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With 22 years experience in building commerce, utility, gaming & financial products for consumers, Deepak has developed analytics & growth
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:
However, the terms get complicated in reality.
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).
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.
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:
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.
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.
With 22 years experience in building commerce, utility, gaming & financial products for consumers, Deepak has developed analytics & growthRead More
One of the hottest companies before the Dotcom bust of 2000 was Webvan. It raised $800 million from Sequoia, SoftBank and several other...
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.
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.
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.
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.
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.
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.
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.
Though this deserves a separate conversation, Tejas touched upon the following during the course of the discussion.
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.
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!
One of the hottest companies before the Dotcom bust of 2000 was Webvan. It raised $800 million from Sequoia, SoftBank and several other...Read More
Ravi Bhushan is the founder of early-stage Ed-tech company, Brightchamps. Before venturing on his own, he was the Chief Product and...
Ravi Bhushan is the founder of early-stage Ed-tech company, Brightchamps. Before venturing on his own, he was the Chief Product and Technology Officer at the PropTiger Group which runs three real-estate properties: Housing.com, Makaan, and PropTiger. Ravi also held the business responsibility of Makaan.
For the sake of context, Magicbricks and 99Acres, are the incumbents of the Indian real estate business. At the start of 2017, Magicbricks and 99Acres were roughly five to seven times larger than the combined traffic of Housing, Makaan and PropTiger. But, in less than two years, by Sept 2018, the PropTiger Group (for the sake of simplicity, referred to as Housing from now onwards — since Housing is the consumer-facing brand and the largest destination within the PropTiger Group) overtook both Magicbricks and 99Acres and became the largest real-estate destination (in terms of the combined traffic). More importantly, most of this traffic was organic traffic.
How did Housing achieve this? What SEO magic did they perform? Ravi shared his secrets during our conversation.
Ravi’s SEO journey into the SEO world started due to “a traffic accident involving Panda in India”! Housing’s traffic dropped by 70% overnight due to Panda penalty imposed by Google. This put paid to the product and engineering investments that the Housing had made over the previous several quarters. Ravi said, “it is painful to have created a beautiful painting and then realizing that someone has put the painting in the dark room and locked the door”! It is important to not only build good products but also to have a clear-cut strategy in terms of how the traffic, especially organic traffic, will come.
The most important and the most impactful SEO activity is “site structuring”. Site structuring ensures that the website is structured in such a way that Google bot crawling the website is provided with the right syntactic and semantic signals about the importance of different sections, pages and content of the website. Site structuring, therefore, focuses on getting the basics right.
First, company should come up with the right information architecture based on the semantic information that the company wants to communicate. The site should be structured such that not only a human being but Google Bot can also understand the importance of different topics / concepts. Each section (corresponding to a topic / concept) should be provided with the right weightage.
To do this, each section of the site should provide coherent meaning and different sections should not overlap too much with each other. For example, a car website can have hub-pages corresponding to the brands (such as Maruti, Tesla, Mercedes, etc.) and, under those pages, hub-pages for various models of the brand. In addition, car website can have a different set of hub-pages for different categories such as sports cars or electric / diesel cars. It is important to structure these pages carefully (as well as link them to the right product detail pages) so that users as well as Google bit can understand the semantic meaning of different sections.
Second, in order to give the right signals to Google and other search engines, it is important to link the pages in a thoughtful manner. Linking pages indiscriminately and ending up with a spaghetti of interlinked pages (including, for example, linking “Contact us” or “Disclaimer” pages from all pages — a common mistake) confuses Google bot and often leads to pages not getting indexed and ranked properly.
Ravi pointed out that linking two pages is nothing but a vote of trust that one passes from one page to another. It is important to link pages by double-checking them from the business priority point-of-view, keyword point of view (importance of specific topics that the company wants to cater to), and from traffic point of view (what opportunities areas are there and how does the company serve those opportunities).
Ravi pointed out that such site structuring experiments can have short-term negative impact (since Google has to decipher new structure and change the earlier set of indexed pages). For example, Housing experienced 30% drop in traffic in the first month after releasing website with proper site structure. However, the traffic increased by 350% in the second month — so, one can get really good results just by doing site structuring properly.
After taking care of hygiene-level activities such as site structuring, it is good to use a prioritization framework that takes ROI into account. One way to compare various experiments is to measure them along four parameters:
First are foremost, one needs to be aware of the given keyword universe and the size of the opportunity — number of search traffic volume one can go after. Next, one needs to consider users’ intent — which helps one to understand the ability to engage with and convert the visitors into customers. Third, one needs to factor in the competitiveness for specific keywords — what’s the domain authority of the competitors, what keywords have they captured deeply, etc. And finally, the cost of the experiments corresponds to the effort (engineering, content, etc.) required to do the tasks.
Given the above, the experiments can be ranked using the following ROI metric:
(Volume * Quality) / (Difficulty * Cost)
This can be applied to various types of experiments — related to a set of keywords, adding platform features (such as images or videos on product pages; or starting initiative to collect UGC content, etc.), or even re-building the platform from scratch.
Unfortunately, almost 70% of time SEO activities are related to minor tweaks such as title changes, meta-tag changes, writing more content, etc. However, by taking the ROI-based approach, one can avoid dealing only with tactical stuff and start picking up more strategic initiatives. Of course, the ROI metric is just an indicator; the exact decision should be made by considering other qualitative aspects such as the business vertical, type of website (transactional or informational, e.g.), platform capabilities, etc. Together with the ROI metric, one can arrive at the most effective SEO roadmap.
Using the above framework, Housing was able to pick the right experiments that helped them surpass Magicbricks and 99Acres, both of which had much higher domain authority. Instead of going after every keyword related to Indian real estate and competing with the incumbents on the “head” keywords (that generate a lot of traffic; these were “property in Mumbai”, “property in Gurgaon”, etc.), Housing focused on the housing project related keywords. In addition, Housing realized that a lot of traffic (as well as higher intent traffic) was in the long-tail keywords such as “2BHK property in Goregaon”, “3BHK Sohna Road”, etc.
These decisions (to focus on specific set of keywords, e.g.) have an impact on the site structure and the information architecture. They also have implications on what kind of platform one needs to build — for example, how should search and navigation function, how should mapping and other infrastructure work, etc. These need to be factored in while calculating the cost of the experiment.
Ravi emphasized that the same analysis can be done by early-stage startups as well. For this, he gave an example of PDFdoctor.com, a passion project that Ravi started after leaving the PropTiger Group. PDFdoctor provides tools to work with PDF documents (like merge or split PDF documents, etc.). As one can imagine, there is a lot of traffic for online tools related to the PDF; also, this is a super-competitive space with several companies working on building tools (and doing SEO for them) for over a decade. However, by prioritizing various activities using the above-mentioned framework, PDFdoctor identified the best candidates to focus on and, as a result, started ranking amongst the top three results globally within 6–8 weeks! Therefore, by understanding what the competitors were focused on and what they were not, it is possible for any startup to compete globally (across multiple languages and countries) and come out as the winner.
Most early-stage founders know that SEO is important but a lot of them are not able to leverage it properly. What can startups do to benefit from the SEO magic?
Ravi suggested that startups have to create the right culture and the right infrastructure in the company to ensure that SEO gets the focus it deserves.
