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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
&
Deepak Abbot
9
min read
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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]:

India's Digital Destinations: Where are Indians going every day?
India's Digital Destinations: Where are Indians going every day?

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
Masterclass
Product Management
October 1, 2020
BigBasket: Defying the Odds to Grow Beyond $1B GMV Sustainably

One of the hottest companies before the Dotcom bust of 2000 was Webvan. It raised $800 million from Sequoia, SoftBank and several other...

Tejas Vyas
&
Dr. Ajay Sethi
8
min read
Read More

BigBasket Hits $1 Billion GMV Mark,

Delivers 3.5 Lakh Orders Daily

Context

One of the hottest companies before the Dotcom bust of 2000 was Webvan. It raised $800 million from Sequoia, SoftBank and several other Silicon Valley investors. Webvan had a successful IPO at $1.2B valuation in 1999 (as compared to Amazon’s IPO of $440M in 1994). Webvan was the original “Blitzscaling” company – it expanded to 26 cities across US without waiting for the business model to get validated. Webvan was touted to be the shining example of the then-unfolding Internet era. Unfortunately, Webvan’s collapse was as spectacular as its rise; it failed within a few months of going public!

Webvan was building an inventory-led online grocery business. Webvan’s failure pointed out that inventory-led grocery business (with perishable inventory and razor-thin profit margins) is hard to build. Webvan’s failure left a huge scar in the psyche of the entrepreneurs and investors in the Silicon Valley – such a big scar that no online grocery startup was built for the next 10 years! Even when the entrepreneurs started exploring this space again (such as Instacart, which started in June 2012), it was with inventory-light model – not the inventory-led model that Webvan had visualized.

Bigbasket was one of the first companies to defy this conventional wisdom and showed how to build a sustainable inventory-led grocery business. BigBasket started in December 2011 and was one of the earliest companies (across the globe) to unlock the magic formula to leverage inventory-led model to deliver great value and experience to users.

Incidentally, Amazon did start Amazon Fresh in 2007 – but it was an experimental launch that was limited to Seattle (and users had to pay $4 per delivery). Amazon Fresh expanded beyond Seattle after six years – in 2013! Users had to pay $299 for Prime Fresh membership – just to get the privilege of ordering groceries online. Amazon Fresh slowly expanded to other cities over the next 3 years (in US and Europe). Even then, users had to pay $15 / mo (over-and-above $99 for Prime membership). If someone orders groceries every week, this turns out to be $4 per order of delivery fee – more or less the same price that was validated in Seattle.

To find out how BigBasket succeeded in building an inventory-led grocery business, we had the privilege to talk to Tejas Vyas, who, incidentally, was the earliest technology employee at BigBasket. Over the last several years, Tejas has worn many hats. In the beginning, he was the lead engineer / architect; subsequently, he moved to the Product side for the entire back-end system (which played a crucial role in BigBasket’s growth). More recently, he heads the Product and Design at Bigbasket.

BigBasket journey

Tejas mentioned that BigBasket started with the “mission statement” of being one of the top 3 grocery players in India (not online grocery business – overall grocery business; though online was clearly the initial / preferred acquisition channel). From this mission statement, BigBasket identified two focus areas:

1. Build trust by providing superior customer experience to users and, based on that, become one of the top brands that customers love.
2. Grow sustainably and economically so that BigBasket can build a profitable business at scale.

Tejas pointed out that the mission statement and focus areas have helped BigBasket navigate through different phases on the journey so far. Tejas divided the journey into 3 phases:

· 0 – 1 phase: Tejas referred to this phase as the “Market-Product Fit” phase (as opposed to “Product Market Fit” phase) because it is important to understand the market before defining the product. Deeply understanding the market (i.e., the target users) and building/adapting the products based on their needs helps to optimize on time, money, resources, etc.
· 1 – 10 phase: the stablilization phase, where the startup onboards more and more customers while building products / features to cater to more use cases.
· 10 – 100 phase: the scale phase, where that startup broadens its offerings to cater to the needs of the “masses”.

Each phase has different requirements and expectations as well as different variables. To navigate through these phases, BigBasket uses the WWWH Framework.

WWWH Framework

The WWWH stands for the Who, the Why, the What and the How.

Who corresponds to the users and the stakeholders of the platform. BigBasket uses this to define and understand the target segment and its needs.

Why corresponds to deeply undersanding user needs and wants. This is an ongoing exercise because user needs and wants are not static. They keep evolving. Moreover, as a company expands its offerings to more user segments (personas), the needs and wants of new sets of users needs to be understood as well.

What corresponds to identifying the correct problems to solve. This, in turn, needs the startup to prioritize effectively. This is need to avoid going down the rabbit hole of endless engineering tasks.

How corresponds to various mechanisms and solutions by which one can convert the possibilities into reality. These include user research, Learn-Build-Measure-Iterate model, quick experimentation, etc.

Tejas provided an outline of each of these  during the three phases of BigBasket’s journey.

[Who] Users

In the 0 – 1 phase, BigBasket started off by targeting early adopters who were looking for convenience. This also matched the profile of users who were open to buy groceries online in 2012. In the 1 – 10 and 10 – 100 phases, BigBasket expanded to cater to masses as well.

To draw an analogy, BigBasket’s initial customers were similar to Nature’s Basket customers. However, as the company scaled, they started drawing in Big Bazaar customers as well. However, Tejas emphasized that, in reality, BigBasket is somewhat in between Nature’s Basket and Big Bazaar and, really, has created a completely new segment which is a mix of broad range and superior customer experience of Nature’s Basket and value-for-money offered by Big Bazaar.

[Who] Farmers and the “Spinach”

Besides end-users, BigBasket has one more set of important stakeholder: the farmers. BigBasket has been very conscientious about farmer’s needs and wants as well.

To highlight this, Tejas spoke about Spinach’s Journey. (We often hear about customer’s journey; the understand the operational complexity involved in the backend supply-chain operations, it is good to view the journey of fresh produce as well.)

Before BigBasket, spinach used to take 3-4 days from the time of harvesting to landing on consumer’s plate. The steps involved were: farmer harvests from the field and goes to a neighbourhood village where the produce from different farmers gets aggregated. It is then transported to the nearest APMC market, which potentially takes half a day or so. At the APMC market, there's an auction, which typically takes a day or so. After being bought there, the spinach is shipped to the local city hub (and each city has two or three such hubs). From there, it is bought, transported and sold to grocery stores and other retail sellers. In all, spinach’s journey used to take 3 – 4 days before it could be consumed.

BigBasket deeply analyzed and understood spinach’s journey and worked to make it more efficient. With the help of an integrated supply chain platform, BigBasket informs the farmers about the expected demand one night before the harvest. Based on this, farmers are able to harvest the right amount of spinach in the morning (say, at 6am) and hand over the harvested spinach to the collection centers (which are located close to the villages) by mid-morning (say, by 10am). From the collection centers, spinach is transported to BigBasket warehouses which are at the outskirts of a city. Typically, these warehouses are within 50 – 100 kilometers from the collection centers. From the warehouses, spinach is shipped to the small warehouses (or local spokes) within the city. Spinach reaches these warehouses by 4pm or 5pm. The delivery agents are then able to pick up from spinach from these warehouses and deliver to the customers by (say) 8pm. And then, if someone decides to cook spinach the same day (or next day morning), it is possible to consume fresh spinach within 24 – 30 hours of it getting harvest from the fields!

On top of this intricate backend supply chain system, BigBasket has been able to build a service that is able to deliver unmatched quality of fresh fruits and vegetables to consumers. This is not only good for consumers but it is good for farmers as well: they are able to monetize their produce more effectively and to get the best yield from their farms. Finally, this is good for BigBasket as well: fresh produce has very short shelf life and wastage of the produce (due to rotting, spoiling, etc.) has been the bane of the grocery business resulting in razor-thin margins.

[Why] User needs and wants

Tejas emphasized the need to separate “needs” from “wants” (pain-killers versus vitamins, in the typical startup parlance). In the initial days, BigBasket’s target users were looking for the convenience of getting fresh groceries delivered to their homes. BigBasket focused on the “needs” in the beginning (and deferred working on their “wants”).

Tejas pointed out that user needs and wants are not static; they keep evolving. Moreover, as BigBasket expanded its offerings to new set of users, their needs and wants had to be handled. In other words, it is important to recognize that the “Market Product Fit” also keeps evolving; as a result, startups need to constantly evolve the product / service to stay on top of evolving user needs and wants.

In the 0 – 1 phase, early adopters were more forgiving about the freshness and the price competitiveness of the “spinach” (in general, fresh produce) because they indexed more on convenience. But the 1 – 10 phaseusers were more demanding in terms of the quality of the spinach – which required BigBasket to evolve and improve its supply chain systems. In other words, clarity of user needs and wants provides right direction to the company in the initial stages.

In the 1 – 10 phase, understanding user needs and wants is necessary to prioritize tasks. But this requires understanding user needs and wants at a deeper level because often several seemingly unrelated requirements have a common underlying root cause. It is important for product managers and organisations to spend time to uncover the deeper triggers and to define the requirements in a simple and clear terms based on the Why. This helps to understand the issues / problems better and to resolve the issues better (instead of patch-fixing the problems).

In the 1 – 10 phase and 10 – 100 phase, it is necessary to start focusing on “wants” as well. For example, BigBasket realized that users want organic spinach. Also, they want green packaging and traceability of products.

