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

Tejas Vyas
min read
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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.


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!

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

Deepak Abbot
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]:

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.


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.


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.


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

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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
min read
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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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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
min read
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Business Strategy Framework

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

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

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

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

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

Impact Matrix

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

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

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

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

Impact Matrix

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

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

Impact Matrix

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

Response Matrix

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

Response Matrix

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

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

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

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

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

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


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

May 22, 2020

Covid19 Crisis: Business Strategy Framework (Part 2)

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

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

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

Dr. Ajay Sethi
min read
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Creativity Unleashed

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

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

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

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

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

Seven Successful Business Strategies

1. Reimagine the category

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

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

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

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

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

Housing (part of the Proptiger group that also manages 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. is doing this not only with the help of webinars (who isn’t?) but also by offering video-based consultations. 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, 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!


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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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
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 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; brand examples would be companies such as eBay and Flickr; and lock-in examples would be Sybase (part of SAP) and COBOL (more than 25,000 companies still use COBOL).

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

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

Spiral Effects

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

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

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

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

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

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

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

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

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

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

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

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

Engagement Graph

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Network Effects

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

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

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

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

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

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

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

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

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

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

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

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

Viral Network Effects

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

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

Exchange Network Effects

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

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

Brand Network Effects

PinDuoDuo stands for “shop more together” [link, link, link]. PinDuoDuo launched e-commerce service in September 2015 in the competitive Chinese market to compete with market leaders such as Taobao (Alibaba’s China-focused e-commerce platform), (part of the Tencent group),, 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 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, $39 for, 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

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.


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

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

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

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

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

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

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

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

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

Engagement Graph

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

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

Value Creation: Three Strategies

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

So, how can a startup compete with this behemoth?

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

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

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

Innovation & Disruption

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

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

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

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

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

Innovation Zone 1: Frequency-led Innovation

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

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

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

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

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

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

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

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

Innovation Zone 2: Importance-led Innovation

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

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

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

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

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

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

FEBRUARY 18, 2020

Creative Innovation & Disruptive Innovation

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

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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
min read
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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

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.

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

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: 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
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
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.

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.

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.


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...

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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
min read
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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:

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
Case Studies
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
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

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

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

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’.


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

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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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
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


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.



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


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 –


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.


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
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