Culture requires understanding the importance of SEO and talking about in senior management meetings and in the company townhall discussions. It also requires empowering a leader to focus on SEO growth and creating a dedicated team or squad focused on SEO. The SEO team should have clear targets and goals (along with dashboards that provide measurability) and the team should be rewarded for both mini-successes and for achieving major milestones. A lot of companies miss this and don’t have anyone responsible for day-to-day handling and growth of organic traffic.
Infrastructure corresponds to infrastructure around log management, A/B testing, etc. This is because SEO should be a proactive game; it should not be played in a reactive manner (reacting to competitors or Google penalty).
Ravi pointed out that Housing had created a separate micro-service for SEO to take care of all technical SEO requirements such as title / header / meta handling, page redirection, content interlinking, content spreading, etc. In other words, all major aspects of technical SEO were supported by a micro-service.
The right infrastructure empowered the developers and PMs to do any experiment they wanted to do in isolation, without impacting the main product roadmap. This enabled the team to run their experiments very quickly and, therefore, become more effective.
The SEO team itself can be part of the product, tech or marketing team; the important part is that (a) it should have SEO-focused developers and product managers and (b) the company should work to ensure that the team doesn’t face roadblocks while taking care of the SEO tasks.
Paid marketing requires understanding of various platforms (such as Google, Facebook, YouTube, Instagram, Linkedin, etc.) and the related marketing tools as well as planning (for example, budget planning, allocating spends across different platforms, etc.). Also, one can get instant results (within a few minutes or hours) regarding paid marketing experiments. Unlike paid marketing, SEO requires longer time period to show results and, therefore, more patience. More importantly, organic marketing and SEO requires a different mindset. This mindset demands product DNA and product-first thinking.
Product DNA is important because, ultimately, Google rewards those websites that are loved by users. So product efficacy is the core of organic marketing and SEO. In other words, as far as the organic traffic growth is concerned, a good product wins in the long term.
To imbibe product DNA, one needs to adopt user-centric mindset. As a result, it is important for the product managers (those who are responsible for the overall product roadmap) to start thinking about SEO items as well.
To avoid the conflicts and to ensure that the SEO does not get Cinderella treatment within the organization, Ravi said that we must treat Google Bot as a user persona!
Why is this important? Ravi said, “imagine who would be the most frequent visitor of your just-recently-launched site? It is going to be Google Bot!” Therefore, one needs to take care of the Google Bot by presenting it with the right site structure, making the site easy to crawl and index, ensuring fast response time with high download rates, etc. If Google Bot finds the site friendly, it would rank the site higher and, therefore, tell the whole world about it and generate a lot of referral traffic!
At the later stages, when the site starts generating good traffic (direct and otherwise) and the product / platform has evolved, then the Google Bot user persona should be considered along with other user personas. At this stage, high quality product (that serves the end-users’ needs) is the most important. If the product quality is high and the end users like the product, Google Bot persona also ends up “liking” the product — even if you do a few mistakes. In other words, one can naturally start treating Google as a channel at this stage and focus on prioritizing user-specific activities (instead of resorting to SEO hacks to rank higher in Google results).
Treating Google Bot as a user persona can ensure that the team views SEO empathetically and applies user-centric mindset to SEO activities as well. It becomes easier to align different features and to consider SEO activities as part of the product and tech roadmaps. As a result, product/tech roadmap conflicts get sorted out.
This, Ravi hoped, would be one of the key points takes away from the conversation.
Ravi Bhushan is the founder of early-stage Ed-tech company, Brightchamps. Before venturing on his own, he was the Chief Product and...Read More
Everyone in the startup ecosystem has heard about the main Flipkart story… and it is a fascinating story because it had many interesting...
Everyone in the startup ecosystem has heard about the main Flipkart story… and it is a fascinating story because it had many interesting characters, a lot of suspense and many sub-plots. Flipkart’s efforts to build a brand that was accessible to all Indians — not just to consumers in metros but also to consumers in Tier 2, 3, 4 cities across India — was a wonderful sub-plot. Shoumyan Biswas spent six fruitful years with Flipkart between 2013 and 2019 and was the Chief Marketing Officer (CMO) for Flipkart starting 2017 and CMO and Business Head of Loyalty, Partnerships & Advertising for Flipkart towards the end. We requested Shoumyan to share details about this aspect of Flipkart’s journey.
The insightful conversation in its entirety can be view below:
Or, you can scan through the main points from the conversation below.
At a high-level, Flipkart’s pre-Walmart acquisition journey can be divided into three phases:
In the first phase, Flipkart had laid for building a strong brand by focusing on providing great customer experience. Flipkart also used Cash-on-Delivery (CoD) to build trust with customers.
Shoumyan mentioned three lessons and guidelines from these early efforts:
 For digital-first brands (and increasing for all brands), “brand” is what one consumer tells another about the company (as opposed to pre-social media days when “brand” used to be what companies proclaimed themselves to be).
What used to be one-way communication from brands to consumers two decades ago has evolved into two-way communication between brands and consumers. Social media has not only provided a voice to the consumers but have also increased their circle of influence. This implies that brands need to consciously shape consumer narrative around the brand.
Based on this understanding, Flipkart’s marketing team had dual charter:
1. Make people transact with the brand
2. Make people to talk about the brand
Flipkart consciously worked to drive conversations about Flipkart amongst consumers.
 Brands — just like two human beings — go through stages of evolution. These stages are:
Unknown → Known → Known for something → Trusted → Loved
While building brands, marketers should not use marketing activities only for tactical reasons (e.g., using TV commercials to build awareness or leveraging discounts to get volumes). A strategic approach is to align the actions with the stage of the brand.
How did Flipkart move across these stages?
How can a brand measure whether or not it is making progress across these stages? Shoumyan mentioned the following metrics:
 Non-digital (“traditional”) brands operate with the think-do-think model; however, digital-first brands need to adopt do-think-do model of execution. This is because, unlike traditional brands (say, FMCG brands), launch of a product can take 18 months. Digital world moves much faster and, therefore, digital-first brands need to “fail fast and learn faster”.
In 2015, Flipkart had ~45% marketshare while Amazon had ~10% marketshare. This changed dramatically in 2016: Flipkart’s share reduced to ~35%, Amazon grew to ~25%. Flipkart was rapidly losing the marketshare to Amazon… and, from the outside, it looked like Amazon might win the war. But then something changed. Flipkart was not only able to hold on to its marketshare and but slowly increase it. What triggered this turnaround?
Shoumyan pointed out that Flipkart started by turnaround by fixing the mistakes that had crept into its consumer strategy. In order to improve unit economics (to move quicker towards profitability) and to enhance its reach, Flipkart had adopted the marketplace strategy for expansion. However, the marketplace strategy (due to lack of oversight and control) caused the user experience to degrade and for consumer trust to get eroded (to an extent). This is what provided a foothold to the competitors.
Flipkart recognized these mistakes and adopted three-pronged strategy to fix this and move forward:
 Getting focus back
First and foremost, Flipkart decided to focus on value-seeking, middle India consumers. As a result of this focus, Flipkart changed its focus on categories, products, etc. Shoumyan pointed out that any strategy has two parts: what you will do and what you will not do. As a consequence of focus on middle India consumers, Flipkart consciously decided to not fight the battle for experience-seeking affluent consumers in Tier 1 cities.
Even more importantly, Flipkart really understood what value meant: it meant more benefits for a given price. And, importantly, it didn’t mean the lowest price and it didn’t mean the biggest discount. As a result, Flipkart explore how to provide more benefits for a given price-point (instead of working towards reducing the price points).