When the loyal customers express their requirements and expectations, it is important to consider them because retaining loyal users is very important. Even then, it is necessary to understand the importance of this “want” and whether it has become a “need” for the loyal customers. If the market has evolved (and the original needs are now taken for granted), it is important to build features that cater to these wants. This, once again, reiterates the need to understand the market in terms of the needs and the wants of the rapidly evolving users. Catering to these “wants” can provide a differentiator to a growth-stage startup.

[What] Prioritisation

In the 0 – 1 phase, BigBasket initially focused on building a fast and efficient supply chain and making sure that the fresh spinach can be delivered at the right price with the right experience. In order to focus on this, BigBasket de-prioritized the UI / UX of the website. The website was kept simple with focus on ease-of-use (and basic features such as product details with image and price, add to cart, and checkout page). There were no bells and whistles. Besides supply chain, the focus was on grocery delivery experience. For example, delivery partners were empowered to accept returns immediately if the customer was unhappy about the freshness of the vegetables. This helped BigBasket achieve the delight that customer’s best neighbourhood grocery store provides.

In the 1 – 10 phase, BigBasket continued to focus on the holistics “customer experience” instead of the digital-only “user experience”. For example, BigBasket built a simple, no-frills app that followed the same philosophy as the website. Instead, BigBasket continued to invest more on adding a lot more automation and sophistication in its backend systems to provide superior farm-to-fork experience. This helped BigBasket to expand its product catalog to approximately 20,000 SKUs (from 2,000 SKUs in the 0 – 1 phase, which is the kind of variety a typical small supermarket grocery store offers). It also helped BigBasket to focus on delivering fantastic experience to users (such as 90 minute delivery) and to ensure that customers were happy with BigBasket.

How did BigBasket make the right calls during these phases? For this, BigBasket used the following prioritization framework to prioritize objectively but ruthlessly.

The prioritization framework depends on two parameters:

· Impact of the problem.
· Effort required for the solution.

First, every “problem” is rated in terms of its impact. Impact, in turn, depends on the customer impact and the business impact. Impact is rated with a score between 1 and 10 (10 being the highest). Also, by incorporating business impact, the framework takes care of business requirements. If the impact is unknown, the impact can be marked as “Experimental”.

One can visualize that the weightage associated with business and customer impact can be adjusted based on the stage of the company and the vertical/segment that it operates in. In the grocery business, for example, it is critically important to give high weightage to business impact because it is a low margin business (and, as a result, all tasks need to have a bottom-line impact).

Second, every “solution” is rated in terms of its effort. Effort is estimated more broadly – in terms of S, M, L, XL. One can also look at the bandwidth availability of different teams for this; for example, if a task requires a module that depends on a engineering sub-team that is overloaded, then the Effort can be marked as high.

This exercise can be made tighter by tagging each problem-solution pair via relative rating (so that not all problems are marked with “10” impact; likewise for effort). If this exercise is done for all features in an objective and fair manner, it can help quantify the ROI for all the possible tasks/features and, therefore, helps to pick the right tasks to perform.

Another option is to classify the tasks into four quadrants defined by Impact x Effort parameters (by classifying Impact as High or Low; likewise, Effort can be classified as High or Low). Based on this, the four quadrants are:

· High Impact, Low Effort: these tasks should be done first (e.g., features built and shipped fastest); they correspond to low-hanging fruits that can give huge wins quickly.
· High Impact, High Effort: these are typically strategic tasks / features. They need more thinking and detailed discussion to figure out if they are worth doing.
· Low Impact, Low Effort: these correspond to low effort experiments that might yield relatively lower benefits; these are worth doing, if there is bandwidth.
· Low Impact, High Effort: these tasks / features can be safely rejected.

BigBasket used this framework to make the right priortization decisions.This helped BigBasket to make rapid progress while avoiding the pitfalls (engineering and tech overheads) that had plagued Webvan and resulted in its downfall.

Experienced product managers would recognize that this is a variant of the more-popularly-known RICE Framework. Not only is this simpler than the RICE Framework but also provides a way to explicitly incorporate business impact and, thereby, handle long-term strategic tasks / projects more naturally. By stack ranking Impact and Effort and with the help of 2x2 matrix (across Impact and Effort), it also makes it easier to prioritize and pick the tasks / features that can provide better ROI.

[How] User Research and Lean Approach

Though this deserves a separate conversation, Tejas touched upon the following during the course of the discussion.

User Research

Tejas poined out that it is important to undersand user needs and wants by doing first-hand user research with the target personas. In the initial days, BigBasket did this by talking with several potential customers in the upper middle-class localities (such as Whitefield in Bangalore).

During the User Research, BigBasket understood both “needs” and “wants”. In the 0 – 1 phase, BigBasket focused on the “needs” and deferred working on the “wants”.

In the 1 – 10 phase, BigBasket started validating product offerings based on customer feedback. To ensure that BigBasket could iterate quickly, BigBasket teams had frequent conversations with the early adoptors and actively solved the problems/issues they pointed out.

Tejas emphasized the importance of continued User Research even after achieving Market Product Fit. In the 1 – 10 phase, for example, Tejas pointed out that it is necessary to ask sharp and probing questions in order to get to the bottom of the problem. A lot of post-PMF companies stumble here – they don’t dig deep enough to uncover the Why underlying the issues (and, as a result, don’t build the right features / products).

Tejas used the following example to highlight this point:

Let’s say a lot of users go from the homepage to the fruits and vegetable section but stop their journey there. In other words, the conversion on the fresh produce section is low.

One possibility is to hypothesize that this could be due to pricing and, to test that, run experiments by placing discount-related banners in the fresh produce section. Even if this results in better conversion, the question is: was price really the only cause? Or was it just a manifestation of a deeper cause?

In other words, it is important to dig deeper to understand the reason for low conversion. There could be other reasons: not clear return policy (which makes people uncertain about whether they can return the “spinach” if its not fresh); or, perhaps, not clear cash-on-delivery support (especially if people associate cash-on-delivery with trust), etc.

Even after formulating various hypotheses internally, one should not start building and running experiments. It is important to talk to users and find out why they are not buying fuits and vegetables. It is possible that they might want it quickly – within 45 minutes, for example. In other words, it is important to get the clarity about hypotheses by talking to users. This can help to uncover not-explictly expressed needs and wants of the users.

Moreover, talking to the loyal users is critically important. Loyal users are more likely to share how their needs and wants are evolving (such as “90 minute delivery for topping up their orders” or the unhappiness with late deliveries). This is important to not only ring-fence good users but also to continually iterate in order to ensure the continued Market Product Fit.

Learn – Build – Measure – Iterate Model

In the 1 – 10 phase, BigBasket started complementing customer feedback with the data collected by the platform. It is important to collect multiple signals about user behavior – not only the qualitative user research but also the quantitative clickstream data. Together, they help to uncover various product areas that can be improved to increase user engagement.

Multiple signals and multiple possibilities are a real challenge in the 1 – 10 phase and 10 – 100 phase; as a result, it becomes important to use a good prioritization framework to select the right tasks / problems to solve.

Tejas also pointed out that before “building”, it is better to run quick experiments to validate the hypothesis – even if the hypothesis is derived from usage-based data patterns. This is because usage patterns reveal what users are doing but not why they are doing (or not doing) certain things.

The experiments themselves can be in the form of doing surveys, calling up and talking to customers, doing a sales pitch to get a sense from customers, or building a mock-up product to observe possible user behaviour. In addition, the A/B testing is a good way to determine if any of the approaches are helping achieve the outcomes. Too many times, companies build products that don’t end up solving the core problem fully. In other words, it is important to understand the potential impact of a feature / product before starting the Learn – Build – Measure – Iterate cycle.

Conclusion

I had observed the rapid rise and spectacular fall of Webvan from close quarters. During the heady days of the initial dotcom wave (1996 – 2000), I was working at Oracle Corp (in the Silicon Valley) and a few of my colleagues had left Oracle to join Webvan (which was located in Foster City – quite close to Oracle’s Redwood Shores’ office). Peter Relan, an ex-Oracle person, was the technology head at Webvan. He identified two main reasons that caused the downfall [Techcrunch article].

· First, Webvan was offering WholeFoods experience, while the users wanted the Safeway prices. In Indian terms, Webvan wanted to offer Nature's Basket like experience but at the Big Bazaar prices.
· Second, Webvan spent too much time over-engineering and building complex backend platforms.

Conversation with Tejas helped clarify how Bigbasket avoided these mistakes. BigBasket avoided the first mistake with the help of “Who” and “Why” of the WWWH Framework and avoided the second mistake with the help of “What” and “How” of the WWWH Framework. In the process, BigBasket became one of the first companies across the world to crack the toughest e-commerce problem – inventory-led grocery business with perishable goods!

October 1, 2020

BigBasket: Defying the Odds to Grow Beyond $1B GMV Sustainably

One of the hottest companies before the Dotcom bust of 2000 was Webvan. It raised $800 million from Sequoia, SoftBank and several other...

Read More
Frameworks
Product Management
September 10, 2020
The 3 Step Framework for Designing Better Products

Why do some products work better than others? Why do people just get them? Probably because these products solve problems. They’re easy...

Akash Sharma
&
8
min read
Read More

Why do some products work better than others? Why do people just get them?

Probably because these products solve problems.

They’re easy to use. Yet, they wholeheartedly embody aesthetics.

