“cost per transaction”; CPT). Based on this, PayTM started optimizing its campaigns to reduce CPT.

PayTM faced another problem in the initial days: most users didn’t want to use their debit cards or netbanking very frequently (for security / safety reasons). To alleviate these concerns, PayTM (and other companies such as Freecharge and Mobikwik) promoted the mobile wallet concept wherein users could load money once (into their wallet) and use it for recharges for their friends or family. Also, in order to increase the repeat usage, PayTM wanted users to load money for 3 – 4 weeks and do multiple recharges. In order to optimize for this, PayTM refined the CAC to correspond to the user who would load money into the wallet (and not the users who had done their first transaction). So, 6 – 7 months after the launch of PayTM wallet, PayTM’s CAC was focused on the “add money to wallet” core action.

After a year or two down the line, once the company had built multiple products and expanded its portfolio, it became possible for PayTM to cross-sell other products/services to users (such as top-ups, etc.). PayTM also added several merchants (such as Uber, Redbus, PVR Cinemas, Inox Cinemas, etc.) after getting the semi-closed loop wallet license (in 2014).

[link, link]

At this stage, PayTM started measuring CAC based on the retained users. For PayTM, this corresponded to users who were doing repeat transactions. This is because the first few transactions were incentivized but, subsequently, users needed to make their own decisions. PayTM was hopeful that users would continue using the product because they had liked the initial product experience. From 2019 onwards, PayTM started using cost per retained user for CAC.

This was also useful because as PayTM scaled, it became difficult to acquire more and more users. Therefore, to continue growing, it became important to retain users and to get them to perform multiple transactions. At this stage, PayTM also started focusing on internal marketing (to their existing user base; via in-app notifications, etc.) in order to not only inform them about various merchants (online and offline) where they could use PayTM as a payment instrument but also to get them to transact more frequently.

LTV

At an early-stage of the startup, LTV is difficult to calculate because startup doesn’t really know the retention duration and how many transactions (repeat usage) would users do during their lifetime with the product. Also, pricing (and take-rate, etc.) is fluid at this stage. Is such a scenario, how should early-stage startups work with LTV?

Deepak pointed out that PayTM didn’t calculate LTV for the first 2 – 3 years. PayTM was clear that it wanted to become a financial powerhouse (and not just a payments provider) in the long term. As a result, besides the initial phone recharge and bill payment services, PayTM had plans to offer other financial services. Therefore, it was clear that even if it takes time, various financial services would not only increase retention but would also result in higher ARPU.

In general, this is a right strategy for any category-creating company; it is better to focus on delivering value and increasing engagement touch-points during the initial phase. Monetization and LTV can be considered when customer clarity increases (with data about retention rates and the average lifetime duration of users) and the markets (around the new category) start taking shape.

PayTM started looking at LTV in 2014 – 2015 when the PayTM started supporting more usecases (and more merchants) as well as when PayTM started making money on payments (via use of mobile wallet across different merchants). By that time, PayTM had identified usage patterns and revenue potential from various types of transactions.

Unit Economics (UE)

At an early stage of a startup, it is clear that UE will be in the negative territory. PayTM initially focused on acquiring lots of transacting users without worrying about making money. This explains why PayTM started with recharges and bill payments, even though these services don’t provide much margin. For these services, PayTM focused on providing good user experience and building user habits so that, over time, PayTM could move towards positive UE territory (by cross selling and upselling various products and services).

For PayTM, UE was negative for 2 – 3 years for almost all the verticals it launched. Even then, it was important to be mindful about CAC (which, as discussed, was the cost to get users to start performing the core action; i.e., to start transacting) and to be aware of the levers available to turn UE positive at scale. As products started to mature – for example, prepaid and postpaid phone recharges, ticketing service, etc., PayTM focused on optimizing for UE.

Deepak suggested that companies should not attach too much importance to UE early in their journey because that can impact growth. However, even when a company goes after growth and defers worrying about UE (by deferring revenues), it is important to use UE-based thinking to maintain balance (and to cap the CAC and other costs related to acquiring and retaining transacting users).

In the initial 5 years (till 2017 or so), PayTM had set targets to ensure UE didn’t go too negative. Also, from 2018 onwards, PayTM started focusing on UE by focusing on optimizing costs. Deepak emphasized that, based on the PayTM experience, UE improvement should not rely too heavily on increasing margins. For example, PayTM’s margins from Telcos were the same when they were doing Rs. 10 crore per month worth of phone recharges as they were they started doing Rs. 5,000 crore per month of phone recharges. In fact, this is true across roughly 70 categories that PayTM has launched products for – for none of these categories, margins improved (because there were other external costs that didn’t go down).

Now, there are different ways to calculate UE. It can be done at the:

  1. Transaction level,
  2. Product level, which spans multiple transactions / interactions, or
  3. User (customer) level, which spans usage across several products.

In the early stage, PayTM used UE at the user (customer) level because payments is a high frequency (high repeat) activity and users are expected to do multiple transactions using the product.

By 2015, when PayTM had multiple products – especially some products in the more mature categories – PayTM started tracking UE at the product level (across several transactions via that product). And, subsequently, PayTM started tracking UE at the transaction level, which is the finest level of granularity. In the beginning, tracking UE at the transaction level is too harsh because the company could be incentivizing (initial) transactions and, if the user continues using the product, the UE would automatically improve.

By gradually looking at UE at finer and finer level of granularity, it becomes possible to track and improve UE at the company level – first in terms of contribution margin and then in terms of EBIDTA.

North Star Metrics (NSM)

Since CAC and LTV and, as a result, UE, are evolving indicators, are there any leading indicators that companies can use to track quantity and quality of growth? Are there some metrics that can be used to give direction to the company and to rally the whole team around the growth imperative? Also, how can the company figure out whether it is moving in the right direction or not?

PayTM has used the same NSM from 2012 to 2020! PayTM has used “number of unique transacting users” as the NSM both at the early stage and at the late stage!

The “unique” aspect of the North Star metric helped PayTM to focus on building a large base of users right from the start.

Also, “transacting users” aspect of the NSM helped PayTM focus on getting more and more users to transact. This automatically made PayTM focus on onboarding (as well as the D1 and D7 retention). It also helped PayTM focus on D30 (and longer-term) retention because it is impossible to signup a large number of new users every month. To do so, PayTM built multiple products and built various engagement hooks to retain users better.

This North Star Metric helped PayTM to grow to millions to transacting users because every product feature, every marketing campaign, every external communication, etc. was focused on driving transacting users. PayTM had different teams with micro-tasks or micro-targets that were aligned to the overall NSM.

In addition to the North Star metric, PayTM would select a different focus area (or “theme”) every year. Some of themes were: bring the CAC down, build a scalable or a secure architecture, increase revenues, etc. So every year PayTM focused on one additional parameter. More recently, PayTM is focused on increasing the revenues and reducing the costs, which would make the balance sheet healthy.

Conclusion

We can see how CAC, LTV, and UE are useful metrics for driving a startup’s growth. However, each of these terms need to be calculated with different levels of sophistication based on the startup’s stage. The terms should correspond to the core focus of the company at a given time. It is counterproductive to use onerous definitions of these terms earlier than necessary.

As the numbers above indicate [Feb 2020 data; link], PayTM was able to become one of the largest payments companies in India and then evolved into a full-fledged financial platform by judiciously refining CAC (Customer Acquisition Cost), LTV (Life Time Value), UE (Unit Economics), and NSM (North Star Metric) across different stages of their journey. Moreover, PayTM usage increased 3.5x during the Covid-19 pandemic despite PayTM discontinuing most of the cashbacks and incentives offered earlier. [link] This augurs well for PayTM’s vision of becoming a financial powerhouse in India.

October 29, 2020

Driving 100 million users to adopt digital payments

With 22 years experience in building commerce, utility, gaming & financial products for consumers, Deepak has developed analytics & growth

Read More
Frameworks
Product Management
May 22, 2020
Covid19 Crisis: Business Strategy Framework (Part 2)

Simple but comprehensive Crisis Response Framework that startups can use to respond to changing user requirements and expectations.