This influenced not only the Flipkart product user experience but also helped identify the right promotional model. It also helped Flipkart prioritize entry into consumer finance and other Fintech products.
As an example, Shoumayn’s talked about “Itne mein itna” campaign during this phase, which emphasized more benefits for a given price point.
 Getting more out of less
To get more out of less, Flipkart used a 2x2 matrix with effectiveness and efficiency. This helped Flipkart track the effectiveness and efficiency of marketing spends across social media (engagement rate vs cost of engagement), performance marketing (cost-per install vs average revenue per install over 14 day period), etc.
Flipkart used this matrix to double-down on channels and activities that were yielding results while eliminating those that were not. It helped to figure out which sources maximized effectiveness while increasing efficiency.
It was also used for allocating marketing (Search Engine Marketing) budget across different categories. For each category, SEM spends were plotted against RPC (revenue per click). Flipkart found that for each category, there are specific levels of SEM spends for which RPC gets maximized; beyond this threshold, the marketing spends yield diminishing returns.
Flipkart used this mechanism to optimize its SEM spends. During this intense competition period, Flipkart found that the competitors — despite spending four times more advertising money — had got only 20% additional boost in revenue.
 Relentless execution
Core values in the marketing team: Creative excellence, Frugality, and Marketing Innovation.
For brand marketing perspective, creative excellence is critical because if creative is good, it helps the company to achieve its goal by spend less money. If the message is sharp, brand can get the same effect with lower budget. The other way to eliminate misattribution is to pick a unique “device” or theme. For Flipkart, kidadults device helped it to cut through all the clutter.
Frugality has two aspects: how to “get more out of less” and how to reduce ineffective spends. Frugality also meant being data-driven about measuring effectiveness of the marketing activities.
Flipkart really pushed the boundaries in marketing innovation and continuously experimented with new things. Shoumyan pointed out that, at one point, almost half of new innovations done by Google and Facebook were done in collaboration with Flipkart. As an example, Flipkart took personalization to the next level by creating more than 3 million personalized video assets. This helped to improve conversion by 45%!
After establishing and retaining lead over Amazon, it would have been tempting to take the foot off the pedal. But Flipkart didn’t do this. Flipkart, instead, consolidated its lead and ensured that its advantage didn’t slip away. What did Flipkart do to become more efficient and more effective during this period?
Flipkart ensured that the — though the victory was celebrated — it didn’t make Flipkart lose the focus or to let complacency set in.
First, Flipkart worked to strengthen the customer funnel and explored how it can engage users better to increase customer LTV.
This was done after segmenting the users and developing a clear understanding of customer requirements. Flipkart used behavioral customer segmentation process. Shoumyan pointed out that any customer segmentation mechanism (based on demographics, psychographics, etc.) can be used as long as it provides Mutually Exclusive and Collectively Exhaustive (MECE) segments. Another important requirement is that the segments should be targetable and actionable.
Flipkart used the behavioral segmentation to divide consumers into 5 segments:
3. Light users,
4. Heavy users, and
5. Super-heavy users.
Behavioral segments were created on the basis of Frequency and AOV (Average Order Value). Shoumyan pointed out that it would be create behavioral segments using contribution margin (in addition to AOV) in order to assess whether the customers were value building or value eroding.
Based on customer segments, Flipkart knew how to engage with the customers and what were the key tasks they wanted to achieve. For example:
For each of these segments, Flipkart created separate marketing programs and defined the relevant metrics. For the browsers and lapsers, Flipkart marketing team designed acquisition programs and reactivation programs. For the light users and heavy users, Flipkart designed upsell/cross-sell program and created loyalty program. And so on. Overall, the goal was to make it easier for Flipkart to acquire users and then move them faster across the customer journey (from light to heavy to super-heavy users). In parallel, the goals was to make the customer bucket less leaky and to reduce the number of lapsers.
Second, Flipkart looked at vertical businesses to leverage them to complement the horizontal e-commerce platform. As part of this, Flipkart expanded to launch fashion, household goods, small and large appliances, baby, grocery, etc. verticals. The goal was to expand beyond the smartphones and electronics, apparel (esp. sarees), and branded goods (such as shoes) verticals.
Flipkart also forayed into non-ecommerce categories such as travel bookings, phone recharges, Flipkart videos, etc. The goal was to either maximize transactions or to maximize time spent with Flipkart. In other words, either more units sold or more DAU (Daily Active Users). Appliances, baby, grocery, etc. verticals were geared towards more units while videos, recharges, etc. were geared toward more DAUs.
Flipkart’s brand journey provides a good template for digital-first brands to chart out their journey. In addition to sharing the frameworks and principles for building digital-first brands, Shoumyan also answered a number of questions from the community members. We will share a summary of some of those in a separate post. In the mean time, if you have any comments and questions about Flipkart’s brand journey or the journey of any other digital-first brand, please do let us know.
Everyone in the startup ecosystem has heard about the main Flipkart story… and it is a fascinating story because it had many interesting...Read More
Vikram Bhaskaran is a distinguished marketer. The Sr Director of Marketing at Chargebee, he headed marketing in Freshworks and FusionCharts.
Chargebee is one of the largest subscription management platforms in the world, driving revenue operations and billing for SaaS and subscription-based businesses.
At the start of the Covid-19 crisis, Chargebee’s traffic dropped by 14% in two weeks — something that most companies across the world also experienced:
However, Chargebee was able to recover quickly in the next two weeks:
In fact, not only did the traffic recover quickly, Chargebee was able to grow its metrics and, in the subsequent two weeks, ended by 20% higher than the pre-Covid levels:
To understand the marketing gears behind their metrics, we spoke with Vikram Bhaskaran, who is the Senior Director of Marketing at Chargebee. He has been with Chargebee since 2018, during which time the company has grown rapidly. Chargebee’s leading marketing metrics in this time have grown a whopping 20X.
Vikram is one of the most experienced SaaS marketers in India, with more than 15 years of SaaS marketing experience. He has handled marketing at some of the most successful SaaS startups in India. He was with Zoho in 2006 (when Zoho was known as AdventNet) and with Freshworks in 2012, when Freshworks — then known as Freshdesk — had just raised its first round of funding. He has been involved in the SaaS space during these formative years and has contributed to the evolution of SaaS marketing in India.
Conventional thinking naturally drives marketing organizations to design progressively more robust strategies as they grow. Vikram adopted a contrarian view and formulated an “Adaptive Marketing Strategy” that is able to evolve rapidly with company needs and market dynamics.
As he describes it, a Robust Strategy is one that is able to sufficiently weather external changes. An Adaptive Strategy, on the other hand, is designed to capitalize on these changes and turn them into opportunities for growth.
Vikram shared that there are 4 elements of the Adaptive Marketing strategy:
Any customer acquisition strategy, let alone adaptive marketing strategy, depends on the buyer. Therefore it is critically important to understand buyer’s needs and goals, and map out their aspirations.
Second, channels correspond to distribution. All post-PMF startups must identify at least one channel that can scale. As the channels hit their capacity, marketing teams need to evolve and discover newer channels.
Third, interestingly, adaptive marketing is built to work with the diversity that is inherent in marketing teams. Marketing teams have artists, data analysts, creators, executors, iterators and optimizers. This is a heterogeneous set of people and adaptive marketing leverages this diversity.
