Driving 100 million users to adopt digital payments

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

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

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

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

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

However, the terms get complicated in reality.

For example:

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

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

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

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

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

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

You can see the entire conversation here.

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


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

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

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

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

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

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

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

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

[link, link]

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

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


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

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

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

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

Unit Economics (UE)

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

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

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

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

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

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

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

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

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

North Star Metrics (NSM)

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

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

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

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

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

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


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

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

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