Dr. Ajay Sethi
7
min read
Read More

Business Strategy Framework

A crisis that profoundly impacts the prevalent outlook brings short-term and long-term user behavior changes along with it. We have found two parameters to be useful to understand and classify the behavior changes:

  1. Importance of an activity
  2. Frequency of an activity

Let’s consider the first parameter: importance of an activity. As an example, consider grooming services offered by UC (for example, waxing, threading, pedicure, manicure, spa, etc.). The importance of these activities has gone down after the lockdown not only because of fewer occasions to venture out of one’s house. On the other hand, consider the grocery or medicine delivery services – clearly, the importance of these activities have gone up after the lockdown.

The second parameter, frequency of an activity, refers to higher (or lower) frequency of an activity as a result of physical distancing and preference for lower human touch. For example: most tech companies use variants of daily standups and periodic meetings to ensure that everyone is one the same page and the blocking dependencies can be resolved. During the pandemic (with everyone working remotely), it has become more difficult to have impromptu and in-the-hallway mini discussions. As a result, the frequency of meetings about task progress and status updates has increased. On the other hand, customers are holding back on engaging in activities involved with buying a home or renting a new house because these are discretionary activities and users prefer to have a more stable future outlook before committing to these spends.

Visually, these changes can be represented as a 3x3 matrix shown below:

Impact Matrix

It is worth highlighting that, over time, importance of an activity gets translated into the demand for a solution for the activity. (In reality, frequency of an activity is also positively correlated with the demand; however, for the sake of simplicity, we will ignore this correlation. Of course, it should be taken this into account when you apply it to your own specific set of use-cases.) Therefore, concrete manifestation of increase (or decrease) of importance of an activity can be observed from the increase (or decrease) of online demand (and corresponding search traffic) for solutions corresponding to the activity.

Of course, there are activities that can have both higher demand (importance) and higher frequency. The most obvious example would be the increase in importance and frequency of PPE equipment and related health supply procurement. Likewise, demand (importance) for activities that keep young kids engaged and teach them something useful has skyrocketed in the last few months. Moreover, its usage amongst the people who were already using it has increased as well.

Given the changing personal goals and nature of daily routine of an individual (and companies), there are activities whose importance as well as demand has decreased. For example, group fitness classes (at a fitness club) as well as in-person yoga or fitness classes (at home) have gone down in importance as well as frequency.

Research had shown that it takes a minimum of 21 days for a new habit to take root. Moreover, when we look across a large category of habits, its takes approximately 66 days to build a new habit. When done repeatedly, an activity gets ingrained in the brain (and new neural pathways to get formed) in approximately two months. [link] Covid19, by either confining people to their homes or by severely restricting their movements for an extended period of time will give rise to new behaviors that will change the normal response patterns of the people across the world.

Impact Matrix

Based on these two parameters, a company (or, for multi-product companies, a specific activity/task supported by the company) would encounter one of the seven distinct scenarios shown earlier.

We refer to the above as the “Impact matrix”. Impact matrix highlights that companies that find themselves in different quadrants face different challenges. Based on this, companies (or products / features) can be classified into Green, Yellow, or Red zone, as shown below:

Impact Matrix

The companies in the Green zone are facing an overall positive impact and, therefore, have an opportunity to grow faster in the post-Covid world. The companies in the Red zone are facing an overall negative impact and, therefore, have the challenge to find avenues for continued growth (or, minimally, to avoid contraction). Finally, the companies in the Yellow zone are not heavily impacted by Covid19 crisis. However, even these companies have to constantly track the evolving user needs and goals to ensure that they can continue to serve their customers.

Response Matrix

So, how should companies respond to the changes in Importance and Frequency? Once a company identifies the increase or decrease in Importance and/or Frequency (for each activity and, therefore, for various products/features) for each distinct persona, the responses are fairly intuitive. This is because company’s goal would be to counter the changes in customer’s requirements and expectations. The proposed responses are shown below:

Response Matrix

We had provided seven concrete examples of business strategies in the Part 1 of the article [see here]. Those seven strategies correspond to seven different quadrants of the Response Matrix. Here’s a outline of how each of them corresponds to a thoughtful response to different types of impact of the Covid19 crisis:

  1. Decrease in Importance & Decrease in Frequency: if a company finds itself (or some of its products) in this tough situation, it is important to explore if there are alternative ways by which the company can serve its customers. CultFit’s focus on online video classes is a wonderful example of this strategy.
  2. Decrease in Importance: if an activity’s importance has reduced, it is important for the company to change its products, processes, positioning, etc. to reflect changed customer requirements and expectations. UC’s focus on process and product changes (to reduce human touch and to emphasize safety) is a great example of this strategy.
  3. Decrease in Frequency: introduce new products / features that help to drive engagement with users. Housing’s “Pay Rent” is a good example of this strategy.
  4. Increase in Frequency: if changed circumstances increase the frequency of usage, it is important to modify the product not only to support the higher usage imperative. It is also useful to explore how company can drive repeats further so that the company’s product gets close to the natural frequency of the usage. AgroStar’s focus on community-driven engagement is a beautiful example of this strategy.
  5. Increase in Importance: increase in importance is a great opportunity for a company to acquire new customers. Moreover, higher importance can help to improve the quality of customer acquisition: it should be possible to acquire large and higher LTV customers due to higher demand for company’s products. Blackbuck’s open marketplace experiment is an impressive example of this strategy.
  6. Increase in Importance & Increase in Frequency: when increase in importance (and, therefore, demand) is coupled with increase in frequency, it is important to seize the opportunity! Moglix’s international expansion to serve the needs to UK and European customers (starting with PPE, masks, and other health-related requirements) is a noteworthy example of this strategy.
  7. No changes: even if the company wasn’t positively or negatively impacted by Covid19 crisis, it is important to keep a close watch on user requirements (needs) and expectations (goals) and respond quickly to the evolving needs and goals. Rapid-fire product launches and enhancements done by Swiggy over the last two months are an excellent example of this strategy.

Why does the Response Matrix only include Scale, Habit (which we use loosely to refer to repeat usage as well as continued engagement), and Brand? What about other strategies to respond to the crisis? We have written about this earlier [here] but it is worth reiterating that there are only four mechanisms by which companies can create value. These four mechanisms are:

  • Scale (to refer to both supply-side and demand-side scale),
  • Habit (includes stickiness and retention for categories such as health & finance),
  • Brand (includes intangible assets such as patents, regulatory approvals, etc. — esp. for pharmaceuticals, finance, etc. categories), and
  • Network effects.

We have also discussed how Network effects can be unlocked via direct user involvement: if company can design their product / service such that it gets users directly involved in the Scale, Habit, or Brand related activities, it super-charges these three and provides ongoing compounding benefits. [here]

Given this, all business strategies will eventually boil down to one of these four value creation drivers. (If you have don’t agree with this and have examples that prove otherwise, please let us know in the comments section below!) The Response Matrix covers all the three primary value creation engines and, therefore, provides comprehensive business strategy guidance.

Summary

The Crisis Framework presented here are useful for companies to respond in most appropriate manner to the changes in the market dynamics due to Covid19 crisis. Impact Matrix is a good way to analyze the impact of the crisis is a granular manner. By analyzing the impact of Covid19 on each persona and for each user activity, the Impact Matrix can be used to evaluate and understand the impact in a granular manner. Subsequently, the Response Matrix can be used to respond to the changes in a thoughtful manner.

May 22, 2020

Covid19 Crisis: Business Strategy Framework (Part 2)

Simple but comprehensive Crisis Response Framework that startups can use to respond to changing user requirements and expectations.

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Frameworks
Customer Success
February 21, 2020
Spiral Effects or Economic Moats?