But, we don’t just want to gawk in awe at their greatness. We want to be able to build them.

Can we discern the magic of a product’s design?

Even if we do that with some success, how can we apply it to our own products?

Truth be told, we can’t.

It isn’t about emulating what works for them. It’s about understanding what our products stand for and how they make a user’s life better?

Though, there’s an underlying framework in the thinking that goes behind the making of these products, that can help us forge the right path for building our own products. Before we dive into it, here are two things to know about the inner workings of our brains.

First we see

Then we see again. And then we make stuff up.

In his astonishingly fascinating book, Creativity Inc., Ed Catmull shared that during the research phase of an upcoming Pixar film, Director Pete Docter learned something interesting from a neuroscientist, the fact that –

40% of what we think we “see” comes in through our eyes. The rest is made up from memory or patterns that we recognize from past experiences.

Because our brains are hard wired to find meaning in random, and sometimes rather archaic shapes and figures, to find order amid chaos, to find signal amid noise. That’s why, we see faces in the moon and react to emoticons as if they were real people.

Origins of our perception, the way we ascribe meaning to our vision, sprout from our expectations, our moods and cultural influences.

What do you see? A duck or a rabbit?

What do you see? A duck or a rabbit?

A lesson from Child Psychology

In an interview with The Paris Review, Adam Philips, an astute psychoanalyst and a prolific author, said that children need a secure social footing to achieve a state of focus:

“In order to be absorbed one has to feel sufficiently safe, as though there is some shield, or somebody guarding you against dangers such that you can ‘forget yourself’ and absorb yourself, in a book, say.”

For us to concentrate and make certain decisions, it’s required that the right emotions kick in, ones that make us feel safe about the immediate environment that surrounds us. Thus, something familiar is more likely to be trusted than something that’s unfamiliar.

That means, as soon as we see something, let’s say a website, perception ensues, and then our brains get to work, looking for cues to interpret how trustworthy the website can be deemed, often without us knowing about it.

Here’s what we’re dealing with – Preconception on steroids coalesced with an insatiable appetite for being safe.

Keeping this in mind, how can we sculpt interfaces that imbue trust? How can we build better products?

Vignelli’s Design Trinity

Legendary Italian designer, Massimo Vignelli believed that there are three aspects of any form of design that are of paramount importance: Semantic, Syntactic and Pragmatic, a 3 step design framework for better design.

I like design to be semantically correct, syntactically consistent, and pragmatically understandable. I like it to be visually powerful, intellectually elegant, and above all timeless.
Massimo Vignelli

Semantics

It’s the first  and the most crucial step in the process and it’s about starting with why.

It’s about searching the real meaning behind any design initiative, and a deep understanding of the nuances of users’ psyche and a drive to learn all possible aspects of problems that they face. And using these nuggets to design better products.

The answer lies in an empathetic approach towards design.

What method acting can teach us about empathy

Goodfellas is one of the best stories ever told on screen by Martin Scorsese. It’s a movie based on the culture of organized crime in New York and was adapted from a non-fiction book called Wiseguy: Life in a mafia family.

To zero in on our lesson, let’s learn how one of the main characters, Jimmy Conway, was brought to life by Robert De Niro – To get to the quality that the role longed for, He had to step inside the character’s head. And that’s exactly what he did.

He didn’t just devour the book but also the research material that was discarded from the book. He spoke with people who had some connection and relevance to his character, and in each conversation, launched a sortie of questions like – “How his character held the shot glass? What kind of faces he made after meeting a certain character? How he used a ketchup bottle?”

He dug through everything he could get his hands on.

That’s method acting. That’s taking an earnest leap into the shoes of another. That’s cognitive empathy in its purest form. That’s caring. And yes, perhaps, that’s what we need to do.

Agreed. It’s easier said than done. But it isn’t impossible.

As per Stanford d.School, an empathy map is a tool that can help you distill users’ needs and insights from your research. You can begin by taking notes of following four traits of your users:

Say: What are some quotes and defining words your users said?

Do: What actions and behaviors did you notice?

Think: What might your user be thinking? What does this tell you about his or her beliefs?

Feel: What emotions might your subject be feeling?

Here’s an example from Craft Coffee, a company that offers a monthly subscription that brings their customers a curated selection of the most delicious roasted coffees available in North America.

They really care about turning morning coffee into a moment of wonder.  To ensure that their mission gets accomplished, one coffee at a time, they have created a short questionnaire to know more about their customers’ existing preferences, to know their Coffee DNA as they call it.

Craft-Coffee-Questionnaire

Syntactics

When you take up a design project, it’s essential to understand the nature of syntax.

It is same as syntax in language where it leads to articulation of an idea with appropriate use of words and grammar.

The dog bit Johnny. Johnny bit the dog.

Exact same words. Entirely different message. Context changes with the sequence.

Similarly, in design, syntax is a careful consideration that helps us pair the right color palletes, the typography, the layouts and all the other elements that form a product and also how they impact each other.

But it’s not just about placing these elements in an order that’s pleasing to the eye but also about testing what triggers the right action.

In an experiment, Pintrest’s growth team found that white text on a black background got 10% more signups when compared to black text on a white background.

There’s science behind how colors, fonts and beautiful images affect our brains.

Though, let’s keep studies aside for a moment.

Here’s a question for you –

If you could only make one webpage for your product, with the following limits, how will you approach the assignment? What would you do?

  • A font with a fixed size and a fixed color
  • Two background colors
  • Your company’s logo
  • No images
  • Few Links
  • A small button
  • No Menu

Thought about it?

Here’s how Basecamp designed the Know Your Company homepage clearly breaking the logjam of constraints, with a beautiful pairing of all important elements, the website is simple yet visually stimulating. The clarity of the message strengthens the promise of the product.

Know Your Company

Pragmatics

If a product fails to communicate its core idea, if it doesn’t make it clear enough for users to understand how it works and what it could do for them, no matter how aesthetically stunning it maybe, it doesn’t engage users’ minds. It has failed by design. Because, people don’t buy products, they buy better versions of themselves.

This graphic from User Onboarding, explains it best –

useronboard-supermario-2

Bellroy does a great job at it. Their product is physical. Still, they’ve managed to show how the product would benefit the user when compared to an average wallet, with a simple slider.

bellroy-slim-wallet-design

Last year, Buffer’s Joel Gascoigne wrote about how buffer was launched without essential features, and he concluded his post with a lesson that calls to mind Don Norman’s advice in the seminal book, The Design of Everyday Things, on how the purpose of a product’s design can easily be lost if the following questions remain unanswered:

What does it all mean?

How is the product supposed to be used?

Is it possible to even figure out what actions are possible and where and how to perform them?

The approach to merge aesthetics with form and function, based on a thorough understanding of the problem at hand can move us towards the right direction. It can guide us towards better design. And help us in creating products that people trust.

Though, it’s just a start.

September 10, 2020

The 3 Step Framework for Designing Better Products

Why do some products work better than others? Why do people just get them? Probably because these products solve problems. They’re easy...

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
&
Ravi Bhushan
10
min read
Read More

Real Estate SEO 101 - Housing.com

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
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
&
Shoumyan Biswas
18
min read
Read More

Flipkart startup story

Flipkart startup story | digital first brands | Trishul, Kumaon range in Himalayas
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
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
&
Vikram Bhaskaran
7
min read
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Chargebee's Growth Story

Chargebee Launch Plan | Mosi-oa-Tunya (“the smoke that thunders”), Zambezi River
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:

Chargebee Robust Marketing Strategy | Chargebee Website Visitor
Chargebee's Website Visitors

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

Chargebee's Website Visitors

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:

Robust Marketing Strategy

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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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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COVID-19: Strategies for the new normal

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
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
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COVID-19: Strategies for the new normal

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:

Business Strategy Framework | Business Model Strategy

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:

The Impact Matrix | A Digital Analytics Strategic Framework

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:

The Response Matrix Method

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
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
&
Anuj Rathi
20
min read
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Swiggy's growth story

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:

swiggy architecture and growth formula

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
Design
March 14, 2020
Designing for Hypergrowth: Lessons from Uber, Lyft, & Bounce

In the present times, there has been a paradigm shift in the startup world from ‘growth at any cost’ to ‘product-led growth’ or growing...

Sheta Chatterjee
&
Sruthi Sivakumar
15
min read
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In the present times, there has been a paradigm shift in the startup world from ‘growth at any cost’ to ‘product-led growth’ or growing sustainably. By product-led growth, we mean using a product as a vehicle for growth. It is a growth strategy whereby companies emphasize on improving their products to make them more customer-centric. When a product caters to users, it automatically witnesses a boom in the adoption rate, which, in turn, soars growth and success.

Before looking into the most powerful strategies and stages of growth, let’s first recap the elements of growth. Simply put, growth is just a function of acquisition, retention and monetization. When a product is rolled out, it should acquire quality customers to become a market-fit. However, acquiring new consumers is just half of the work. we should equally focus on retaining as many customers as possible and ensuring they are actively using the product. That’s because when you acquire a customer, you ensure 1 purchase but it is retention that ensures repeat purchases. Thus, you should divert all your efforts in acquisition and retention to grow profitably. Now let’s understand each step through real-life experiments and examples of giants like Uber, Bounce, Lyft and Dropbox.