Finally, adaptive marketing needs to be fluid (“flow like water”) in order to be able to quickly respond to market dynamic changes — either induced by customers, competitors or external factors.
The Buyer Persona is the most important aspect of the Adaptive Marketing strategy. Constantly evolving insights about customer requirements enables the companies to react quickly to adjust channels, reorient teams and respond to changing market dynamics.
But how can a company define buyers precisely? In particular, Chargebee has more than 15,000 customers across various industries in 53 countries. These include businesses ranging from early-stage startups just launching their products, all the way to established companies with sophisticated finance and revenue functions.
Within a customer, Chargebee has a multitude of stakeholders: finance teams involved in receivables, reconciliation and reporting; billing teams that need to automate their invoicing operations; product teams that need to drive pricing decisions; sales teams that need to close increasingly sophisticated negotiations; developers that need to implement the platform; Business heads; and CEOs.
With so much diversity across customer segments, roles, and geographies, how does Chargebee define its target customer or the buyer persona?
For Chargebee, this came from deep customer research that involved talking to users and existing customers, studying the first interaction of prospects in sales calls, and analyzing market behavior from search trends. This blog describes Chargebee’s approach to Continuous Customer Development & the steps involved in detail.
Vikram gave a brief overview of how Chargebee is building a brand around “RevOps” (Revenue Operations). The RevOps idea evolved while Chargebee was developing the buyer persona. Chargebee started this exercise by listening to the sales call — especially the first call between the prospects and the sales team. Vikram said that it was insightful to listen to how potential customers described their problems and their pain points. This initial understanding was refined by subsequent 1:1 conversations with customers and prospects to understand what their day looked like and what were their typical workflows. This helped develop a deeper understanding about customers:
The end result was the realization that irrespective of their actual roles, prospects came to Chargebee with the specific pain of solving inefficiencies in their revenue operations just as they saw a hockey-stick growth potential in their horizon.
Defining personas not only helped the marketing team fine-tune positioning and messaging, but also made it possible for the whole organization to build, sell, support and engage the right customers.
But more important — clarity about the target persona also had a wonderful side effect: happiness! The org-wide focus and clarity made it even more satisfying to plan and visualize the value they were adding.
Three pillars of any marketing team are: research, creation, and distribution. And within this, distribution comes down to the mix of channels, and how well the team is able to leverage them.
Within the first 6 months of deploying the Adaptive Marketing Strategy, Chargebee saw a 3X growth in demand. And, as mentioned earlier, over the next 2 years, these leading metrics have grown over 20X.
Vikram broke Chargebee’s approach to channels into 2 frameworks: Discovering Channel Fitment and Assessing Channel Capacity.
Channel Fitment corresponds to evaluating whether a channel is the right one for acquiring the target personas. Channel Capacity lets the team evaluate the continued potential left to tap within the channel.
As an essential platform required by every high-growth SaaS business, Chargebee has the advantage of playing in a market with huge inbound demand. As a result, channels like Organic content and Paid search, in addition to inbound category creation through branding and thought leadership have been the most scalable channels.
As the business grew, Chargebee scaled into additional channels to augment specific customer segments (like Account Based Marketing for Enterprises), while beefing up its community focus for early stage businesses with high growth potential.
What is the best way for a startup to explore a new channel? Vikram said that Chargebee uses what he calls the 3S Framework for this: Scout, Scope, and Snipe.
In this Scout Phase, Chargebee runs broad low-investment campaigns to test a new channel. These could be paid ads, content-based activity, etc. The idea is to identify a few successes that can be explored further, without expending significant budgets.
As specific channels & campaigns demonstrate potential, the Scope Phasefocuses on improving the quality of the results. In paid search, for example, this is done by launching specialized campaigns with narrower focus on just keywords or activities yield better results.
Finally, the Snipe Phase serves to turn the activity into a repeatable process. In the context of paid search, this involves setting negatives and having exact matches that can be scaled for predictable results.
These three phases help continuously drive new campaigns & channel possibilities, while still ensuring quality & ROI.
In the context of Channel Capacity, Vikram referred to the “Law of Shitty Clickthroughs” proposed by Andrew Chen [link], which refers to the diminishing returns provided by a channel as volume scales up.
Even as the quality goes up, how does one make sure that the quality doesn’t get compromised? How can businesses pick channels with sufficient capacity so that they can defer the curse of shitty clickthroughs?
Chargebee uses the following Channel ROI Framework for this purpose:
Clearly, the goal is to find channels that are “Leaders” (i.e. provide high quality leads / customers at low cost) and to stay away from high cost but low quality quadrant. Vikram mentioned that Bleeders (such as low-cost paid ads) are interesting because they can provide volume at low cost. However, marketing teams need to closely monitor their Bleeders, and measure their downstream ROI in dollar terms. Finally, Truffles are likely the largest segment — composed of high quality campaigns that also require more effort and budget. Typically, Truffle campaigns have to constantly out-bid competitors, but they are good indicators of market size and our position in it.
For Chargebee, Account-based Marketing (ABM) has grown from a Bleeder into a Truffle channel, while Community-based initiatives shift between Bleeders and Leaders.
Focus on Channel Fitment and Channel Capacity allows marketing teams to quickly double down or back away from initiatives.
In a fast-growth business, the focus of the marketing team continuously shifts from Volume, to Velocity, Predictability, and Variability.
Vikram pointed out the moving targets that a marketing team needs to solve for, as it evolves from “Robust” to “Adaptive”:
Note that the first three stages correspond to linear growth (with respect to the marketing spends). Building brand and developing an emotional connection with customers provides some amount of non-linearity, while investing in customer engagement unlocks network effects. (We have earlier talked about how companies that don’t have inherent “networking” play can also unlock network effects [link].)
Each of these activities, clearly, are quite different from each other. In order to succeed, marketing leaders should structure their teams around specific focus areas, with metrics and inter-locking contracts. Vikram (elsewhere) illustrates this with the following representative example:
Given the evolving or moving target for the overall marketing team and differing success metrics for various marketing sub-teams, what holds the marketing team together? It is the clarity about the buyer persona (what are customers’ needs and goals) and the organization’s needs and goals.
Business needs are often captured by the “north star metric”. For Chargebee, the core indicator of success is “Net Retention Rate”. This goal is shared by the whole company and helps Chargebee track the speed at which the customers are growing. It helps the marketing team to also evaluate the leads, MQLs, SQLs, etc. in a more consistent manner.
In order to be truly Adaptive, marketing teams need to decentralize strategy. Vikram explained how every marketer in Chargebee is empowered to drive strategy with minimal oversight. He explained 2 systems that the team uses to ensure they stay aligned without stepping on each other:
The final and most critical aspect of the Adaptive Marketing strategy is the ability to not resist the constantly evolving market dynamics but to rather turn it into an advantage.
Vikram builds on Bruce Lee’s quote (Be like water) to describe how an Adaptive Strategy must flow freely without resisting change in order to stay flexible and evolve rapidly.
For this, Chargebee built a system of Indicators, Triggers, and Strategies. Chargebee takes the org-wide look at the Indicators on a weekly basis (while the marketing team tracks the Indicators on a daily basis). As indicators hit different thresholds, they trigger a specific strategy set that the team operates on.