Warren Buffet of Berkshire Hathaway has helped popularize the concept of “economic moats” over the last 25 years Morningstar, an investment

Dr. Ajay Sethi
18
min read
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Warren Buffet of Berkshire Hathaway has helped popularize the concept of “economic moats” over the last 25 years. Morningstar, an investment research firm, was amongst the first to formalize and systematically leverage economic moats as an investment strategy (in mid 2000s). Based on their research, Morningstar identified five sources of economic moats (in descending order of their importance):

(1) Intangible assets (patents, brands, etc.),

(2) Sustainable cost advantage,

(3) Switching costs,

(4) Network effects,

(5) Efficient scale.

These economic moats mechanisms have been investigated and studied deeply from capital allocation perspective — esp. for investment into mature and late-stage companies. It has been shown that moats-based capital allocation strategy (esp. when combined for stock valuation) provides higher return on invested capital (RoIC).

Are economic moats (“defensive strategies”) relevant for the digital companies? Or, does the speed of innovation (“offensive strategies”) suffice? Not surprisingly, there are differences of opinions amongst the practitioners. This was brought out sharply by the altercation between Elon Musk and Warren Buffet after Musk referred to economic moats as “lame” and “like nice in a sort of quaint, vestigial way” during a Tesla’s quarterly earnings call [May 2018; link]. Musk asserted that “the pace of innovation” is a stronger predictor of long-term success because “if your only defense against invading armies is a moat, you will not last long”.

Not everyone agrees with Musk’s assertions. For example, analysis done by some Venture Capital firms (VCs) has indicated that digital firms do benefit from the defensibility provided by various mechanisms. Interestingly, it turns out that the digital firms also have the similar four mechanisms for defensibility: network effects, scale, brand, and lock-in; however, the order of importance of these four differs vis-à-vis non-digital firms.

In the first three decades of the Internet era, network effects were found to provide the best defensibility. For example, consider Visa and Mastercard, which provide a payment network for customers, merchants, card “issuers” (credit / debit cards issued to customers) and payment “acquirers” (payment devices provided to merchants to accept card payments). This is a lucrative business: Visa generated $23B in revenue on $8.8T total payment volume (TPV) in 2019 — which is 0.25% of the payment volume. Mastercard is roughly half the size of Visa but with similar service fee structure [link].

There have been several attempts to disrupt this network — for example, AT&T, T-Mobile, and Verizon tried to build a payment network along with the large mobile phone manufacturers such as Samsung, Motorola, HTC, LG, etc. (called Softcard; originally, Isis Mobile Wallet; link) starting 2010 using the NFC technology (along with a secure hardware component). It failed to disrupt Visa and Mastercard networks and was acquired by Google Wallet in 2015. (As an aside: Alipay and WePay have been able to disrupt Visa and Mastercard networks in China (with combined TPV of $36T, which is 2.5x of Visa and Mastercard combined global TPV; link). More impressively, UPI in India has also been able to disrupt these networks — both in terms of number of transactions and TPV — within 3 years of its launch [link].)

This can be seen observed from Google’s ability to defend its search engine dominance despite spirited effort by Microsoft Bing; or, Facebook’s ability to defend and grow its social network despite Google’s numerous efforts to build independent social networks. This can also be seen from the continued relevance of services such as Craigslist and Monster.com despite limited innovation and upgradation in their services.

What about other three defensibility mechanisms? There are several examples for each of these: examples of defensibility provided by scale would be companies such as Expedia and Booking.com; brand examples would be companies such as eBay and Flickr; and lock-in examples would be Sybase (part of SAP) and COBOL (more than 25,000 companies still use COBOL).

Though investors (especially public markets and late-stage investors) might desire defensibility, entrepreneurs crave for growth. This is because growth is the magic potion that energizes startups and drives them to innovate faster during their journey. Therefore, till startups achieve maturity, one can visualize that growth is more important than building moats for defensibility for startups and entrepreneurs.

Given this, the question becomes: what are good drivers for growth? How can startups create value faster? How can entrepreneurs identify the most suitable growth strategies for their startups?

Spiral Effects

Based on our experience with hundreds of category-creating and category-dominating companies across consumer, business, health, finance, etc. categories, we have found that there are four value creation drivers:

  • Scale (to refer to both supply-side and demand-side scale),
  • Habit (includes stickiness and retention for categories such as health & finance),
  • Brand (includes intangible assets such as patents, regulatory approvals, etc. — esp. for pharmaceuticals, finance, etc. categories), and
  • Network effects.

You would notice that these look almost identical to the economic moats! So, are we just playing around with words? No — when we look at these from value creation perspective (instead of defensibility lens), we must look at these from product-first perspective. In other words, we must ask: how can startup build relevant products that directly help drive scale, create habit, build brand, and unlock network effects?

Here’s the key insight: the first three growth drivers can be unlocked by adding products with distinct characteristics to the product mix:

  • Scale growth enablers require products/features that have high frequency of usage and low importance.
  • Habit growth enablers require products/features that have medium to high frequency of usage and low to medium importance of activity.
  • Brand growth enablers require products/features that have high importance and low frequency of usage.

We refer to this product-led growth enablers that help build Scale, Habit and Brand as “Spiral Effects”. Why Spiral Effects? Products with the right characteristics, once built, provide ongoing benefits to the company. Not only the value of these products increases as number of users/customers increase, but the products themselves can be improved as well. The additive nature of these product-led mechanisms is acknowledged by referring to them as Spiral Effects.

Spiral Effects are product-led growth enablers for scale, habit, and brand.

Spiral Effects are highly prevalent in nature, as can be seen below [link]:

The above spirals are known as Hemachandra-Fibonacci Spirals corresponding to eponymous number series. Hemachandra number series is additive as can be see from the first few numbers:

1, 1, 2, 3, 5, 8, 13, 21, 34, and so on.

After the initial two numbers, each number in the series is the sum of the previous two numbers. There are fascinating stories about the origins of these numbers in Ancient India and how Pingala, Varahamihira and Hemachandra — Indian mathematicians — used them to define rules of Sanskrit poetry, music, art, astronomy, etc. (and, thereby, providing mathematical foundations to art; one example of this can be seen from the use of “Golden Ratio” in design and arts even now) [link].

Coming back to Spiral Effects, these product-led engagement boosters are not targeted towards building moats; instead, these product-led boosters help companies to create value and benefit from the ongoing additive nature of the products.

Engagement Graph

How can companies build products that help them unlock Spiral Effects? Is there a framework that can be used by startups to explore and build these value creation engines in a systematic manner?

Towards this, we first define Engagement Graph that depends on two parameters that underlie the value creation drivers. We then use the Engagement Graph to outline how startups can build Spiral Effects in a systematic manner.

All activities done by people — whether in personal or work context — can be viewed from the perspective of the “Frequency of activity” and “Importance of activity”. Let’s start by defining the scale for “frequency of activity”. Tasks that correspond to daily (or a few times a week) use-cases are considered to have “high” frequency of activity; weekly (or a few times a month) use-cases have “medium” frequency of activity; all other use-cases have “low” frequency of activity. The scale for “importance of activity” can be defined likewise. Tasks that have large implication and, therefore, require consultation with other stakeholders (such as family members or corporate committees) can be classified to have “high” importance of activity; tasks that trigger users to diligently evaluate pros/cons amongst alternatives as “medium” importance; utility-like tasks that can be performed without much thought are “low” importance tasks.

Following diagram shows the Engagement Graph with the characteristics of the three Spiral Effects:

The figure above provides an indicator towards how startups can unlock Spiral Effects: products/features are more amenable to scale-based, habit-based, or brand-based defensibility mechanisms based on the characteristics of user activities they cater to. There are different zones corresponding to different activity characteristics; the figure above shows the Scale, Habit, and Brand zones.

Conversely, a company can scale faster, increase stickiness or strengthen brand by consciously building products/features that cater to user activities with the desired frequency and importance of activity characteristics. In other words, by adding products/features with the relevant characteristics to their product mix, companies can strengthen and sustain their growth using scale, habit, and brand value drivers.

Let’s consider a few examples to make this more concrete.