Onboarding and acquisition

When you are building a product, you need to reach out to the right users so that they can get onboard. So, we asked our experts what they did, what worked, and didn’t work in their respective journeys:

Uber

In the early days of Uber, the design team aimed at product-led growth by focusing on acquiring more drivers. Onboarding was a key area to achieve growth . Getting new drivers made it possible to increase operational cities and doing it through a product guaranteed exponential scale. Hence the product led driver growth was key to uber’s success

uber growth strategy, uber onboarding process, uber driver onboarding, uber onboarding flow
Uber Growth Over Time

While carrying out research, they found that complex documentation and legal processes were major bottlenecks that prevented drivers from getting onboard. So, to reduce the friction, Uber’s driver growth team planned to make the process of onboarding seamless for new drivers by reducing their efforts. They bifurcated the onboarding process into small steps instead and focused on executing one-step-at-a-time.

Good experiments advance product strategy. Bad experiments only advance metrics. [reforge]

Measuring the right success metric was crucial to Uber’s success. Instead of falling in the pitfalls of the day, they set up their north star metric as the first trip. For the team growth wasn’t just the sign up but it was the entire early lifecycle of the user. The way Uber’s product team defined it was the first 28 days. This lead them invest in levers for growth such as driver education, reducing anxiety etc. leading to a string of successful experiments.

Uber’s improved flow for driver onboarding (inspection)

First goal for the driver growth team was to reduce the number of people dropping off during the process. The second was investing in referrals. When a driver is referred by somebody, they retained higher since they knew somebody that’s already on the platform which in turn reduced the anxiety involved. Beyond organic and paid acquisition Uber’s team discovered that referrals were a huge channel for acquiring high quality drivers. This led to them investing significantly in referrals and became a mainstay of Uber’s growth strategy.

A similar yet a more on-brand strategy was adopted by Lyft where a buddy was allocated to the rider to reduce their anxiety before taking an actual drive. They also laid focus on valuing behavior and on creating a friendly environment for the users.

Bounce

The acquisition strategy of Bounce is also in the same vein to Lyft and Uber. In addition to taking the first ride, the time to take the first ride (time to activation) became a key metric for bounce. This was because of user’s behavior and how they discovered the product. They aimed at making onboarding seamless for the riders by simplifying the KYC process with the only requirement of Driving License from driver’s side.

Bounce Growth & Acquisition Strategy

Pulling this off well required innovation from the team during document upload to both make it intuitive and educate the user along the way on the right way to do it. Since this wasn’t possible using native camera, their engineering team built a custom camera that enabled them to do just that.The team further made an attempt to reduce the time between KYC verification and first ride through integrating with state databases making it seamless.

By focusing on reducing as much cognitive load as possible during onboarding enabled Bounce and uber to activate a lot of customers in their early stages setting them up for hypergrowth later.

Retention and habit

Congratulations! You now have customers! What do you do next? Ideally you want them to come back often and spend more.

Now the question that arises here: How can you retain customers and spend less doing it? Several growth drivers are available for fast growing companies focusing on retention. One of the fundamental drivers is Habit.

A habit is formed when the customer uses your product repeatedly where their natural frequency of doing a particular task is strongly associated with your product or brand.

Bounce experimented with habit formation to improve retention in its early days. The team focused on understanding its existing power users and launched quick experiments in order to increase the frequency of usage. The key goal here was to inculcate a habit of using Bounce for the everyday commute. Furthermore, the product team trained themselves to call existing customers and take feedback from them. Sruthi recollects how the idea of putting together a VIP experience team was conceived and executed within a day. The team focused on understanding the friction and obstacles that prevent a habit from forming. The early customer feedback showed that customers wanted to see improvement in the condition of bikes. So, they emphasized on improving the quality of their services to gratify existing users.

Bounce Startup Growth | Bounce Premiere League | Bounce Bonus

While frictions were being addressed, another formula to aid habit formation is giving the right trigger and reward. In order to achieve this through product, Bounce retention growth team introduced a rewarding system whereby they gifted scratch cards to their customers on completing habit forming quests such 5 rides each week, Consecutive good parking, etc. This proved to be very successful in improving bounce’s retention in the early days.

Lyft is on pace to surpass Uber according to data shared by Apptopia.

lyft growth, lyft onboarding
Lyft retains users at a significantly higher rate than Uber

This is driven by significantly better retention. Lyft’s retention strategy is very centered on the user and they have operated with a long-term vision. Its focused and measured by user lifetime value. This has enabled them to experiment in the short term that compound the results over time towards their overall strategy. One of the fundamental levers Lyft decided from early on was focusing on improving the job space (i.e. transportation and mobility) as a whole than bounding themselves within ridesharing.

For example, Lyft’s team added all transportation options that include the likes of public transportation in their app. While this was counterintuitive, it helped Lyft become top of mind for their customers when they want to commute. This was a powerful foundation for retention.

Lyft also decided to not build a traditional loyalty program. They went with a membership program instead. This was a deliberate decision as the team wanted to drive value for the customers every month than consistently. The membership program itself is an amalgamation of both monetary and non-monetary rewards. They introduced several perks for those users who enrolled for membership. The membership perks include perks like priority airport pickup, free 30-minute bike/scooter ride and 15% discount.

By focusing on keeping the user engaged Bounce and Lyft were able to achieve significant advantage by building a habit. Products that enable forming of habits have aspired to fulfill the natural tendency of users to do a particular task or job. This sets up ideal conditions for sustainable growth loops that keep paying dividends for a long term.

We have compiled the key insights from our first masterclass. I hope you have gained various insights from the stories from people who championed growth at Uber, Lyft, and Bounce. There are more behind the scene stories where we dived deeper in to network effects , referrals, and the elusive product market fit. To get access to all this exclusive content and future masterclasses, sign up at productgrowth.org and follow us on twitter: @product_growth.

March 14, 2020

Designing for Hypergrowth: Lessons from Uber, Lyft, & Bounce

In the present times, there has been a paradigm shift in the startup world from ‘growth at any cost’ to ‘product-led growth’ or growing...

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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]:

Spiral Effects

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.

Engagement Graph

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.

Engagement Graph

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:

Value Creation: Three Strategies

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

Value Creation: Three Strategies | Importance of activity

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.

Innovation & Disruption

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 1: Frequency-led Innovation | First Four Years Growth after Launch

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...

Read More
Frameworks
Marketing
February 6, 2020
Engage 101 - Building sustainable growth through retention

Today, we will start diving deeper into the Engage phase of the customer journey to drive sustainable growth. Here we will look into how...

Nachiketas Ramanujam
&
7
min read
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Simple Customer Retention Strategies For Sustainable Growth

Today, we will start diving deeper into the Engage phase of the customer journey to drive sustainable growth. Here we will look into how Retention-led growth plays a big role in the growth of your company. In the Engage phase, we focus on:

  1. Active users: Increasing the number of active users → User/cohort growth
  2. Active usage: Increasing the frequency of usage → Stickiness/Habit

Growth Spiral: Engage phase of the customer journey

Why Engage?

In the early stages of growth, most companies focus on acquiring users. Companies seeking growth might go a step further to focus on driving usage of the product’s core value. This is what we call the invite phase and we will cover this in a separate post. For your product to achieve its full potential and create the most value for most users, it must GROW.

Retention curves comparison of top android apps

Why Engage? - Retention Curves for Android Apps

Ideally, all the new users you acquire would continue to engage with the product forever as your product grows. In reality, that’s not how it shapes up. A quick look at retention curves of popular apps will confirm this. So now, retaining your users becomes a major factor for growth. As you notice, the top 10 apps do a phenomenal job of holding their customer’s interest.

The majority of products achieve this by engaging users to consistently deliver value at each touchpoint.

This is not a mere coincidence. They actively prioritize and consistently execute various strategies as well as tactics to improve retention. This is true even if you are in a strong pull-market.

Benefits of retention-led growth

One common question that arises when I work with high growth companies is: Why lead with retention? We will see this in an example that follows. In this example, we will be using the Quick ratio and retention rate as a way to measure the growth and health of the business as seen below.

Credits: Reforge & Sequoia Data team.

Benefits of retention-led growth | Retention "Product-Market Fit"

In case you need a quick refresher on quick ratio and measuring retention, please refer back to our previous post on this topic.

Example: SuperCab

Back of the napkin growth strategy

Now consider a fast-growing company we will call “Supercab”. Supercab is a venture-backed company in a fast-growing market segment. They are quickly approaching the coveted product-market fit. They have a couple of strategies available to them.

  1. Acquisition-led model: here we focus on improving our acquisition funnel and don’t actively do anything to improve retention but only stabilize it.
  2. Retention-led: here we leave the acquisition stable while focusing on improving retention

Based on your understanding of the numbers above, which do you see is better for sustainable growth. Now if you said Retention-led, you will be correct. As you can see by focusing on improving retention, Supercab can potentially grow better.

Retention is the best indicator of product-market fit and the most important lever for sustainable growth.

Now, if you are a founder or part of a fast-growing, venture-backed startup, you are never running just one strategy. It’s mostly the question of resource allocation and prioritization. So, what if Supercab did acquisition well in addition to retention-led growth? As you might have noticed, they can get 50% better growth built on the backs of retention. This is the compounding effect that a retention-led growth strategy provides.

A note on embedding & lock-in

If you are a SaaS product, one of the fundamental moat available to you is lock-in. Simply put, the company/user using your product is locked in to continue using your product. This enables you to expand your revenue by providing value-added products contributing to your Expansion.