The dashboard with Indicators, Triggers, and Strategies is shown below:
Chargebee has four top-level indicators:
These Indicators have different impact on the company’s north-star metric: NRR (Net Revenue Retention). Aligned with NRR, Chargebee has a number of Triggers. For example: Estimated Bookings, Cost of Estimated Bookings, Estimated Payback Period, etc.
As these triggers flip, Chargebee’s marketing team already has the strategic priorities and initiatives in place to quickly shift directions.
Chargebee’s marketing team further structures all their initiatives into 7 categories ranging from Critical Necessities to Discretionary by just answering 3 questions:
Depending on the Yes or No answer for each question, there are eight different scenarios. Each scenario provides guidance for the strategy:
Along with the operational rigor, Indicators, Triggers, and Strategies provide the mechanism for Chargebee to respond quickly to the evolving market dynamics.
Vikram emphasized that scaling marketing teams should not waste their time building robust marketing strategies. Robust market strategies attempt to withstand external disturbances and resist changes, which makes them quickly lose touch with reality.
First, in a fast-growth business internal teams, processes and priorities change so rapidly that forcing robustness could isolate the marketing function from the rest of the organization.
Second, while the Covid situation is unprecedented, markets and environments change directions even otherwise in unpredictable ways. While robust strategies might provide immediate insulation from these turbulences, the incremental shifts in the environment render the strategy obsolete quickly.
Eventually these deep, well thought strategies end up living only on the paper (or spreadsheets) while the actual marketing plan gets changed ad-hoc. Teams lose sight of the bigger picture, stresses run high, and marketing leaders find themselves struggling to plug the leaks in their strategy.
Instead, Vikram suggested organizations to focus on fluidity instead — by designing their marketing strategies to be Adaptive.
However, an Adaptive approach is as much an organizational mindset as a strategy — in order to be truly adaptive, marketing teams should account for
Based on these, an Adaptive Marketing Strategy provides a natural way to keep the marketing plan flexible to identify new realities and capture opportunities. Adaptive Marketing, therefore, is the ideal way to build the marketing function at every company: from an early-stage startup to growth-stage startup and even large enterprises!
Vikram Bhaskaran is a distinguished marketer. The Sr Director of Marketing at Chargebee, he headed marketing in Freshworks and FusionCharts.Read More
Anuj Rathi (VP for Product, Revenue and Growth) is a senior product and growth leader at Swiggy. Anuj joined Swiggy in Aug 2016 when...
Anuj Rathi (VP for Product, Revenue and Growth) is a senior product and growth leader at Swiggy. Anuj joined Swiggy in Aug 2016 when Swiggy is handling approximately 1.5 million deliveries per month. In Oct 2019, Swiggy announced that they were handling 1.5 million deliveries per day. In other words, Anuj has been part of the journey when Swiggy achieved 30x growth in 3 years!
Swiggy has been able to achieve this rapid growth not only with the help of exceptional talent (across engineering, product, marketing, operations, etc. teams) but, more importantly, with the help of a culture that is strongly anchored on “customer-backward thinking” (with high customer empathy), first-principles thinking, fast execution focused on raw problem-solving skills (complemented by strong data-driven experimentation), insane passion, and grit” (in Anuj’s words).
To understand Swiggy’s product and growth formula, we invited Anuj for the 2nd Masterclass by ProductGrowth.org. Before the session, we requested community members to tell us to know what they wanted to hear from Anuj and got the following response:
Based on the community feedback (and specific questions from the community members — both before and during the session), we had a conversation around building habits, driving scale, and effective growth architecture. It was a power-packed conversation, full of insights and suggestions about how other companies can drive efficient and sustainable growth. You can listen to the full conversation below:
Or, if you prefer, you can scan through the summary below. If you have any questions or suggestions, please do let us know by responding to this story below.
Mindful of the fact that the startup community (indeed, everyone across the world!) is grappling with the unprecedented Covid19 crisis, we started the conversation by discussing how Swiggy was responding to the crisis.
This is an important question because, in the times of crisis, both the JTBD (Jobs to be Done) and GTBA (Goals to be Achieved; the “why” behind the JTBD; for example, the importance a person attaches to JTBD and other deeper requirements; see here for more details) can change dramatically. Given this, how can a startup or company respond quickly?
In this context, it is useful to highlight that Swiggy has reacted very quickly during the current crisis: starting with “zero contact deliveries”, Swiggy has launched new services to meet evolving customer needs. For example, Swiggy Stores for groceries and Swiggy Genie for instant pickup/drop service (to get groceries and other essentials from nearby stores). Given this, it is interesting to understand what has enabled Swiggy to iterate quickly and respond effectively to the rapidly evolving Covid19 crisis?
Anuj provided a two-part answer to this question.
The first one was related to customers-backward thinking. Swiggy uses a framework called “Accepted Customer Belief” (ACB) for this purpose. ACB is typically used by the marketing teams to understand how customers look at a product (and the broader product category). ACB is used to understand whether the company understands customers or not — especially, the target consumers’ frustrations of their unmet needs.
Swiggy has found that ACB is a wonderful tool for imbibing customers-backward thinking in the product teams. ACB also enables product teams to capture evolving customer needs and wants quickly. For example, before the Covid19 crisis, ACB for college students (to pick a specific persona) included: need for not-expensive food, extremely fast delivery, and desire for discounts. However, after the spread of the Covid19 crisis, their ACBs changed and were instead focused on safety (of the delivered food and the delivery partners). In addition, the speed of delivery became less important though the need for not-expensive food continued. In other words, though JTBD remained the same, the underlying requirements (GTBAs / expectations) changed significantly. In some sense, the changing environment needed a “new product” and Swiggy, indeed, iterated quickly to validate the PMF for this new incarnation of the product.
ACB framework, therefore, provides a quick and methodical way of understanding customer requirements — esp. the non-functional requirements underlying the tasks/activities they want to fulfill.
The second part was related to how to structure and organize teams so that the startup has the agility required to react quickly to a crisis (as well as the changing market dynamics, in general). We will take a look at this towards the end of the article.
We started the conversation on habits by pointing out the inapplicability of the popular Hooked framework (by Nir Eyal) for building habits for a majority of companies. The Hooked framework suggests that there are four levers for creating habits: Trigger — Action — Variable rewards — Investment. However, some of its levers (such as “action” and “investment”) can’t really be tweaked around much for a majority of the usage scenarios. Moreover, “variable reward” is also not typically available — in a lot of use-cases, the certainty of results (for example, “food delivery within 40 minutes”) is more important than the variability of rewards. In fact, non-variability and consistency of delivery is the “reward” that is valued by the customers. So, one is left only with a “trigger” as the habit-building lever! Perhaps this is the reason why Hooked framework inadvertently triggered a deluge of much-too-frequent and bordering-on-spam push notifications (“external triggers”) that did more harm than good to the companies trying their best to create the elusive habits.
Anuj pointed out that “growth hacking” — another Silicon Valley fad — is not useful either; it is typically associated with the narrow definition of growth: tactical growth focused on increasing DAUs, MAUs, etc. by running incremental experiments.
So, how can one build habits?
The starting point for building habits is to understand customer requirements deeply. As discussed above, this needs the company to understand both the JTBD and the “core assumptions” (ACBs) of the customers.
Based on this foundation, the first step is to build a product that works for consumers. This is critically important because several companies rush to increase usage by 20–30% (via growth hacking, etc.) before doing this.