Let’s look at Amazon to look at the first two Spiral Effects. Amazon’s primary focus area (i.e., commerce) has a weekly or bi-weekly usage frequency. Typical purchases range from low to medium importance — from buying household goods to purchase of fashion (say, apparel) products. In other words, Amazon’s e-commerce product falls under the “Habit Zone”.

In strengthen its position in the Habit Zone, Amazon launched Amazon Prime in 2005 to address two primary concerns: delivery charges and the speed of delivery. Amazon Prime removed minimum basket size requirement and promised “two day shipping” — that is, any product order that is covered under Prime gets delivered within two business days. (Amazon Prime removed 2-day shipping promise in 2015 — approximately, 10 years after launching the program. However, most people still believe that Amazon Prime guarantees free 2-day shipping!) By reducing both mental and emotional effort associated with ordering online, Amazon was able to increase frequency of usage. The graph below highlights not only the growth of Amazon Prime but, more importantly, the fact that Amazon Prime customers spend more than twice as much as the non-Prime customers ($1400 versus $600 per year). Amazon Prime, therefore, is a great example of Habit Spiral Effects.

Amazon Prime Videos is a wonderful example of Scale Spiral Effects. Amazon Prime Video addresses a user activity that has daily usage frequency and has low importance. By making thousands of Prime Video available to users at zero cost, Prime Videos is able to acquire customers. Subsequently, customers can not only consume free videos but also see TV episodes and move on a pay-per-use basis. Moreover, customers can subscribe to more than 100 premium channels with Prime Video Channels.

Prime Video increases customer’s engagement with Amazon platform, which, inevitably, results in higher frequency of e-commerce purchases from Amazon stores. This is highlighted in the graph below: 6% of Prime customers make daily purchases; 18% of Prime customers purchase 2+ times per week; another 22% purchase once a week. In other words, 46% of Prime customers (as opposed to 13% of non-Prime customers) purchase at least once a week!

Another beautiful example of Habit Spiral Effects is the Zestimate tool/product launched by Zillow in 2006. Zestimate provides an estimate of the value of every house that is listed on the Zillow website. The goal is to enable users to assess not only how their own home is trending but also to provide voyeuristic pleasure of assessing the wealth of their friends and colleagues (based on the value of their properties).

Zestimate attracted more than 1 million visitors with the first three days of its launch. Subsequently, Zestimate helped Zillow grow its traffic to more than 200 million visitors per month. More than 80% of US houses have been viewed on Zestimate; in order words, Zestimate has helped Zillow attract users even if they are not actively looking to buy or sell a real-estate property. If we assume Zillow’s target audience to be around 200 million (out of the total population of approximately 350 million in USA), we can see that (on average) every person amongst the audience visits Zillow once a month.

It is interesting to note that Zestimate unlocks Brand Spiral Effects from homeowner’s perspective,. This is because home ownership is (clearly) a high importance activity from customer’s perspective. Towards this, Zestimate encourages homeowners to provide data about major upgrades and repairs to their homes so that Zestimate algorithm can compute the price more accurately. Homeowners have provided details about prices and upgrades for more than 80 million homes. Zestimate, therefore, has contributed in a significant way to build Zillow into the largest and the most well known real estate brand in the USA

As another example of Brand Spiral Effects, let’s look at AirBnB. AirBnB (short-from of AirBed & Breakfast) started off as an organized and better version of the traditional Bed & Breakfast lodging entities. The AirBnB brand took shape when the team productized the tourist’s desire to “travel like a human”. Towards this, they not only facilitated emotional connect between the hosts and guests (via rich host & guest profiles) but also worked to get hosts involved to provide personalized local experience to the guests. By enabling guests to get authentic local experience (instead of shallower and commercialized touristy experience), AirBnB leveraged the product itself to amplify the emotional connect between guests and the hosts. This was captured brilliantly in the company’s “Belong Anywhere” brand marketing campaign.

In this context, emotional design or emotion-aware design can be treated as an element of Brand Spiral Effects — products that reflect / complement customers’ emotional and non-functional needs are able to connect better with the customers. AirBnB has been a leading proponent of emotional design and supported “Wish List” feature to capture the aspirational aspect of the customers’ wants. Within four months of its launch, AirBnB reported that 45% of AirBnB users were engaging with it! A small A/B testing experiment reiterated and emphasized the importance of emotional design: changing Wish List icon from “star” to “heart” resulted in 30% increase in engagement! [link] Therefore, it is important to leverage products to establish emotional connect with customers (instead of limiting products only to functional aspects by focusing on features and tasks).

At this point (and using AirBnB’s successful “Belong Anywhere” campaign as an example), it is important to emphasize that efficient and sustainable brand marketing campaigns are often based on the Brand Spiral Effects. In other words, before running brand campaigns, it is important to ensure that the product (or product mix) supports high importance activity. Since brand marketing is a “linear” activity (reach / awareness increases in direct proportion to the spends), it is important to build non-linear boosters within the product so that the company can maximize the impact of the brand marketing spends. In the absence of Brand Spiral Effects, companies need to sustain brand-marketing campaigns to ensure that the brand recall doesn’t atrophy quickly. For example, Nike spent approximately $3.7 billion on advertising and promotion costs in 2019 while Coca-Cola spent $5.8 billion on global advertising and marketing in 2018! (How can Nike and Coco Cola build Brand Spiral Effects? This is an interesting question — but, for the sake of brevity, we defer exploring this right now.)

To summarize, companies can build different types of Spiral Effects in a systematic, efficient and sustainable way by building products/features with relevant frequency and importance characteristics. Spiral Effects are self-sustaining: once products with the right frequency and importance characteristics are built, they yield results whenever they are used. In other words, product-led approach helps to continuously support and grow the Spiral Effects.

Network Effects

We have talked about Scale, Habit, and Brand so far. What about Network Effects, which have helped create the most value in the Internet era?

Here’s an interesting insight we have uncovered: Network Effects are Spiral Effects with one important addition — direct user involvement. If product can get users directly involved in the Scale, Habit, or Brand Spiral Effects (i.e., in the products/features that correspond to these aspects of the product), it super-charges the three Spiral Effects! This provides the compounding benefit: not only the scale/habit/brand improve due to the use of Spiral Effects but the Spiral Effects themselves improve due to direct user involvement. As a result, Network Effects become stronger as a result of user growth. This is the lure and the strength of the network effects: they promise ever-improving product and customer experience!

Network Effect helps to increase the importance of frequent activities and/or helps to increase the frequency of medium importance activities. Figure above shows the “Network Effect Zone” in the Engagement Graph.

Direct users involvement in the three Spiral Effects results in three different kinds of network effects:

  • Direct user involvement in Scale Spiral Effects gives rise to “Viral Networks”
  • Direct user involvement in Habit Spiral Effects gives rise to “Exchange Networks”
  • Direct user involvement in Brand Spiral Effects gives rise to “Connected Networks”

We will look at them each of these in more depth subsequently; for the time being, we outline their main characteristics:

Viral Networks are built when current users invite new users to join the network. There are two types of viral networks: (1) acquisition-based viral loops and (2) engagement-based Viral Networks.

Exchange Networks are built when current users engage with each other to improve the experience for everyone. There are three types of Exchange Networks: (1) marketplaces & market networks, (2) platform-based networks (including metadata networks and SaaS-enable Marketplaces — SeMs), and (3) platforms with n-sided network effects (including content & data networks).

Connected networks are built when current users help to build and deepen emotional connect for everyone. There are three types of Connected Networks: (1) social & collaboration networks, (2) community-based networks, and (3) marketplaces with collaboration & same-side network effects.

Even in the absence of direct user involvement, weaker forms of Network Effects are possible. For example, users generate valuable metadata during the course of their engagement with products. This metadata (aggregated over current and past users) can be used to provide better experience to new users. For example, based on past buyer journeys, companies can improve their ability to attract customers, manage leads more efficiently, and to onboard them more effectively.

Network Effects that arise due to indirect involvement of users correspond to the weakest form of network effects and can be referred to as “Indirect Networks”.

