The internet now has over 3.8 billion global users and counting, with the number increasing steadily by 10% year over year. Easier online access means people are using digital products across desktop and mobile more than ever. Unfortunately, that means it’s only getting harder for software companies to stand out from the competition and keep users interested in their product.
While this trend affects all software products and digital services, it has significant impact on the mobile app industry in particular.
Fact: Over 4 million apps now populate the Apple App Store and Google Play Store alone.
Time spent on mobile devices has been increasing steadily year-over-year, with apps owned by big names like Facebook and Google dominating the top download spots. With fierce competition, many apps are pretty much “dead on arrival.” In fact, because they have so few users, 90% of English-speaking iOS/Android apps cannot be discovered in any list, any category, or any genre.
So how do you build a product that’s not only capable of acquiring users, but also keeping them around? In other words, how do you sustain real growth? It starts with understanding why retention is so critical to growth.
With these concepts in place, you’ll be set to get a better grasp of your product’s usage and baseline user engagement metrics in Chapter 2. This baseline will help you better contextualize the Retention Lifecycle Framework for your product.
Pour enough dollars into acquiring your users and you might be able to temporarily get on one of the App Store’s top charts. But attracting users is not enough. Our analysis of over 500 million mobile devices has shown that:
80% of new users stop using the average app just three days after downloading it.
This doesn’t just apply to mobile. If you don’t demonstrate value to your users early and often, and turn them into habitual users, your product—be it a mobile app or otherwise—will die. Filling the top of your funnel doesn’t matter if your product is effectively a leaky bucket; long-term growth of a product, as well as the health of a business, depends on how well you retain users. That’s how you demonstrate your product has real value.
N-Day retention of iOS (blue) and Android (green) users. Less than 20% of users return on exactly the third day after first use of the product.
TERMS TO KNOW:
N-Day Retention: The proportion of users who come back on the ‘Nth’ day after first use.
Retention Curve: A line graph depicting the average percentage of active users for each day within a specified timeframe.
At a high level, retention is a measure of how many users return to your product over time.
Even the best products lose the majority of their users in just a few days, but if you make retention your primary growth metric, you can change the trajectory of your company from one that stalls or loses users over time, to one that sustains true growth.
Increasing user retention and minimizing churn is the key to building a base of loyal, engaged users and driving sustainable growth. A business that retains its users increases its revenue and becomes profitable faster than one that does not. Retention impacts every important business metric that you (and your investors) care about—active user count, engagement, customer lifetime value, payback period, and more.
‘The point is, every improvement that you make to retention also improves all of these other things—virality, LTV, payback period. It is literally the foundation to all of growth, and that’s really why retention is the king.’
– Brian Balfour, former VP Growth, Hubspot & Co-founder, Reforge
When measuring N-Day Retention, Day 0 typically refers to the day on which a new user first uses the product; first-use can encompass anything from downloading and opening a mobile app to completing a specific action within it. Following that, retention on Day N is the proportion of users who started on Day 0, who also returned and were active N days later.
A good way of visualizing retention rate is by plotting a retention curve, as shown below.
Let’s say this is a retention curve for a hypothetical app, and we’re looking at users who started using this app for the first time on January 1 through January 31.
This graph shows the weighted average of all Nth day retention numbers from cohorts of users acquired within that time period. According to the retention curve, for example, Day 7 retention is about 13%. This means out of all the users who first used the product on January 1 (Day 0), 13% came back and were active on January 8 (Day 7). You can also quickly visualize the drop-off from Day 0 to Day 1: about 37% of users come back 1 day after first use—nearly 2x higher than the industry average.
A common misconception about retention is that it only matters after your company is past a certain stage of growth. In reality, once you have some amount of users coming back to your product on a regular basis, you have enough information to begin optimizing for retention.
Keep in mind that the way you approach your retention may change over time, but what doesn’t change is the fact that only companies that constantly improve their retention rates can grow and become profitable.
Let’s look at how retention analysis can benefit companies at all stages of growth.
Retention can actually indicate if you have a product-market fit problem; if you plot out your retention numbers as a percentage of active users over time and you have a flat line that reaches zero instead of a curve that stabilizes—you need to solve a product-market fit problem, not a retention problem.
In the graph above, Product A has achieved product-market fit. Its retention drops off initially but stabilizes at roughly 45% active users.
Product B’s retention, on the other hand, trends straight to zero and never levels off, which means that it lacks a user base who regularly uses this product. Product B has not achieved product-market fit.
How do you think about retention when you don’t have users? At the early product development stage, you should be thinking about what retention ultimately boils down to—why any user would get hooked on your product.
Ask yourself if your technology is, as Nir Eyal puts it, “manufacturing desire.” Building a habit-forming product is the crux of retaining users long term.
Once you have your early users, it’s time to start optimizing retention. Test out and resolve factors affecting app performance and clean up bugs—users have little patience for apps that crash all the time. Then start understanding what value your users are getting out of your product.
Figure out what your power users are doing and nudge your user base to behave more like them. Get your retention to a healthy baseline before spending on acquisition.
Diagnosing and improving your retention should be an ongoing process, at all stages of growth. As you iterate on your product experience—in order to develop a more habit-forming, sticky product—you have to measure retention in tandem. You may have hypotheses as to why some users churn and others retain, but you have to continuously iterate on and assess your retention metrics to understand how users respond to your product.
Making retention the central focus of your business’s growth might sound daunting, but it doesn’t have to be. This is where The Retention Lifecycle Framework (more on that in Chapter 3) and this playbook can help.
While there is no magic formula that will improve your retention, this playbook provides a framework to diagnose your product’s retention and develop strategies to improve it . Going back to the retention curve, Brian Balfour describes two “levers” that can improve retention. At a very high-level, these are:
Optimize your first-time user experience and demonstrate your product’s core value to new users, so users want to stay around longer, instead of churning in the first few days.
Increase your baseline level of users by consistently delivering a solid product experience.
Following both of these concepts, this playbook offers a novel framework for diagnosing and systematically improving your user retention.
We developed our retention framework by working with eight customers at different stages of growth and in different verticals. By diving into their user behavior data, we were able to validate the principles of our framework as well as the methods we used, and find real insights and recommendations for improving their retention.
Over the next several chapters you’ll see how different companies—from utility apps, to ecommerce products, to mobile games—have used the Retention Lifecycle Framework to gain a deeper understanding of their users, implement strategies to improve retention long term, and accelerate their growth.