But wait, what if you are a consumer company or a bottom-up SaaS product. Even then this will work to your advantage. Most consumer plays have the problem of multi-tenanting. If the users don’t use your product, they are probably using an alternative. The alternative could be an inefficient product or a competitor. By engaging and delivering value consistently you create an unimpeachable position in the user’s mind. The lucrative top-of-mind recall. Let’s take the SuperCab example again. If every time a user wants to take a cab ride and SuperCab is top of mind, then it is their customer to lose.

Getting Started

By now I hope you are convinced about the effectiveness of the Engage phase and retention led growth. Let's start the Engage phase by setting up the foundation and increasing the number of active users. Here are some quick tips to get you started on your journey to executing it.

Step 1: Segmentation

Segmentation overview

Step 1: Segmentation

Segmentation also sometimes know as cohort analysis is the best place to start your journey to understand growth levers. Segments or cohorts are just a fancy way of saying grouping users based on some commonalities. So instead of looking at all users in one broad view, cohort analysis breaks them down into groups. In our playbooks series, we will dive deeper into how to do better segmentation.

For now, I recommend starting with behavioral segmentation, which is grouping users based on their behavior within your product. Once you have a fair amount of confidence in your segments, start looking for the following:

  • see how their behavior changes over time
  • look for patterns that influence their engagement with your product
  • compare different groups to identify best practices and use cases

Step 2: Understand your super users

If you are building a valuable product, you are most likely solving a problem or satisfying a need in the market. The early adopters of your solution are people with the highest pain point. They are the ones most engaged with the core value that your product provides. Understanding how this group interacts with the product is the best way to improve overall retention.

To identify your super users, start with a hypothesis about which features are core to the product’s value proposition, Once you’ve identified your “magical moment,” segment your users based on how, and how frequently, they engage with it.

Next, find ways to talk to them to understand:

  1. Empathy: Who are they as a person? What does their life look like? What needs are we satisfying for them?
  2. Journey: What was their experience using your product? What were some points of friction?
  3. Workarounds: What did they do to overcome that?
  4. Expectations: What is their version of a “magical” experience?

(We plan to cover this in more detail in a future post on research methods available to startups)

Step 3: Remove friction and Entice

Once you understand the key behaviors and friction surrounding them, its time to fix them. Now, as a fast-growing company, you may not be able to fix every friction point. So to prioritize, we seek the help of a cognitive psychology phenomenon called “The Peak-end rule”.

Illustration describing the peak-end rule. Image courtesy: the UX blog

Dr. Kahneman puts this the best as below:

We judge an experience by its most intense point and its end, as opposed to the total sum or average of every moment of the experience. So tap into empathy, end on a high and make people feel great about using your service.
- Kahneman, D. (1999) Well-Being: The Foundations of Hedonic Psychology

To apply this, just look at your ends and see how you can make those points “magical”. Then look at the negative peaks during the experience and make them less painful. Remember delighting users is nothing but reducing their effort to achieve an outcome and exceeding expectations while doing that.

Step 4: Measure and Repeat

Always remember, Your company’s retention isn’t ever going to be static — so you need to pay constant attention and put in the time and effort to calculate it and optimize it. Once you are through one cohort, traverse to the next one. Continue to measure the different experiences and avoid falling in the silver bullet fallacy.

Direct your attention towards getting customers to stick around, and you’ll build a stable foundation with huge potential for internal growth.

No silver bullets, just solid strategy.

Concluding remarks

I hope you found this helpful in kick-starting your Engage Phase of applying The Growth Spiral model. We are planning on following up with more posts diving deeper with case studies, and tactical frameworks for everyone.

Meanwhile, If you are an Accel founder and interested in getting early access to upcoming material and the Growth community we are building, please email: venturein@accel.com or partner from your investment team.


February 6, 2020

Engage 101 - Building sustainable growth through retention

Today, we will start diving deeper into the Engage phase of the customer journey to drive sustainable growth. Here we will look into how...

Read More
Case Studies
Operations
August 16, 2019
From First Mile To Nth: Onboarding Beyond User Onboarding

Yohann Kunders from Chargebee writes about onboarding and tries to answer questions like ‘How does a user onboarding flow end?’ and other...

Yohann Kunders
&
19
min read
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‘How does a user onboarding flow end?’ and other difficult questions

How does a user onboarding flow end? And when? Does it extend past the first mile? If it does, where are the lines between user onboarding, feature adoption, and customer success? Are they different things at all?

We’ve been obsessing about every tiny user onboarding detail at Chargebee over the last few months—where to begin, how to cater to intent, what creates delight, and how to ask questions—easy questions.

But we’ve been tiptoeing around tough ones. And they are tough.

On the one hand, you have what I call the fuzzy view or the view that user onboarding never really ends.

Here’s Samuel Hulick snaring the essence of it: “Well, it gets a little philosophical at this point, but I usually define onboarding as a continuum. I believe an onboarding opportunity arises anytime there’s a gap between what the user is currently doing and what they’re capable of doing based on everything you provide to them.”

On the other, you’ve got the clear-cut view or the view that user onboarding ought to apply only to the initial use of your product.

Ty Magnin puts it in a nutshell perfectly: “Users are not customers…the situations of the user and customer are entirely different. Their motivations are different. Their abilities are different. When you look at it, they’re on the opposite ends of the scale. They differ in approach, ownership, and the way you should onboard them.”

Both sides have fair arguments. And this means the lines between onboarding for the first mile and onboarding for the nth are difficult to discern. What does each look like?  

We can’t avoid the tough questions forever, though.

Kathy Sierra explains why in her book “Badass: Making users awesome”: a great first mile + not-so-great miles after that = product quicksand.
product quicksand user onboarding | first mile computer definition

Product quicksand and what it means

To put that graph in perspective, here’s what a user journey looks like when every mile is a great mile. This is what all products want—for every user to be in the top corner of this graph. Going from strength to strength and success to success with use and engagement.

making users awesome user onboarding | Product quicksand and what it means

It’s hard, though. Especially when you have a complex product. We certainly do. And when users feel like they can’t move forward, one of two awful things happens:

  • Users stay stuck in product quicksand.

    It might look fine but it’s not.

    Kathy explains why: “This might look good on a spreadsheet for user retention,” she writes, “But if [users are] no longer moving up and to the right, they aren’t increasing resolution, gaining new skills, or becoming more powerful. Their enthusiasm for their new abilities and results will slowly fade.”
  • Worse, users can tip back down over time and begin to feel like the product isn’t working for them. At which point they will no longer be users anymore.

Product quicksand almost always means churn, in other words.

Here’s the thing: I just don’t understand how product quicksand even happens. If you’ve crafted a beautiful first mile that propels users to the promised land, how could it be that they can’t discover new ways to make it better?

How is it that you can sell users on your product and then fail to sell them on your features?

My sense is that it might be a consequence of the vagueness around what the transition from the first mile to second and beyond looks like. A hesitance around how to balance onboarding for activation and onboarding for retention.

In what follows, I’m going to clear up the lines of ambiguity around user onboarding for the first mile and the nth and give you five tactics (three on flow and two on mindset) that we’ve learned to apply to any onboarding that happens in the user journey.

Onboarding at the first vs. the nth mile

Let’s begin by zooming out a bit. It’s worth taking a look at the difference between the first mile and the nth if we’re trying to get at the difference between onboarding for either.

What’s actually happening when a user progresses in her journey through your product, Kathy argues, is your user is getting better and better at integrating your product into the big life thing she’s trying to be better at (integrating your camera, for example—if you were building cameras—into her big life thing of being a better photographer).

Understanding this is the key to keeping customers for life, argues Jonathan Kim, the founder of Appcues, in his talk, How To Keep Customers For Life.

When you do, he says, you see that you need to do everything in your power to push and guide this integration along over the course of a user’s journey.

In the short term, this means creating success with user onboarding. In the mid term, it means helping customers integrate your core features. In the long term, it means getting out of the way (for the most part) and surfacing the right features when you find that a customer needs one of them.

Jonathan’s ideas align so beautifully with Kathy’s because they’re both saying pretty much the same thing: the best way to keep users is to keep making them awesome.

Here’s how their ideas overlap:

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retention means making users awesome | The Ultimate Guide To Revenue Operations

Calling it ‘feature adoption’ really sets a bad precedent. There’s so much more to making customers awesome than throwing features at them and hoping a few stick.

The term ‘feature adoption’ underplays (if not entirely discounts) how feature/product fit is found, it says nothing of whether features align with a user’s workflow, and it ignores how features are adopted.

Users integrating a feature into their lives isn’t something that just happens, it’s something you pursue, and something you fight for.

It’s time we replaced the term ‘feature adoption’ with something that captures this. ‘Customer onboarding’ is the perfect replacement.

When a user signs up, user onboarding propels them to initial success and they go from ‘I don’t get this’ to ‘I can do the basics’. Onboarding customer onto core features and expanding their use takes them from ‘I’m competent’ to ‘Hey, I’m good at this’. Getting out of the way and interrupting only to onboard them onto a relevant feature that will bring them even greater success (whether it’s new or it has been around for a decade) will take them from ‘I’m advanced’ to ‘I can kick ass’.

Let’s dig into how they might be different.

Ty and other advocates of the clear cut view are right—user and customers have different contexts, motivations, abilities. And this means different data, approaches, and onboarding.

But there’s one crucial difference (or similarity, depending on how you look at it) they’re missing.