Viral Networks, Exchange Networks, and Connected Networks are progressively stronger forms of Network Effects. This is because these Network Effects correspond to three levels of users’ direct involvement in the product. Across these three kinds of Network Effects, user involvement progressively becomes deeper — resulting in increasingly strong Network Effects.

Viral Network Effects

Dropbox grew rapidly due to Viral Network Effect that was based on getting current users involved to invite new users. Dropbox had a very effective two-sided referral program that augmented the inherent virality with additional referral incentives. If a user got a new user to signup, both benefited by getting additional free storage (25MB). In any case, all non-users received a URL that pointed to the files uploaded into Dropbox by the sender. Also, as additional users signed up to use Dropbox, the frequency of engagement increased — users were accessing Dropbox more often (and, therefore, had some elements of Exchange Network Effects).

Viral Network Effects helped Dropbox to quickly acquire more than 500 million users after launching the initial version of the product in 2008. This enabled Dropbox to generate $1B ARR within 8 years of its launch — at that time, it was the fastest SaaS company to hit $1B ARR. [link]

Exchange Network Effects

Google Waze, a navigation app, provides real-time traffic updates and directions to users (travelers) based on inputs provided by fellow travelers. Google Waze was visualized as an Exchange Network right from the inception.

Is it possible to add Exchange Networks on top of existing products? The answer is yes: by adding Habit Spiral Effects and getting users involved in them. For example, Intuit’s TurboTax consciously added community support to make it more interactive. Scott Cook, chairman and cofounder of Intuit, mentions: “With TurboTax, we’re getting customers to answer people’s tax questions. We’ve created the largest and best source of answers on taxes — if you go to Google and put in a tax question, the link at the top will often be our answer. This is tapping a newer habit from the digital age: participating in online communities.” [link]

Brand Network Effects

PinDuoDuo stands for “shop more together” [link, link, link]. PinDuoDuo launched e-commerce service in September 2015 in the competitive Chinese market to compete with market leaders such as Taobao (Alibaba’s China-focused e-commerce platform), JD.com (part of the Tencent group), Vip.com, etc. The markets had grown rapidly over the last 7+ years to become the largest in the world (at $600B GMV). As a result of hyper-growth over the last several years, the growth-rate of e-commerce was tampering down a bit (though still growing at 30–40% year-on-year rate).

Despite entering a competitive market, PinDuoDuo was able to become the 2nd largest e-commerce player in China within 3 years. It IPOed in Jul 2018 in the US markets with almost $24 billion valuation.

How was PinDuoDuo able to crack open the Chinese e-commerce market? How was it able to compete with Taobao and JD.com? This is because it layered in Viral Network Effects and Brand Network Effects natively in the user experience.

PinDuoDuo focused on making it easier for users to create a new group for purchasing specific items. PinDuoDuo encourages users to form “shopping teams” (a new group) by prepaying for the selected items; after this, they can send a link to invite their friends and family members and encourage them to participate in order to buy the products at a group-purchase price, which is much lower than the normal price. To enrich the shared shopping experience, PinDuoDuo’s product has added many elements of gamification to commerce; for example, users can play games with friends and family to win a shopping coupon. Users are also provided with discounts if they pay for a friend.

This mechanism also allowed PinDuoDuo to unlock powerful Viral Network Effects. PinDuoDuo incentivized social sharing via WeChat, which resulted in rapid adoption and wide reach of the platform. This is the reason why PinDuoDuo’s CAC is $2 (vis-à-vis $18 for Vip.com, $39 for JD.com, and $41 for Taobao). In fact, PinDuoDuo’s CAC has reduced from $5 to $3 to $2 as they grew from 110M active buyers to 240M to 340M [link].

Note that, unlike Groupon, PinDuoDuo prefers teams of people known to one other (instead of teaming up with strangers just to get discount prices). Since “tech-savvy” early-adopters were able to onboard their friends and family members (often in Tier 3 or lower cities), PinDuoDuo succeeded in onboarding a large number of users in Tier 3 and lower cities. Almost 57% of PinDuoDuo users are from Tier 3 or lower cities (compared to 44% for Taobao and 53% for JD.com).

PinDuoDuo also worked to add Habit Spiral Effects: there are limited-time offers and lucky draws that seek to get users to visit the app every day. Users can spin a wheel in order to win shopping coupons. Users are provided with cash rewards for checking-in daily.

Amazon has built strong Exchange Network Effects with the help of the user reviews and ratings platform. PinDuoDuo took the network effects to a different level by unlocking Viral and Brand network effects. Together, these mechanisms helped PinDuoDuo to unlock the elusive network effects in the e-commerce experience.

It is important to emphasize that, so far, it has been assumed that Network Effects are dependent on product’s category in the sense that a company can leverage Network Effects if and only if the category intrinsically is dependent on marketplace dynamics, community-based interactions, etc. By identifying different types of Network Effects and their dependence on different types of direct user involvement, we have explained how companies — even those that don’t have natural marketplace mechanics or social networking dynamics — can thoughtfully and systematically craft various types of Network Effects into their products/services. In other words, any company can overlay Network Effects (over their core products) to amplify value creation and to strengthen defensibility.

Summary

We have observed that the three Spiral Effects (Scale, Habit, and Brand) have distinct product characteristics and, therefore, can be unlocked by adding relevant features / products to the product mix. Spiral Effects not only enable startups to create more value in a sustainable way but also help to strengthen their defensibility.

We have shown that product-based Spiral Effects can be built by identifying relevant category-specific tasks (undertaken by target personas) with specific frequency and importance of engagement characteristics: high frequency and low importance tasks for the Scale Spiral Effects, medium frequency and medium importance tasks for the Habit Spiral Effects, and high importance and low/medium frequency tasks for the Brand Spiral Effects.

In addition, we have highlighted that Network Effects are variants of Spiral Effects that can be unlocked via direct users involvement. Product-led user involvement in the three Spiral Effects gives rise to three different types of explicit network effects. Direct user involvement helps convert Spiral Effects into Network Effects, wherein the product itself continually becomes more energized and stronger with growing number of users.

Entrepreneurs can use the framework to build the Spiral Effects and Network Effects in a structured way and, thereby, create more value and build sustainable competitive advantage in a systematic, efficient, and sustainable manner.

February 21, 2020

Spiral Effects or Economic Moats?

Warren Buffet of Berkshire Hathaway has helped popularize the concept of “economic moats” over the last 25 years Morningstar, an investment

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Frameworks
Design
February 18, 2020
Creative Innovation & Disruptive Innovation

Over the last 25 years (since the start of the Internet era, i.e.), entrepreneurs have created more value by innovating and building new...

Dr. Ajay Sethi
18
min read
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Introduction

Over the last 25 years (since the start of the Internet era, i.e.), entrepreneurs have created more value by innovating and building new solutions than by disrupting existing products/services. In this regard, Clayton Christensen’s “Disruptive Innovation” theory (introduced in 1995 — just before the start of the Internet era, coincidentally) needs a close examination and evaluated whether it is relevant for digital-first startups and companies.

This is how Christensen, Raynor, and McDonald explain their theory of Disruptive Innovation [link]:

“Disruption” describes a process whereby a smaller company with fewer resources is able to successfully challenge established incumbent businesses. Specifically, as incumbents focus on improving their products and services for their most demanding (and usually most profitable) customers, they exceed the needs of some segments and ignore the needs of others. Entrants that prove disruptive begin by successfully targeting those overlooked segments, gaining a foothold by delivering more-suitable functionality — frequently at a lower price. Incumbents, chasing higher profitability in more-demanding segments, tend not to respond vigorously. Entrants then move upmarket, delivering the performance that incumbents’ mainstream customers require, while preserving the advantages that drove their early success. When mainstream customers start adopting the entrants’ offerings in volume, disruption has occurred.

Christensen’s theory was highly influential because it provided a systematic way for startups to create value and for the incumbents to innovate in order to avoid getting disrupted. The theory also helped popularize concepts such as JTBD (Jobs To Be Done; JTBD helps to understand the needs of the overlooked segments), MVP (Minimum Viable Product; the light-weight product that has more-suitable functionality), etc.