Once this has been achieved, the next step is to understand the natural frequency of the activities under consideration. Now, Swiggy can consider itself to be either in the business of delivering restaurant food to customers (for example, as a substitute to eating-in at a restaurant; this would happen, say, 5–10 times a month) or in the business of delivering food (which can happen much more often because customers eat meals 3–4 times per day or 90–100 times a month).
In fact, one needs to look at the natural frequency together with the target persona (or segment). Target persona can incorporate not only behaviors and segments but also the customer’s value (i.e., CLTV). Note that the CLTV should not be the value the persona is providing right now but the maximum value they can ever provide (which, itself, depends on the maximum frequency of usage for the persona). Swiggy, for example, understands that there is a large difference between students and young professionals who are Snapchat users versus those who are not — these two sets vary significantly in terms of their CLTV (or “aukaat”, the colorful term used by Anuj). Swiggy goes further and understands that the same user can have different behaviors at different times: a user can be “speed seeker” on weekdays (while ordering food from the office) or “discount seeker” on weekends (when ordering lots of food for the whole family)!
Once the natural frequency for each persona is understood, the next question becomes: how can we help each persona increase their usage so that they get close to their natural frequency (which is the maximum usage frequency for the persona)?
In this context, Nir Eyal’s “triggers” become just one way of getting there. Another way could be building the best possible product and consistently delivering on the brand promise. This, then, gets people to start talking about the product/service and help spread the word. This can help make using a company’s product a social norm and, therefore, lead to building a habit!
With this view, habit is all about increasing the frequency of product usage until it reaches natural frequency. Habit, therefore, becomes a means to an end; the end remains to deliver value to customers and making sure their needs and goals are fulfilled.
Anuj used the example of the Super subscription program to explain how Swiggy actually leverages the above approach to habit building. Let’s look at the journey in detail.
The first question Swiggy asked was: what is preventing customers from ordering at or near natural frequency? For this, they spoke to customers who were “super users” — that is, who had been ordering fairly frequently (say, 15–20 times a month). With the help of user research, they discovered that almost all of them hated the delivery fee: either they didn’t like the very idea of delivery fee (especially because they realized they were giving good business to Swiggy) or (worse) they hated the bill shock they got at the end of the ordering session — especially when Swiggy levied higher surge delivery prices.
Second, while starting to explore a subscription program, Swiggy also looked at several variants of subscription programs: from a single-tier (paid) membership program (like Amazon Prime), multi-tiered membership program (like airline miles programs), earn-and-burn programs (like Flipkart Plus), and so on.
Third, with the goal to increase the frequency of usage, Swiggy looked at possible benefits that the membership could offer. For example, these were: faster delivery promise, higher discounts from customer’s favorite restaurants, higher overall discounts, free delivery without surge fees, etc.
Fourth, to understand how customers would react to these, Swiggy designed landing pages for various options and used them to simulate experience. Swiggy then tested various options with a small set of representative customers. Swiggy not only looked at the quantitative data to analyze how super users engaged with the simulated options but also interviewed them to get a qualitative feel about their reactions.
Finally, to choose between these options, Swiggy applied the RICE framework (where R = Reach, I = Impact, C = Confidence, and E = Effort) to perform a cost-benefit analysis and to pick the best candidate. For example, RICE analysis revealed that “faster delivery promise” not only required a lot of effort but also implied that faster delivery for super users might worsen the experience customers (because the delivery partners would get allocated to serve the super users and, therefore, might increase the delivery times for other customers). This implied that this option not only had high ‘E’ (effort) but also low ‘C’ (confidence).
Based on the above sequence of steps, Swiggy designed and launched with the current version of Swiggy Super with unlimited free deliveries, no surge fee during rains or high demand, and “free delights”.
How was Swiggy able to scale 30x in 3 years? What are the frameworks and playbooks that might be useful for others?
For the sake of the context, it is worth recalling the market landscape for the “food delivery” business in 2016. Following on the heels of the rapid growth of e-commerce adoption in 2015 and 2016, “food-tech” industry was super hot in 2016. There was strong competition amongst several energetic and skillful players. For example, there were companies such as TinyOwl (which provided beautiful user experience), Ola Cafe (which had the reach of Ola), FoodPanda (which had strong financial backing and access to international knowhow), etc. Also, UberEats was around the corner. The “incumbent” was Zomato, which had built India’s largest platform for restaurant reviews and ratings (and, therefore, had built strong network effects with “insurmountable moats”, using Warren Buffet terminology).
Therefore, there was an immense pressure on Swiggy to scale fast — while also trying to carefully balance the growth of 3-sided marketplace consisting of customers, restaurants, and delivery partners.
So, how did Swiggy grow 30x in such an intense environment?
Anuj explained that Swiggy did this by first figuring out the growth drivers and then deeply understanding the growth equation of its business.
What does this mean?
For Swiggy, its growth equation indicated that supply-side capabilities needed to be built and stabilized before unlocking the demand. More specifically, Swiggy discovered that the demand (and conversion rates, for example) was correlated with the density of restaurants in an area. For example, conversion rates were low when there were less than 80 restaurants in an area, the conversion rate increased as the restaurants increased from 80 to 225 and beyond; and, interestingly, conversion rates started falling if there were too many restaurants in an area (due to additional cognitive load as a result of too many choices, it turns out)!
Therefore, to launch in a new zone, Swiggy needed to first onboard ‘x’ number of restaurants and ‘y’ number of delivery partners and then kickstart the marketing engine to generate demand (which needed to generate at least ‘z’ orders for the engine to continue working smoothly). Doing so ensured that the customers had a great experience right from the start!
For a new zone (and a new city) launch, Swiggy uses a framework that Anuj referred to as ADS where:
Other marketplaces might also find variants of ADS to be useful to connect the supply-side drivers with demand-side drivers. This is because ADS facilitates a combination of supply-side analysis with demand-side behavior. Swiggy has found that ADS is strongly correlated with business metrics and helps to predict consumer behavior.
Anuj emphasized that Swiggy has always taken a customer-centric approach while scaling; Swiggy had resolved to scale only when the company was sure that it would not provide a poor experience to customers. This is why Swiggy had limited its operations to only 7–8 cities during the first few years of its journey.
How can other startups mimic Swiggy’s exponential growth trajectory? Anuj mentioned that once the growth equation is understood by a startup, its scale equation and growth roadmap would become clear as well. It becomes clear which levers need to be pulled to achieve scale.
Anuj also pointed out that startups should keep going back to their growth equation in order to continuously improve it. A startup’s growth equation keeps evolving based on customer behavior changes, macro-economic changes, market dynamics changes, etc.
We didn’t explicitly discuss Swiggy’s product-led brand activities. However, as part of Habit and Scale discussions, a few things did emerge that are relevant from the brand perspective.
It is important to highlight that brand should not be treated as synonymous with the brand logo or brand marketing campaigns. Brand, instead, should be considered as the tangible and verifiable promise that a startup makes to its customers and, subsequently, the product, operations (logistics) and other activities that the startup does to ensure that it consistently delivers on the promise.
ACB, that Anuj referred to, is one of the tools used in the marketing function to gain insights into why consumers do what they do. By acknowledging consumers’ point of view with understanding and empathy, startups can put together a tangible promise that not only highlights the benefits of the product but also gives them Reasons To Believe (RTBs, in the marketing speak).