A product is composed of features. This means that both user and customer onboarding are onboarding onto features. Yet, while one says ‘this is the promised land’, the other says ‘this is how the promised land is getting better’.

Why?

Because one (user onboarding) has to present the features as a whole (this is the promised land). And the other (customer onboarding) has to present a feature as a part of the whole (this is how the promised land is getting better).

Rolling with this idea of composition, here’s how I would frame the difference between user and customer onboarding: in the first mile, you’re onboarding users to your product (as a whole). In the nth, you’re onboarding them onto features (new features, old features, whatever part of your product your customers need to be successful at the time).

So. In light of the points of difference (and similarity) between user and customer onboarding we can ask:

  • Is it possible that your user onboarding sets the foundation for any sort of onboarding that is to follow?
  • Can seeing the connections between the two help you use usage data for better customer onboarding?
  • Can a user onboarding mindset make customer onboarding easier?

The answers are yes, yes, and yes.

Putting user onboarding to work beyond mile no.1

Seeing user onboarding as helping new users find the aha of the product and customer onboarding as helping customers (who are in the promised land) find the aha of features helps us see the points of difference between the two a little better.

User onboarding has to deliver on the promise of the homepage and customer onboarding has to orient new features against it. The fact that one helps activate and the other helps retain doesn’t imply that they’re two entirely different things. They work together. And this means you can put user onboarding to work long after your user onboarding flow has ended.

Here’s how.  

A. Laying the foundation for the nth mile in a user onboarding flow

First, let’s talk about the user onboarding flow. Here are three ways you can tweak your user onboarding flow so that it actively makes space for what’s to follow.

#1: Set up expectations for feature-driven success

Give users a glimpse of what’s in store.

This glimpse can either let users in on the fact that there’s lots more to discover, that other users are using features in creative ways (for problems they might not be expecting), and that there is breadth to the solution you’re offering.  

Of course, user onboarding has to be light; more than just a peek at a feature can overwhelm.

This is why I don’t like tactics like in-line hinting and tooltips that point to other features during onboarding. They’re just too distracting. These glimpses need to be a little more subtle, a little more passive—loading screens, prepopulated data, alternate tabs for the curious, and microcopy.

Here’s how a few businesses handle it.

Intercom hints at the use cases that it can handle (even if users arrive with the intention of using it for something else) during signup with an unobtrusive tab:

intercom customer onboarding | #1: Set up expectations for feature-driven success

The ‘Products’ tab is the default (it’s where you let Intercom know which of their products you’re interested in when you sign up), this ‘Use Cases’ tab is only for the curious.  

On the other hand, ActiveCollab hints at the breadth of the product with a prepopulated project:  

active collab setting up customer onboarding

For someone who’s new to project management, this can be overwhelming. But it also suggests that there is so much that ActiveCollab can help you get done.

Samuel Hulick on how Peach manages to pull off being “hands-on while hinting at a lot more to explore”:

samuel hulick peach

And finally, here’s Invision, creatively suggesting that there are lots of projects you can use it to handle even if you’ve got just one on your mind right now:

invision setting up customer onboarding

That’s all you need to set up new features in user onboarding: a hint, a spark, a set-up. Far from obtrusive and distracting. The fire will happen on its own as a user gets familiar with your product.

#2: End the flow on a customer onboarding key

If user onboarding ends with delivering a user to the promised land, what better way to end the flow than with a setup for something that can make the promised land even better?

Most apps end their user onboarding flow rather abruptly and I’ve always seen it as a suggestion that it’s time for me to start exploring the product on my own. Canva, for example, redirects to its dashboard post user onboarding flow.

There is the rare app, however, that ends their user onboarding flow with a suggestion for a next step that builds on the actions it has just helped me complete.

Some apps, like Headspace and Duolingo, have it easy. When onboarding is the first lesson, all it needs to end with is directions to the second. Netflix and Hulu have it easy as well—the sooner they get out of the way and let me watch an episode of The Good Place, the better.

It’s a little more complex when you’re dealing with a product that is made for different kinds of users and has tens of possible features on the table. Here’s the last pop up you see in Buffer’s onboarding flow and it really expands on the promised land (this might be a little outdated but it still stands):

Buffer setting up customer onboarding

And this doesn’t get in the way of my exploration either. When I close the pop-up, I’m at the dashboard—the same place I might have been without this suggestion anyway.

Alternatively, Xero handles ending their user onboarding flow on the dashboard in style. It’s literally a list of next steps:

xero customer onboarding

#3: Set up the vocabulary of the product

If your features come with lingo, your user onboarding flow can set it up.

This way, she won’t have to spend too much time figuring out the feel of the product in her post-onboarding exploration, she’ll gradually be immersed in it from the get-go.

There is a caveat: Like setting up feature-driven success, introducing vocabulary needs to be subtle. An overwhelming onboarding flow can not only fail to set up customer onboarding, it can harm the promise of the product.

Here are two apps that manage to do it beautifully.

IF, IFTTT’s mobile app, works the vocabulary that you need to use it into the very first use of the product. No better validation than from Samuel Hulick:

new vocab in ifttt
The idea is that when I enter a user onboarding flow, I haven’t formed clear concepts of what it means to perform an action. Especially for a complex product like IFTTT (few of us are integrating applications like Instagram and Dropbox together every day), giving what I’m doing a name the first time that I do it doesn’t get in the way of the learning, it enhances it.

Here’s Facebook’s Slingshot, on the other hand. Another screenshot from Samuel Hulick enjoying the new vocab:

slingshot introducing new vocab

Both examples work for two reasons:

  • Only the core vocabulary is introduced. A concrete question to guide whether vocabulary is core is, ‘how far can a user go in my app without needing to know this word’? The closer the answer to zero, the more core the word.
  • The vocabulary is introduced before any real use. The action is new, opinions are just about starting to form, it’s easy for the word to take root and establish itself.  

B. Taking the mindset of user onboarding into customer onboarding

Second, the mindset of user onboarding is aha focused. Crafting the first mile of the product means balancing education, showcasing features, and action so that it comes together perfectly in an aha moment.

Of course balancing, in turn, means three things:

  • Identifying the core value of your product,
  • customizing the first mile based on intent, persona, or job-to-be-done, and
  • eliminating anything in the flow that delays getting to this aha moment as fast as possible.
The controversial question is when an aha moment occurs.

Is it a magical moment that manifests when a user completes an action? What about when she’s reading a blog post? Can your product’s aha moment come from a friend telling her about it, months after she’s tried your product?

I believe all three are possible.

Tying an aha moment to an action or place blunts the beauty of it: it’s an instantaneous, emotionally-driven connection.

Eric Weiss gets the definition right, I think, when he says: “[The aha moment is] the moment in which [users] grok at what your product can do for them, and they can’t wait to use it again. Your marketing materials made the promises, and your onboarding shows the proof.”

This can happen anywhere, under any circumstances.

This is Lex from Jurassic Park, figuring out how to work the computer and save the day. Look at her—that’s an aha if I ever saw one. And there are raptors outside.

lex aha moment user onboarding

User onboarding propels a user to the product’s aha moment and customer onboarding to features’. So it follows that the mindset that got user onboarding off the ground can be put to work at the nth mile.

The following are built around aha moments and are fundamental to the user onboarding mindset. They are what make onboarding users onto the product so effective. And they can be applied to feature/customer onboarding too.

#1: How user onboarding gets it’s aha

User onboarding gets its aha by positioning the product against a greater good.

What this means is that an aha moment is more than a consequence of how good the solution is, it’s a consequence of how well the problem is articulated.

The better the problem is framed, the better the aha lands during user onboarding.

Here’s the Sunsama promise, for example. Great work organized so I get the best of Trello and Google calendar.

sunsama promise user onboarding

The promise immediately sets up the shortcomings of Trello and Google Calendar (both tools that I use) and how much easier it would be to have a tool that had both side by side. This framing is one half of the aha moment. The other is the delivery, which Sunsama handles delightfully:

sunsama aha moment

An aha is only as good as its tie to the greater good and its delivery on that tie.

When a feature is trying to get to its aha, it has to tie back to the greater good that got me through the door in the first place. It’s not enough to point at what the feature does, it’s essential to point at how it can help me get better at the thing I hired the product for.

Here’s an amazing customer onboarding email from Trello (I still love Trello) to illustrate. For reference, this is Trello’s promise: ‘Trello lets you work more collaboratively and get more done’.

trello customer onboarding

Emails like this one are rare. Most invites to a webinar or links to a resource aren’t usually accompanied with more than a description.

In tying their resources to why I signed up for Trello to begin with, Trello got my attention and helped me use their product a little better.

When you’re trying to build engagement with new features, resources (ebooks, webinars, whatever form this might take), or conversations, a tie back to the greater good that your product is enabling can build motivation and reinforce that better life you’re facilitating for your customers.

#2: Features can get their aha even faster

One of the benefits of seeing user and customer onboarding together is that it puts aha moments in perspective.

While both have to drive towards aha moments, customer onboarding needn’t start from scratch.

New users are all questions. They’re making judgments about long-term value and assessing your solution against what they’re using at the moment.

Customers, on the other hand, have been using your app for some time now. Questions have been replaced with answers (and deeper questions), judgment with motivation, and the old way to get a job done with your way.

This means features don’t need a flow to get to their aha moments, they can get to them much much faster. Like in a feature announcement email, for example. Or a tooltip inside the app.