Disruption-from-below was a necessity in the era that required high amount of upfront investment costs in order to build products and then required some more capital to build distribution networks. The examples used by Christensen reveal the scenarios that fitted this paradigm: for example, how mainframe computers were disrupted by mini-computers and subsequently by personal computers; how disk drive industry innovated and evolved; or, how the mini steel mills disrupted the larger steel mills; or, how Intel worked to avoid getting disrupted by developing lower-power Celeron chips; etc. [link]

However, in the Internet-first era, these are no longer valid concerns. Increasingly, the infrastructure needed to build the products is available with usage-based payment model that enables startups to avoid the upfront capital expenditure required earlier. With the availability of all layers of the infrastructure (from storage to compute; from database to middle-tier; from backend tools or frontend tools; myriad of online distribution channels; etc.), the costs of building and launching a product has drastically come down. Moreover, building offline distribution networks can be deferred (or avoided completely) due to the availability of plethora of online distribution channels.

For example, Google, famously, started with less than $1M investment from four angel investors ($200k by David Cheriton, $100k Andy Bechtolsheim, $250k by Ram Shriram, and $250k by Jeff Bezos). Facebook started with $500k initial investment from Peter Thiel. Both Google and Facebook, incidentally, started before the cloud infrastructure revolution had started.

Evolving customer requirements and expectations, shifting competitive patterns in an industry, technological breakthroughs, etc. help trigger innovations, often creating value by uncovering new opportunities. In this context, disruption-from-below is just a piece of the overall innovation and value creation jigsaw. Given this, there is a need for a new and comprehensive theory of innovation that provides a framework to the startups and companies looking to create value in the Internet era.

By analyzing innovation since the start of the Internet era, we propose a new theory that not only subsumes the scenario covered by Christensen, et al but also two new mechanisms that have driven much of the innovation in the Internet era.

Engagement Graph

All the activities done by people — whether in personal or work context — can be viewed from the perspective of the “Frequency of activity” and “Importance of activity”. Let’s start by defining the scale for “frequency of activity”. Tasks that correspond to daily (or a few times a week) use-cases are considered to have “high” frequency of activity; weekly (or a few times a month) use-cases have “medium” frequency of activity; all other use-cases have “low” frequency of activity. The scale for “importance of activity” can be defined likewise. Tasks that have large implication and, therefore, require consultation with other stakeholders (such as family members or corporate committees) can be classified to have “high” importance of activity; tasks that trigger users to diligently evaluate pros/cons amongst alternatives as “medium” importance; utility-like tasks that can be performed without much thought are “low” importance tasks.

Engagement Graph below shows various personal activities. First-mile and last-mile commute, food ordering, cab service are amongst the most frequent activities (with more than once-a-day frequency). E-commerce has approximately once-a-week frequency and lower importance. Education-related activities also have once-a-week frequency but higher importance while activities such as personal finances (including investments and lending), travel for leisure, real-estate transactions, and healthcare-related activities have much lower frequency but high importance.

Value Creation: Three Strategies

Let’s consider that an entrepreneur wants to build a new product in the Education category (or, the “EdTech” sector, as is popularly known today). Before getting started, one has to look at the incumbents in the market and their products. To make this concrete, let’s consider the largest EdTech company (as of Feb 2020) in India: Byju’s. Byju’s has raised $1.2B at $8B valuation. The valuation is based on more than $200M revenue that the company generated last year and, more impressively, the 3x revenue growth during last year. Byju’s has 40M registered users and approximately 3M paying customers. Last but not the least, it has reported 85% annual renewal rate.

So, how can a startup compete with this behemoth?

It can do so by identifying the target personas being addressed by Byju’s and by understanding the “frequency of activity” and “importance of activity” for each of these personas. Based on this analysis, the startup will be able to explore three options:

For the sake of illustration, these three different personas might benefit from services mentioned below:

And, indeed, there are companies that are building solutions along these three dimensions in the Indian market. These companies have demonstrated significant growth over the last few years and, therefore, shown that it is possible to create value even while competing with a highly-funded and fast-growing incumbent.

Innovation & Disruption

Based on this, we can divide the Engagement Graph into different zones, which we refer to as Innovation Zone 1 (Frequency-led Innovation), Innovation Zone 2 (Importance-led Innovation), and Disruption Zone.

Companies in Innovation Zone 1 create value by providing an easier and more convenient solution to a more frequent activity.

Companies in Innovation Zone 2 create value by providing a better solution to a problem and, thereby, increase the reliability and trust in the offerings. Better quality of product/service provided by these companies helps to match customer expectation in terms of functional needs as well as non-functional goals.

Companies in the Disruption Zone create value by providing a not-as-good solution to a problem at a lower price. These companies don’t offer cheaper or inferior products — they, instead, build products that offer higher “value for money” to the target personas.

Let’s look at each of these three Zones in more details.

Innovation Zone 1: Frequency-led Innovation

Companies that fall in Innovation Zone 1 have, inevitably, chosen a more frequent problem to solve. In order to do so, it was important for these companies to understand which needs of the target personas were not being met efficiently and effectively by current solutions. Innovation is centered on building a product that caters to the under-served frequent activities.

A more frequent problem demands a simpler solution — a lower-effort product that users can start using quickly and derive value almost instantaneously. It is often the case that these solutions are made possible by the increasing availability of new technologies at affordable costs.

Consider the example showcased above. In the urban mobility space, we can see that a lot of innovation has happened over the last two decades. Zipcar (following Mobility Cooperative’s footsteps in Europe) was amongst the first set of companies that attempted to tackle car sharing opportunity in the USA market. [link] Zipcar’s typical usecase was once- or twice-a-week car rental (where users paid hourly usage fee along with a membership fee). By offering a lower effort solution, Zipcar created a new market and, eventually, started disrupting the car rental companies (it was acquired by Avis for approximately $500 million in 2012).

Zipcar, however, was not suitable for frequent, short-distance commute in and around the central business district areas in the larger cities in the USA. This need was served by taxicabs that operated with permits that were artificially restricted (to limit the supply and to ensure that the prices remained high). Uber and Lyft tackled this problem by building a service that made it easier to book and get cabs (via an easy-to-use app that provided cabs within 5 minutes; moreover, users could track the assigned cab from their office instead of standing on the road side). Uber & Lyft assiduously worked to signup drivers and increase the cab supply in the beginning — which not only helped to reduce the waiting time but also helped to reduce the cab fares. By offering a lower effort solution, Uber and Lyft created new markets across the world and, eventually, started disrupting Zipcar and the car rental companies. Together, Uber and Lyft created more than $90 billion, based on IPOs market caps.

Typically, users take a few cab rides in a week. However, there is an even more frequent problem in urban mobility and this is related to the first-mile and last-mile commutes. Uber & Lyft are not suitable for this because of the five minutes wait time (and, to some extent, the price points of these services). Bird and Lime are tackling these even more frequent problems via dock-less bikes that can be picked up and dropped off at any location. Users typically take such rides a couple of times every day. As we can see, these companies innovated within the urban mobility space by tackling a more frequent problem. We have noticed that more frequent products inevitably disrupt less frequent products in the same category. In this regard, Uber’s investment in Lime and Ola’s investment in Vogo makes eminent sense.

Tackling a more frequent problem allows companies to even overcome strong network effects established by incumbents. For example, WhatsApp started its operations in Feb 2009 and raised $250,000 seed round of funding in October 2009 (and released WhatsApp 2.0 to iPhone App Store in Aug 2009). For the sake of reference, during the same time (i.e., from February 2009 to November 2009), Facebook grew from 175 million active users to 300 million active users (and to 350 million active users by end of 2009) [link].

Unlike Facebook, WhatsApp focused on a more frequent problem: short messages amongst a network of closely connected people. This resulted in WhatsApp being used more frequently than any other social networking or social communications app. As a result, WhatsApp is used more frequently than Facebook — almost 60% of WhatsApp users use the product more than once every day (as compared to 50% of Facebook users)!