Swiggy, right at the start of its journey, realized that the food delivery product needed a clear and simple articulation of its brand promise. Swiggy realized that consistency of delivery (within the promised delivery times) would help it build an early differentiator. Towards this, Swiggy launched the “Swiggy Assured” guarantee with “on time or on us” promise backed by “25% cashback” if the food wasn’t delivered within the promised duration (typically 30–40 minutes).
By consistently delivering on its promise, Swiggy had laid the foundations for building a strong brand. By ensuring awesome customer experience, Swiggy was able to build trust and unlock growth in its initial years. Anuj pointed out that Swiggy didn’t need to do any digital marketing for the first few years.
One of the observations that can be made based on our conversation was that brands can be strengthened by understanding the ACBs of the “super users” (i.e., the most engaged users). Swiggy did this while developing the Swiggy Super program; Anuj pointed out Swiggy spoke with several loyal customers while exploring options to recognize and reward the super users in the most effective manner.
An insightful side story shared by Anuj during the conversation was the unexpected benefits of running an India-wide brand marketing campaign in 2017 (when Swiggy was present in only 7–8 cities). The highly visible campaign resulted in “market spillage” and the company received eyeballs and visibility in the cities where it was not present. As a result, the campaign triggered consumers across India to download the Swiggy app to explore the service. The number of downloads from various cities, in turn, revealed latent demand across different cities. This provided useful signals for Swiggy to decide the order and priority of launching its operations across cities! Combining this demand-side metadata with a replicable go-live playbook (based on the ADS framework), Swiggy rapidly expanded to more than 500 cities by the second half of 2019.
How can a company structure and organize its teams so that it not only it can unlock and achieve its growth potential but also be agile and respond quickly and thoughtfully to the changing market dynamics? What has helped Swiggy to react quickly to a crisis such as Covid19?
Anuj explained the team architecture that provides a strong growth-oriented foundation to Swiggy (despite having grown rapidly over the last few years) and helps Swiggy to be extremely agile. To build and organize the teams, Swiggy looks at five parameters:
Depending on the variation of needs across these five parameters, Swiggy has five different types of teams within Swiggy:
1. Core teams: work on platform capabilities (for example, search and logistics capabilities, for example) and drive medium-term projects (such as Swiggy Pop, Swiggy Super, etc.).
2. X teams: churn out quick, incremental experiments (for example, Swiggy “takeaway counter” at airports).
3. Growth teams: explore adjacencies and other growth avenues. Growth, for example, can come from category adjacencies (for example, grocery delivery), geographical adjacencies (for example, international expansion), JTBD adjacencies (for example, bike taxis), and so on.
4. Big bets (new businesses): explore new business opportunities and respond to evolving (macro-economic, competitive, etc.) climate.
5. Labs: explore moonshot initiatives and very long-term strategic projects; for example, exploring using drones, noddle-making machines, etc.
Note that the above teams are not “two-pizza teams” or “agile pods” or “scrum teams”. Unlike these team structures (which are used to provide agility within product-tech teams), the above is more deliberate and thoughtful team architecture that provides both short-term agility and long-term competitive edge to the company.
Anuj highlighted the differences with two-pizza teams, et al by explaining that each of the above teams has different DNAs. For example:
Core team, for example, works with lots of data and thorough while Big bets team has to work with much less data and lot of intuition. Due to different charters and different focus areas, each of these five teams, naturally, have different DNAs. Since there is clarity in each team’s charter and clear expectations from each team, it is possible to pick the right processes and right metrics for each team.
Swiggy Pop and Swiggy Super, which were handled by the Core team, were thought-through and tested products when they were launched. On the other hand, Swiggy Stores and Swiggy Genie were built and launched quickly so that the product/service offerings could be quickly iterated upon and improved based on customer needs and feedback.
This team architecture makes it easier to staff various types of teams appropriately. To allocate resources appropriately across different teams, Swiggy decides annual and quarterly goals/targets and, depending on that, figures out initiatives and projects needed to meet those goals. This, therefore, helps to decide how to allocate product-marketing-tech resources across various teams. Anuj recommended referring to “Team Topologies” (link) book for more details in this regard.
This team architecture is what enables Swiggy to respond quickly to evolving marketing situations. And, it is this team architecture that enabled Swiggy to move so swiftly during the Covid19 crisis: it was able to make sure that the relevant teams were resourced properly and supported appropriately.
There are only four levers of growth for any company [link]:
In order to devise a growth roadmap, first and foremost, a company needs to understand its growth equations. Anuj repeatedly emphasized that PMs should strive hard towards figuring out and refining their growth drivers/levers as well as the growth equation. Anuj explained how Swiggy uses ADS framework for this.
Next question is: how does one come up with the right growth roadmap?
Anuj pointed out that organizations where business goals are set by the “management” and then cascaded down to teams across the organization are not able to perform optimally. If the goals flow only from top to bottom, the company is not able to leverage full potential of PMs (as well as engineers and other operators in the company) that work in the trenches and have a more accurate understanding of the ground realities as well as more realistic view of future possibilities.
Anuj emphasized that the best teams (especially, product and growth teams) build roadmaps and set goals using a combination of top-down and bottom-up discussions. Swiggy, for example, starts with a bottom-up process where teams come up with their ideas and suggest possibilities. Product leaders collate these ideas and convert them into projects. The projects are used to devise various growth scenarios. These scenarios are discussed with the senior leaders where they are considered in the context of strategic directions (such as customer experience goals, future goals and targets, competitive pulls-and-pushes, fundamental requirements, etc.). Based on the strategic directions, all the proposed projects are classified into different buckets, their tradeoffs are discussed, and prioritized appropriately.
OKR is one of the most popular techniques used by companies worldwide to merge top-down cascading and bottom-up project propagation. In this context, we discussed Swiggy’s experience with the OKR process.
Anuj pointed out that OKR planning doesn’t work well for Swiggy. This is because the growth equation at Swiggy is not simple — various growth levers are dependent of each other; in fact, various growth levers are deeply intertwined with each other.
Given that Swiggy is a 3-sided marketplace, its growth equation depends on all three moving parts. The three sides of the marketplace themselves are highly interlinked — metrics of different sides are closely tied to each other and every metric has potentially large impact on the overall success metric.
Anuj suggested that OKRs work well if a company has simpler growth equation with independent growth levers. Independent growth levers make it possible for different teams to have independent and complementary goals. Independent goals and metrics — especially when the metrics are additive (and not interlinked and interdependent) — make it possible to split company-wide goals into sub-goals and to cleanly distribute the sub-goals to different teams. Recursively, each team is also able to map its goal (the sub-goal owned by it) into smaller goals (and subsequently into potential projects). Of course, if there are some dependencies, it is possible to ensure that the teams talk to each other and resolve the dependencies.
Marketplaces with interlinked parts (and interdependent metrics) render OKR process ineffective. Anuj referred to this as the “butterfly effects” and, as an example, mentioned that a small variation in earnings per hour of delivery executive has been observed to have an impact on conversion rate (on consumer side)!
Due to the intertwined nature of its products/projects, Swiggy looks for win-win-win while evaluating possible projects. Each project is evaluated from the perspective of whether it would provide a ‘win’ for consumers, a ‘win’ for restaurants, and a ‘win’ for delivery partners.