All that matters is that the tie-back to the greater good (that user onboarding already established) be crystal clear.

Here’s Basecamp, for example, achieving feature aha in seconds with an email:

basecamp customer onboarding

Not only is Basecamp telling me what a boost is but it’s also tying the feature back to the aha that their user onboarding has already established: zero hair-on-fire days at work.

The point is you don’t need emails and in-app banners and tooltips and demos and walkthroughs to get to a feature aha—your user onboarding has already done all the heavy lifting. The only reason I can think of to still have all of them reach everyone is awareness. But the thing is there are better ways to solve the awareness problem. After all, you have customer data, tons of it, and you can segment features by context, awareness, ability, and interest.

To conclude

If great user onboarding + poor feature adoption = product quicksand, then user onboarding + customer onboarding = runway to product success.

The nth mile matters just as much as the first.

Luckily user and customer onboarding complement each other in hugely beneficial ways. You can tweak your user onboarding flow to set up customer onboarding. And you can take the mindset that made user onboarding work forward so customer onboarding doesn’t start from scratch.

What’s more: when your user and customer onboarding are aligned, your users will have a more consistent experience with your product and who doesn’t want that?

Here are the deets for a quick skim:

A. You can lay the foundation for the nth mile when crafting the first by:

  • setting up expectations for feature-driven success,
  • introducing the core vocabulary that your product relies on, and
  • ending the flow on a customer onboarding key.

B. Customer onboarding needn’t start from scratch because  

  • the product gets to an aha by tying core value to the promise on the homepage. Features do the same.
  • And features don’t need a flow to get to an aha. By building on the aha you’ve established in your customers’ minds, you can facilitate a feature aha much faster.

August 16, 2019

From First Mile To Nth: Onboarding Beyond User Onboarding

Yohann Kunders from Chargebee writes about onboarding and tries to answer questions like ‘How does a user onboarding flow end?’ and other...

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Case Studies
Design
July 31, 2019
5 Lessons in Scale, Engagement and User Delight from India

Anshumani Ruddra speaks about his talk on product and design lessons from India. He succinctly summarises 5 learnings from observing...

Anshumani Ruddra
&
15
min read
Read More

I recently spoke at DesignUp Singapore on product and design lessons from India. The overall conference was fascinating (I learnt a lot about the South East Asian design community) – and I am glad that DesignUp is increasing its reach beyond India and becoming one of the most important design gatherings in the world.

Weiman Kow did a fantastic job of capturing all the talks through her sketch notes:

Design Up 2019: Sketchnotes and Learnings (Singapore, Jun 18–19)

The cover picture I used above is from her sketch notes of my talk. Hat tip!

I tend to keep my presentations simple: trying to put just one key thought/ insight/ take-away on each slide. This one also follows a similar template. Adding my speaker notes below to provide more depth and context to each slide.

Slide 1

Good afternoon. I have had the good fortune of working on a broad spectrum of consumer internet products for the Indian market - games, chat applications, healthcare, education and video content. The following are 5 lessons that I have learnt – and given the similarities between Indian and SEA – I think these lessons would be applicable here as well.

Slide 2

I always like starting my presentations with questions to the audience. Raise your hand if your answer is yes to any of the questions.

Slide 3

Did you travel by air last year?
Have you ever purchased anything online ever?
Did at least one of your parents go to college?
Do you make more than USD 10/ day?
Is English your first language?

Last year I asked the same set of questions to a room full of designers in India - whether they travelled by air, the level of education their parents had received, the amount of money they made, whether they shopped online. Under each of these parameters - the people in the room fell in the top 10% (and in some cases the top 1-2%) of India’s population. The point I was trying to drive was that none of them represented the true India and designing for the whole of India was a myth - you were at any given point only designing for a part of it. It was very critical for designers to be aware of their privilege.

Slide 4

Be aware of your privilege. You and your user have very little in common.

User research is critical. Bridging the gap between the people who build products (us) and the people who use our products is perhaps the greatest challenge we face is Asia.

Slide 5

You will never design for the whole of India/ Southeast Asia, but only a small segment of it. (And this is a good thing!)

Pick your battles. We live in very populous regions. If the addressable market for our product/ business is 5-10% of our region - it is still a massive user base. Focus on the opportunity.

Slide 7 and 8

Design languages are not as universal as you think they are.
Dominant products become the lingua franca of design. Don’t fight, but evolve.

At Practo, when we were building a social network for doctors, we realized that the percentage of doctors who were editing the information on their profiles was very low. When we spoke to some of our beta testers (doctors), we realized that they had no idea that the ubiquitous pencil icon signified that the particular text field could be edited (doctors are not the most tech-savvy bunch). Adding some contextual copy resolved the issue.

This reinforced an important lesson for me: that design languages are not universal. Interactions, gestures and iconography are not universal.

But the reverse is also true – a dominant product quickly shapes an entire population’s understanding of a design language. Case in point – WhatsApp in India. I learnt the ‘swipe a chat message to reply to it’ functionality from my mother – a technology noob who has quickly become a WhatsApp power-user.

One of the big challenges in India and SEA is that our users are evolving their design sensibilities at a breakneck pace – and this is happening through market-dominating products developed either in the US or China. If we don’t evolve – we will perish.

Slide 9 and 10

Focus on what users do, but never lose sight of what users say. Deliver on needs, but build for aspirations.

At Cuemath we realized that there was a huge gap in what parents said (“we’d like our kids to fall in love with maths and develop mathematical thinking”) and how they acted (“we’d like our kids to score more marks in school tests”). What they said represented their attitude – which was aspirational in nature. How they acted represented their behaviour – and their immediate needs.

It is critical for businesses to solve for a user’s behaviour in the short term: as you will solve an immediate need. But long term – you have to solve for a user’s aspirations.

Slide 11 and 12

Close the loop on your product experience.
If parts of the experience happen outside your product (and are not in your control), you are losing a massive opportunity.

One of the big lessons from Hotstar is how we have focused on closing the experience loop for the user. Our widely successful social feed was built on the insight that while users were watching live cricket on Hotstar, they were reacting to what they had seen (enjoying a six, frustration with the captain of their cricket team, chatting with friends about the game) outside our ecosystem. By bringing this experience inside the Hotstar app, we not only completed the loop but also created massive engagement.

Thank you!


July 31, 2019

5 Lessons in Scale, Engagement and User Delight from India

Anshumani Ruddra speaks about his talk on product and design lessons from India. He succinctly summarises 5 learnings from observing...

Read More
Frameworks
Operations
March 26, 2019
The Story of Spotify Personas

Spotify personas are the topic of much discussion by those in the product, design and user research communities. Here, Olga Hörding, Mady...

Sohit Karol
&
20
min read
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Spotify personas are the topic of much discussion by those in the product, design and user research communities. Here, Olga Hörding, Mady Torres de Souza and Sohit Karol explain how we developed our personas tool, how we use it today and why it’s so useful for an autonomous, cross-functional organisation like Spotify.

Why not listen to our companion playlist for this article?

Here at Spotify, we often ask ourselves who we’re designing for. And since listening to music is so universally popular, it might seem at first that the answer is ‘everyone’. After all, Spotify is available as a free and paid product. It can be used by anyone with a phone, computer, car, set of smart speakers or many other devices. It’s present in over 79 markets and it offers experiences – like Daily Mixes – that are personalised to every single listener.

Yet designing for a mass, generalised audience isn’t likely to end up pleasing ‘everyone’. So in 2017, our team was challenged to create a better understanding of existing and potential listeners. We wanted to agree on how to differentiate the needs of these listeners and the problems our products could solve for them. We needed a solution that was durable and flexible enough to work for autonomous teams, working out of different offices, in different countries and on different parts of our products. And we were determined to put a face to our listeners – an identity that everyone at Spotify could recognise and talk about with ease.

We responded to this challenge by designing personas.


How did we craft the personas?

User-centered design has several schools of thought on how best to create and use personas. The general idea is that capturing and clustering the needs, goals, habits, and attitudes of existing and potential users helps to build a solid understanding of the problem space. For us, our personas tool is an example of a boundary object – a durable and reliable artifact that’s flexible enough to inspire discussions, share information and adapt to the needs of the product development process. And we developed it in two phases – as follows:

Phase 1 (2017)

In Phase 1, we scoped our analysis to US listeners. We picked this market due to its size and the variety of listening behaviours that emerge from the way of life there – for instance, long commutes, suburban lifestyles and so on. At the start, we discussed the idea of clustering behaviours gathered from our current data. But we moved away from this approach because it revealed only superficial knowledge about our listeners and concealed the reasons behind their behaviour. It also failed to help us understand why potential customers listen to music. So instead, we decided to study listeners of different ages, incomes, family types, lifestyles, music cultures and more. We used a combination of diary studies and contextual inquiries to collect this data.

Early in the analysis, we noticed that people’s needs or reasons for listening to music were consistent, even in different clusters — that is, to kill boredom, to feel productive, to entertain themselves and so on. But what was different was their attitude towards music consumption, the value they saw in paying for music and their behaviours around devices in different contexts.

As a result, we ruled out the idea of clustering based on needs alone and instead used a combination of Alan Coopers method and the Grounded Theory approach to build our personas. We transcribed our interviews minute-by-minute. Then, we coded and clustered them into needs, attitudes, device habits, contexts, and other dimensions in order to identify the best cluster combinations. Two tools — Mural and Airtable — were particularly useful during this phase.