By focusing on a more frequent problem, WhatsApp was able to beat Facebook at its own game: building a stronger social network while competing with a behemoth with incredibly strong network effects! This also helped WhatsApp to grow faster than every social networking and messaging apps (such as Facebook, Gmail, Twitter, and Skype) [link]:

Innovation Zone 2: Importance-led Innovation

Companies that fall in Innovation Zone 2 come up with a better solution to a problem. They compete with and beat the incumbents by offering superior end-to-end user experience that satisfies either the functional needs or non-functional goals of the under-served customer personas (or both). In order to do so, it is important for the companies to understand what is important to the target personas.

There are two possibilities: (1) the current products don’t fully or satisfactorily cater to the functional needs of the customers or (2) the current products don’t fully or satisfactorily cater to the non-functional goals of the customers. A startup can innovate by improving the products along either (or both) of these dimensions. In the first case, startups build products that offer better quality of service to customers; in the second case, startups build products that establish better emotional connect with customers.

An example of the first case is Urban Company — a company that provides consistent home and beauty services via managed marketplace in India and globally. Urban Company not only short-lists partners to work with very selectively but also trains them extensively to ensure that they are able to delivery better quality of service. In addition, Urban Company is constantly innovating to identify newer ways to measure the quality of service in order to ensure consistent quality of service. This innovation has helped Urban Company to create a new category and, in the process, disrupt the earlier market leader — JustDial — that provided a marketplace to match customers with relevant service providers. JustDial, itself, had earlier disrupted the “yellow-page” companies by enriching the listings with customer ratings and reviews as well as by verifying the service providers.

AirBnB is a great example of the second case: establishing better emotional connects with customers (in addition to building a better functional product). Initially, AirBnB (short-from of AirBed & Breakfast) started as an organized and better version of the traditional Bed & Breakfast lodging units. After the initial validation (partially by timing their launch to coincide with high demand periods and Craigslist growth hacks), AirBnB found it difficult to drive growth. AirBnB fixed it by focusing on activities that helped generate higher trust: better quality and consistent pictures of the properties (and paying for the professional photographers to achieve this) and by emphasizing the need for detailed host and guest profiles (which help to build trust amongst hosts and guests).

The next phase of evolution (leading to the “Belong Anywhere” brand campaign) happened when AirBnB focused on helping tourists “travel like a human” and to allow hosts to connect with the guest while providing personalized local experience to them. By enabling guests to get authentic local experience (instead of shallower and commercialized touristy experience), AirBnB increased the quality of solution offered to the customers. More importantly, AirBnB catered to the non-functional and emotional needs of the guests and the hosts to connect with each other as humans and to learn about different cultures and races. This was captured brilliantly in the company’s “Belong Anywhere” brand marketing campaign.

Both AirBnB and Urban Company, therefore, disrupted the incumbents by making the services more consistent, reliable and trustworthy as well as by enriching the experience provided to the customers. As we can see, these companies innovated within the hospitality and home services space by tackling a more important problem and by building better products.

February 18, 2020

Creative Innovation & Disruptive Innovation

Over the last 25 years (since the start of the Internet era, i.e.), entrepreneurs have created more value by innovating and building new...

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Case Studies
Product Management
May 22, 2020
Covid19 Crisis: Business Strategy Framework (Part 1)

Crisis Response Strategies from leading Indian Startups: AgroStar, Blackbuck, CultFit, Housing/PropTiger, Moglix, Swiggy, and Urban Company.

Dr. Ajay Sethi
10
min read
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Creativity Unleashed

For generations, people across the world will remember the havoc wrecked by the Covid19 crisis. Unlike the financial crisis of 2008 or the Twin Tower terrorist attacks of 2001, this crisis has not spared anyone. The homogeneity and simultaneity of the challenges faced by people due to lockdowns (or “shelter in place”) has created a shared consciousness that will have a profound impact on the humanity with across-the-board revision of outlook towards health and lives, livelihoods, social interactions, office and social spaces, travel, entertainment, spirituality, etc. – in essence, almost all aspects of life that are fundamental to human nature.

Several countries, including India, took proactive steps to contain Coronavirus spread and started imposing lockdowns by late March 2020. In a lot of countries, lockdowns continued for several weeks. (In India, lockdown was enforced from the last week of March till the first week of May 2020 – in other words, for more than 6 weeks.) During this time, people across the world had to radically change their daily routines. Also, even after the lockdown is lifted (or, perhaps, as we enter a “rolling lockdown” period), the practices of physical distancing, wearing masks in public spaces, avoiding enclosed crowded places (such as malls, movie theaters, etc.), etc. are likely to sustain – at least till the war against Covid19 is won.

While the societies reels under the constraints imposed by Covid19, entrepreneurs – who, in “normal” times go through a rollercoaster journey encompassing innumerable highs and lows of emotions – have had to handle a crisis for which they had never bargained for! However, the challenging times often bring out the best in people and we have observed the same with entrepreneurs. Accel India has a portfolio of 150+ companies and we have observed an extraordinary and exemplary spurt in creativity across the portfolio companies.

This creative energy has had an invigorating effect on the Accel India team and we felt that it is important to share these strategies with everyone because they can help startups across the world to gain a new perspective and, therefore, provide directional guidelines to handle the post-lockdown scenarios.

In the first part of the article, we will share seven concrete examples of different strategic responses to the crisis. In the second part of the article, we will provide an outline of a simple Crisis Response framework that can be used by entrepreneurs to pick the most relevant strategy for their companies.

Seven Successful Business Strategies

1. Reimagine the category

Since its launch in Nov 2016, CultFit had grown at a rapid pace to become the largest health and fitness brand in India. In Mar 2020, CultFit had more than 150 centers and  almost 80,000 users were working out in these centers on a daily basis. CultFit had grown 7x in the previous year and was on road to grow 4-5x in 2020 as well.

Covid19-led lockdown, however, completely disrupted the dynamics. Fitness centers had to be shut down resulting in the offline operations coming to a complete standstill. Revenues from the fitness business (which was around $10M per month) went down to zero overnight. Worse, it was not clear when CultFit would be able to go back to “business as usual” – there were concerns that Covid19-related side-effects would last for several quarters even after lockdowns were lifted. With offline operations grinding to a halt, CultFit, however, quickly pivoted to live video classes by adding them to its CultFit app. Earlier, CultFit app had only DIY fitness classes and played a minor supplemental role.

Live video classes that users could join from their homes turned out to be quite popular and attracted a large number of non-members as well. Within six weeks of the lockdown, CultFit live classes were clocking more than 500,000 daily active users and had served more than 5 million customers! In other words, CultFit grew 6x in the first 6 weeks of the lockdown.

Given the success of the video classes, CultFit is now exploring digital-first strategy more broadly and hopes to re-start the revenue engine!


2. Engagement & Retention: Drive Higher Usage and Retain Best Users

Housing (part of the Proptiger group that also manages Makaan.com) is amongst the largest real-estate sites in India. Real-estate industry has had to suffer a double-whammy due to Covid19 crisis: a lot of customers at the “top-of-the-funnel” (that is, those that were in the initial phases of their real-estate buying journey) have deferred their journey; also, customers at the “bottom-of-the-funnel” (that is, those that were close to making the final decision) are unable to visit the shortlisted properties and, as a result, are unable to make the final decision. Even otherwise, the uncertainty created by Covid19 has increased reluctance to make large commitments.

In such a scenario, the best thing a company can do is to keep the customer engaged by providing them with all the relevant information so that the customers can make the right decisions. Housing.com is doing this not only with the help of webinars (who isn’t?) but also by offering video-based consultations. Housing.com is also helping builders and brokers gear up to offer video-based virtual tours to simulate site visit experience (as opposed to static video walk-throughs, slideshows or 3D models of a property – which was done earlier).