Consider the example of Swiggy Pop, which is Swiggy’s offering of single-serve meals. Customers ‘win’ because of fewer choices and the ease-of-use. Restaurants ‘win’ because Swiggy provides predictable order requirement with partner restaurants in advance. Delivery partners ‘win’ because they are able to batch more orders and do more deliveries. And, by delivering value to each of its stakeholders, Swiggy ‘wins’.
Anuj pointed out that it is not easy to pick one or two metrics for projects such as Swiggy Pop. This is because the success of Pop relies on all parts of the 3-sided marketplace. Moreover, it is not possible for any single PM to own such a product. Projects such as Swiggy Pop needed end-to-end teams (and not just ‘two-pizza teams’ spread across product and tech teams).
Finally, Anuj suggested that it is good to consider product roadmaps with three different horizons:
Anuj Rathi (VP for Product, Revenue and Growth) is a senior product and growth leader at Swiggy. Anuj joined Swiggy in Aug 2016 when...Read More
In the present times, there has been a paradigm shift in the startup world from ‘growth at any cost’ to ‘product-led growth’ or growing...
In the present times, there has been a paradigm shift in the startup world from ‘growth at any cost’ to ‘product-led growth’ or growing sustainably. By product-led growth, we mean using a product as a vehicle for growth. It is a growth strategy whereby companies emphasize on improving their products to make them more customer-centric. When a product caters to users, it automatically witnesses a boom in the adoption rate, which, in turn, soars growth and success.
Before looking into the most powerful strategies and stages of growth, let’s first recap the elements of growth. Simply put, growth is just a function of acquisition, retention and monetization. When a product is rolled out, it should acquire quality customers to become a market-fit. However, acquiring new consumers is just half of the work. we should equally focus on retaining as many customers as possible and ensuring they are actively using the product. That’s because when you acquire a customer, you ensure 1 purchase but it is retention that ensures repeat purchases. Thus, you should divert all your efforts in acquisition and retention to grow profitably. Now let’s understand each step through real-life experiments and examples of giants like Uber, Bounce, Lyft and Dropbox.
When you are building a product, you need to reach out to the right users so that they can get onboard. So, we asked our experts what they did, what worked, and didn’t work in their respective journeys:
In the early days of Uber, the design team aimed at product-led growth by focusing on acquiring more drivers. Onboarding was a key area to achieve growth . Getting new drivers made it possible to increase operational cities and doing it through a product guaranteed exponential scale. Hence the product led driver growth was key to uber’s success
While carrying out research, they found that complex documentation and legal processes were major bottlenecks that prevented drivers from getting onboard. So, to reduce the friction, Uber’s driver growth team planned to make the process of onboarding seamless for new drivers by reducing their efforts. They bifurcated the onboarding process into small steps instead and focused on executing one-step-at-a-time.
Good experiments advance product strategy. Bad experiments only advance metrics. [reforge]
Measuring the right success metric was crucial to Uber’s success. Instead of falling in the pitfalls of the day, they set up their north star metric as the first trip. For the team growth wasn’t just the sign up but it was the entire early lifecycle of the user. The way Uber’s product team defined it was the first 28 days. This lead them invest in levers for growth such as driver education, reducing anxiety etc. leading to a string of successful experiments.
First goal for the driver growth team was to reduce the number of people dropping off during the process. The second was investing in referrals. When a driver is referred by somebody, they retained higher since they knew somebody that’s already on the platform which in turn reduced the anxiety involved. Beyond organic and paid acquisition Uber’s team discovered that referrals were a huge channel for acquiring high quality drivers. This led to them investing significantly in referrals and became a mainstay of Uber’s growth strategy.
A similar yet a more on-brand strategy was adopted by Lyft where a buddy was allocated to the rider to reduce their anxiety before taking an actual drive. They also laid focus on valuing behavior and on creating a friendly environment for the users.
The acquisition strategy of Bounce is also in the same vein to Lyft and Uber. In addition to taking the first ride, the time to take the first ride (time to activation) became a key metric for bounce. This was because of user’s behavior and how they discovered the product. They aimed at making onboarding seamless for the riders by simplifying the KYC process with the only requirement of Driving License from driver’s side.
Pulling this off well required innovation from the team during document upload to both make it intuitive and educate the user along the way on the right way to do it. Since this wasn’t possible using native camera, their engineering team built a custom camera that enabled them to do just that.The team further made an attempt to reduce the time between KYC verification and first ride through integrating with state databases making it seamless.
By focusing on reducing as much cognitive load as possible during onboarding enabled Bounce and uber to activate a lot of customers in their early stages setting them up for hypergrowth later.
Congratulations! You now have customers! What do you do next? Ideally you want them to come back often and spend more.
Now the question that arises here: How can you retain customers and spend less doing it? Several growth drivers are available for fast growing companies focusing on retention. One of the fundamental drivers is Habit.
A habit is formed when the customer uses your product repeatedly where their natural frequency of doing a particular task is strongly associated with your product or brand.
Bounce experimented with habit formation to improve retention in its early days. The team focused on understanding its existing power users and launched quick experiments in order to increase the frequency of usage. The key goal here was to inculcate a habit of using Bounce for the everyday commute. Furthermore, the product team trained themselves to call existing customers and take feedback from them. Sruthi recollects how the idea of putting together a VIP experience team was conceived and executed within a day. The team focused on understanding the friction and obstacles that prevent a habit from forming. The early customer feedback showed that customers wanted to see improvement in the condition of bikes. So, they emphasized on improving the quality of their services to gratify existing users.
While frictions were being addressed, another formula to aid habit formation is giving the right trigger and reward. In order to achieve this through product, Bounce retention growth team introduced a rewarding system whereby they gifted scratch cards to their customers on completing habit forming quests such 5 rides each week, Consecutive good parking, etc. This proved to be very successful in improving bounce’s retention in the early days.
Lyft is on pace to surpass Uber according to data shared by Apptopia.
This is driven by significantly better retention. Lyft’s retention strategy is very centered on the user and they have operated with a long-term vision. Its focused and measured by user lifetime value. This has enabled them to experiment in the short term that compound the results over time towards their overall strategy. One of the fundamental levers Lyft decided from early on was focusing on improving the job space (i.e. transportation and mobility) as a whole than bounding themselves within ridesharing.
For example, Lyft’s team added all transportation options that include the likes of public transportation in their app. While this was counterintuitive, it helped Lyft become top of mind for their customers when they want to commute. This was a powerful foundation for retention.
Lyft also decided to not build a traditional loyalty program. They went with a membership program instead. This was a deliberate decision as the team wanted to drive value for the customers every month than consistently. The membership program itself is an amalgamation of both monetary and non-monetary rewards. They introduced several perks for those users who enrolled for membership. The membership perks include perks like priority airport pickup, free 30-minute bike/scooter ride and 15% discount.
By focusing on keeping the user engaged Bounce and Lyft were able to achieve significant advantage by building a habit. Products that enable forming of habits have aspired to fulfill the natural tendency of users to do a particular task or job. This sets up ideal conditions for sustainable growth loops that keep paying dividends for a long term.
We have compiled the key insights from our first masterclass. I hope you have gained various insights from the stories from people who championed growth at Uber, Lyft, and Bounce. There are more behind the scene stories where we dived deeper in to network effects , referrals, and the elusive product market fit. To get access to all this exclusive content and future masterclasses, sign up at productgrowth.org and follow us on twitter: @product_growth.
In the present times, there has been a paradigm shift in the startup world from ‘growth at any cost’ to ‘product-led growth’ or growing...Read More
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