Phase 2 (2018)

In our next phase, we built on a key Phase 1 insight – that when it comes to music listening, context matters. Sure, there’s value in creating abstract dimensions, such as needs and motivations. But ultimately, people use Spotify in the real world. Their device ecosystems, physical and mental abilities and other contextual factors shape their listening choices. And so combining the learnings from Phase 1 with a literature review of theories from sociotechnical systems and adaptive computing, we decided to focus in Phase 2 on how people listen to music together.

In this phase, we sought to unpack the nuances and complexities that arise when people listen together at home, in the car, with kids and so on. And since this work built on our previous research, we once again kept our sampling to within the US. We included roommates, empty nesters, partners with and without kids, households with toddlers, teenagers and more. Our goal was to ensure we had an extensive variety of situations where people came together to listen to music.

Unlike in Phase 1, we followed up our diary studies and contextual inquiries with a bottom-up analysis using the Grounded Theory Approach. Qualitative coding revealed insights that we would have otherwise missed and resulted in the Listening Together Framework™, our tool to communicate the outcomes to a broader audience.

Spotify Personas | Listen Together — Collaborative Listening Concept for Spotify

While people might have the same problems or needs, the existing habits determine the existing methods they use to address those problems. Attitudes determine how different people will adopt the products that are designed to meet their needs. Personas combine similar user needs, habits and attitudes and communicate the nuanced commonalities and differences between our users.

Next, how should we represent our listeners?

Representing personas poses a tricky challenge: we want them to be relatable, but they’re not 1:1 matches with real people. Believable human traits and flaws help create empathy with problems and needs. But we don't want groups to be wrongly excluded based on the characteristics we've picked. So finding a balance is a crucial step if we’re to create useful and believable archetypes.

For that reason, we arbitrarily picked genders, names and appearances that matched the range of people we interviewed. While personas exist independently from these traits, they were fundamental to make them memorable as people. And deciding which human characteristics to include in each of the personas was especially challenging. To do so, we reduced the representation of personas to keywords, colours, symbols and energy levels reflecting their enthusiasm for music. This exercise helped us navigate through the variations of poses, facial features, clothing and visual styles we created.

To balance out these specific traits, we used flat illustrations with our brand colours, giving them a more abstract look. Avoiding a too-realistic representation made the material easy to refresh with evolving illustrative styles. It was also much easier to reproduce in high or low fidelity, since sketching a specific pose or picking a colour palette would be enough to refer to a persona.

How did we share our work?

We didn’t wait until our personas were complete before sharing them – we actually started thinking about communication as soon as we began our research. We spent a lot of time testing our asset ideas in pilot workshops. The goal was to integrate with our existing practices seamlessly. And by following our team needs, we crafted a communication strategy for Personas that includes digital assets, physical assets and workshops.

Digital assets

Traditionally at Spotify, we create Google presentations when reporting back research – and sometimes, these get lost amongst all the many other presentations produced! But this time around, we envisioned our personas work to be relevant for at least a couple of years. So we created an interactive website, shared across Spotify offices through announcements and posters. Having a digital source of truth for the research was especially handy whenever we needed to update the study or add new learnings.

Digital assets

A sneak-peek of our internal personas website.

Physical assets

Raising awareness about the personas was useful, but we didn't want to stop there. We wanted to create fun, playful ways for the teams to incorporate them into their workflows. So we created assets that teams could use on their own, whether they were running one-hour mini workshops or design sprints over several days. These assets were made available through our personas website.

Our team hanging on with the personas cardboard cutouts and the card game we've created to share the insights.

Workshops

One of the most powerful modalities for learning that emerged during our pilot workshops was ‘learning by doing’. So the user research team hosted workshops with product teams and helped them to use personas in a way that was relevant to their specific areas.

What was the impact?

Since our teams are so autonomous, we realised right from the start that the personas would be relevant to them all in different ways and at different stages of their work. For that reason, no one was mandated to use personas. Yet, as a reliable, durable and carefully designed information artifact, we’ve seen many teams beyond the product organization adopt them into their work and vocabulary over time – including those across Marketing, Content and Brand.

For instance, teams that want to create features from scratch can now choose their personas, map out the existing opportunities, pick a direction and start ideating from there. Although personas don’t replace user research, they can help us create educated hypotheses and save us time – meaning we don’t need to run foundational research every time we want to explore a new topic within the music listening experience. Our teams can now focus their resources on diving deeper into problems from the level set by the personas.

Equally, when teams are more focused on maintaining features, they can now map out their work and see how different personas would use it. They can create mental model diagrams for different personas and discover how they experience their journeys. And in doing so, they can refine the features to better fit certain ways of listening to music, whilst making sure they don’t alienate others.

Crucially, the personas are slowly becoming a part of our internal vocabulary – a means of helping teams to select and identify which ways of listening are being affected. We can’t optimise a feature for all 200 million of our listeners. So today, it’s common to see teams having their product roadmaps centred around specific personas instead.

A long process, with long-lasting results

Sometimes in order to move fast, you have to move slow. Foundational research initiatives, like the development of personas, take time and are resource intensive. Yet the learnings benefit us long into the future – and here are just a few of them…

  • When in doubt, over-communicate. We need a regular cadence to share details and progress around the organisation – this might add overhead, but it ensures alignment and transparency. We used Facebook Workplace, Slack and emails to keep the stakeholders updated throughout the process.
  • Keep your disciplines close. Our process had to move quickly from behavioural analysis to fieldwork, then straight onto asset creation and scoping needs, attitudes and habits, through the use of surveys. The speed we moved was only possible by having design, user research and data science integrated throughout the process.
  • Know your audience. Adopting new frameworks may be a significant change for some product teams. So we spent lots of time getting to know their workflows, running pilot workshops and inviting them to field work sessions in order to build trust and reduce any potential resistance to change.

As Spotify continues to grow, we expect to expand and adapt our personas for markets outside the US, as well as broadening out our area of study to also include podcasts. There are exciting times ahead and plenty more work to be done – we’re looking forward to the next chapter in the story of Spotify personas

March 26, 2019

The Story of Spotify Personas

Spotify personas are the topic of much discussion by those in the product, design and user research communities. Here, Olga Hörding, Mady...

Read More
Frameworks
Product Management
March 6, 2015
Empathetic vs Sympathetic Product Development

Recently, I was talking to a technologist I greatly admire about different approaches to problem-solving and product development. His arg...

Anshumani Ruddra
&
5
min read
Read More

Recently, I was talking to a technologist I greatly admire about different approaches to problem-solving and product development. His argument (which I strongly agreed with) was that most design, technological and product development in India at the moment is sympathetic in nature and that this is a big problem. It needs to be empathetic.

But what is the difference between a sympathetic approach and an empathetic one? The following excerpt is from an article by George Langelett (who has written extensively about empathy in the workplace and using it effectively to manage employees):

Often people confuse empathy with sympathy. The dictionary defines sympathy as the “fact or power of sharing the feelings of another, especially in sorrow or trouble; fellow feeling, compassion, or commiseration.” Embedded in this definition of sympathy is “commiseration,” which has an element of feeling bad or sorry for the person.

The confusion between sympathy and empathy is unfortunate. The intention of sympathy is to commiserate with the person, in order to try and comfort. By contrast, the goal of empathy is to understand. To empathize is to not only understand the other person’s emotional state or predicament from his or her perspective, but also to comprehend the underlying meaning and causes of one’s feelings and behavior. This misunderstanding of the difference between sympathy and empathy is a serious problem because too often when we feel sorry for a person, we feel better, but the other person most likely will not feel better because no one with dignity wants other people to feel sorry for them.

In the simplest terms, the goal of sympathy is to comfort; the goal of empathy is to understand.

This hilarious video – “It’s not About the Nail” captures this difference well:

Product and technology companies around the world (and especially in India) are following the sympathetic approach:

  • People/ users/ consumers have a problem
  • This is so sad – I feel bad for them
  • I could solve this problem – the solution is so obvious
  • I solved it!
  • I feel so much better now that I have made everyone’s life better

The sympathetic approach brings in personal ego. You want to be the one to solve other people’s problems because it will make you feel better. And while the problem is temporarily solved at a superficial level, its root/ true cause is never discovered.

Sympathetic solutions also often cause much bigger problems down the line. Early settlers who moved to Australia from England in the middle of the 19th century missed certain hobbies and pursuits from back home. One of these was rabbit hunting – Australia had no native rabbit population. An easy and straightforward solution was offered by sympathetic friends: let’s import a few rabbits. So they got about two dozen of them.

They said, “… the introduction of a few rabbits could do little harm and might provide a touch of home, in addition to a spot of hunting.”

This was 1859. Within ten years, even shooting and trapping two million rabbits had no noticeable effect on their population. It is the fastest spread ever recorded of any mammal species anywhere in the world and is the single, most significant factor in mass scale species loss (both flora and fauna) in Australia. {Read Bill Bryson’s enchanting “Down Under” for a more detailed account.}

Perhaps the early settlers needed a new hobby.

If we intend to solve product problems of all shapes and sizes in India (and we have a lot of them), we need to have an empathetic development approach – put aside personal ego and truly understand the problem – not just the symptoms, but the causes.

March 6, 2015

Empathetic vs Sympathetic Product Development

Recently, I was talking to a technologist I greatly admire about different approaches to problem-solving and product development. His arg...

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