In addition, Housing.com worked on adding support to enable tenants to “Pay Rent” via their credit cards. This helps people facing short-term liquidity issues during this Covid-19 pandemic. NoBroker, another real-estate firm, had earlier tied up with HDFC PayZapp to offer similar feature. Pay Rent not only provides additional 30 – 45 days of credit to customers but also reduces the dependence of cash and other physical payment instruments. Moreover, by providing ongoing service to customers, it helps these companies to stay engaged with customers.

3. Strengthen Brand: Alleviate Concerns & Deepen Connect

Urban Company (UC) is a managed marketplace that provides beauty-related and home-related services to customers. Beauty services (such as salon, grooming, spa as well as fitness and yoga) have been the mainstay of the company. Home related services include repairs of appliances as well as cleaning, painting, etc.

UC was handling more than 50,000 service orders per day in Mar 2020. India-wide lockdown brought this down to zero. Even worse, the quick spread and extreme virality of the disease ruptured user confidence in any contact with the outside world – whether it was in the form of newspaper, paper money, parcels, or people.

Recognizing the dramatic change in health and safety outlook, Urban Company launched “Mission Shakti” in mid-April to “protect the health, safety and well-being of its customers, service partners & employees.” UC provided masks, gloves, eye goggles, sanitizers, etc. to all service partners in order to protect themselves. UC also provided health insurance and income protection program to all service partners.

UC recognized that in the new world, users will have a difficult choice between going to a salon for haircut (with potentially large set of unknown people) or calling a “stranger” (service partner) home. The latter option has lesser variables – especially if the UC service can be made as safe as possible. UC has introduced new Standard Operating Procedures (that includes sanitizing tools and using single-use sachets and disposables) as well as providing contactless service in categories such as repairs, cleaning, etc. UC also introduced services such as sanitation and disinfection to cater to customer requirements.

All these safety-related process changes were appreciated by the service partners and were reflected in a dramatic 40% increase in the partner NPS scores since the start of the crisis!


4. Rapid Experiments: Match Evolving User Needs and Goals

Swiggy reacted very quickly during the Covid19 crisis. As the Covid19 concerns were increasing (before the lockdown, that is), Swiggy introduced "zero contact deliveries". Later, to help customers order from safer restaurants, Swiggy added “Best Safety Standards” badge to restaurants that have introduction additional safety measures to minimize the spread of the disease – these include temperature checks, frequent sanitation, self packing mechanisms, etc.

Second, Swiggy had grocery service support on its platform for more than a year. To match increased customer requirements, grocery services were scaled up rapidly after the pandemic spread. It was also expanded to serve more than 125 cities across India.

Third, Another service that existed before the pandemic was Swiggy Go, which provided instant pick-up and drop service to users. It was renamed to Swiggy Genie and expanded across more than 15 cities to enable family members, friends, etc. to send necessary items to each other without having to step out of their homes.

Fourth, Swiggy Stores service was introduced to help users buy groceries and other essential items from the nearby stores. Swiggy also partnered with several FMCG brands and retailers such as Adani Wilmers, Cipla, Dabur, HUL, Godrej, Marico, Nivea, Procter & Gamble (P&G), Vishal Mega Mart, etc. to supply food items and branded essential products direct to consumers.

Finally, Swiggy started to explore meal-kit delivery service. Swiggy introduced DIY (Do It Yourself) meal kits that let users to order ingredients of a specific dish from well-known partner restaurants so that users can cook the meal themselves at home.

Swiggy’s “customer-backwards thinking” has helped it to launch these services in rapid-fire manner. Swiggy uses a number of interesting frameworks (such as “Accepted Customer Beliefs”) to quickly understand evolving customer needs and wants (goals) and an efficient Growth Team Architecture to build and roll out these products rapidly. You can read more about these here.


5. Build Habit: Increase repeat usage

AgroStar is one of the largest agri-tech companies that offers complete range of agri solutions to the farmers. AgroStar is also the largest online farming community (known as “Krishi Charcha”) that provides crop-related and other inputs to farmers. AgroStar provides a combination of agronomy advice coupled with service and agri input products that enable farmers to significantly improve their productivity and income.

The e-commerce aspect of AgroStar had to get suspended in the initial days of the lockdown. In order to continue to deliver value to customers, AgroStar focused on improving community engagement. For this, AgroStar improved both the onboarding and increased customer engagement touch-points. These not only improved customer engagement and retention by 2x (in 6 weeks) but also helped increase number of e-commerce transactions subsequently!

Increasing the number of repeats and engagement helped deliver upfront value to customers, which helped establish trust with the customers. Higher trust, in turn, resulted in more than 2x growth in the transaction funnel. This was all the more impressive because AgroStar had significantly reduced its marketing budget during this period!


6. Scale Faster: Improve Quantity and/or Quality of Acquisition

While the coronavirus pandemic has hit most sectors, logistics is amongst the hardest hit because “essential items” (corresponding to agricultural produce, etc. for example) constitute only 15 – 20% of the whole market. The crisis brought almost all the 4 million trucks and the whole industry, literally and figuratively, to a standstill.

In order to get the industry moving again, Blackbuck launched “Move India” initiative that had two main elements [link]:

  • First, Blackbuck waived off the commission fees in order to enable any manufacturer or trader (in addition to the 30,000 customers that it already works with) to find the trucks that they need. This has become important due to non-availability of trucks – especially because a lot of truck drivers are reluctant to get back on the road (both to avoid contracting the virus and because petrol pumps, eateries (dhabas), etc. are mostly closed).
  • Second, Blackbuck worked to support the supply-side partners (which include includes five lakh fleet owners with ten lakh trucks) to discover demand and procure FASTags (for toll payment), fuel cards, etc. Blackbuck has waived off fee for supply-side players as well. In addition, BlackBuck is offering an added incentive of Rs 2,000 to Rs 3,000 to truckers for every trip they undertake as well as Rs. 50,000 trip insurance that covers hospitalization expenses either due to accident or Covid-related treatment.

Blackbuck, in other words, opened up its marketplace to both demand-side and supply-side participants. The open marketplace helped drive more than 120,000 matches to be made within the first three weeks of its launch and helped get more than 10,000 truckers back on the roads!

7. Unlock new opportunities!

Moglix is a B2B e-commerce marketplace that helps manufacturers and other businesses purchase ongoing supplies related to maintenance, repair, and operations of their factories and workplaces. Moglix is leveraging technology to improve the B2B supply chain.

The Covid19 pandemic has seen Indian textiles industry move quickly move to start producing Protective Personal Equipment (PPE) kits. At the start of the pandemic, India was manufacturing no PPE kits that were suitable for Covid19 and all the needs were being met by imports. In subsequent two months, India had 400 accredited manufacturers who were producing 300,000 kits per day! And the manufacturing capacity was projected to double over the next 6 weeks. [link] Globally, however, there is major shortage of PPEs. After achieving self-reliance by mid-May, Indian apparel exporters were ready to serve the global demand. [link]

Despite the disruptions introduced by the lockdown, Moglix actively worked to ensure that the manufacturers and customers (State governments, hospitals, etc.) were able to discover and transact with each other using Moglix’s online e-commerce platform. Moglix also worked to ensure that the consignments reached the customers.

Given that there is a global demand for PPEs and the realignment of the global supply chain, Moglix launched its operations in UK and European markets. Despite the constraints imposed by the pandemic, Moglix has, in fact, been able to satisfy the needs of several countries as well!

Summary

We have provided seven examples of distinct responses to the Covid19 crisis across a wide spectrum of B2C and B2B companies. There are countless more examples across the world. We will love to hear from you: please let us know other examples of companies that have responded quickly to the Covid19 crisis!

In the Part 2 of the article [see here], we provide the framework that helps to understand the “why” behind these responses and how you can apply them to your startup or company.

May 22, 2020

Covid19 Crisis: Business Strategy Framework (Part 1)

Crisis Response Strategies from leading Indian Startups: AgroStar, Blackbuck, CultFit, Housing/PropTiger, Moglix, Swiggy, and Urban Company.

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