Day N retention, rolling retention, and the many facets of the retention metric

Retention rate is one of the fundamental metrics in product management. We all use it regularly, yet few of us know that there are many different ways to calculate retention rate. And it is very important to know which one to use when you’re making decisions based on retention data.

Let me start with a story. When I worked at Zeptolab (popular game development company, creator of Cut the Rope, King of Thieves, CATS) once we got an email from a gamedev studio that wanted us to publish their game. We were getting many similar emails, but that one got our attention. We were impressed by the metrics of the game, which had just recently soft launched. According to the developers, Day 1 retention rate of the game was over 55%, and Day 7 retention rate was over 25%.

However, when we started playing and testing the game, something felt wrong. The gameplay was not engaging enough to justify >55% Day 1 retention rate. And the meta game design was not good enough to users in the longer term.

Further investigation revealed that what this game development company called retention was actually “rolling retention.” Classic Day N retention of the game happened to be unimpressive.

This is just one example of how retention metrics can misguide you. There are many nuances in how you can calculate it.

P.S. If you want to learn how data can help you build and grow products, take a look at GoPractice! Simulator.

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Looking for spikes. How to increase the effectiveness of your dashboard

Consider this:

A product manager asks an analyst: “Please make a dashboard where we can see Day 1, Day 3 and Day 7 retention rates in dynamics.”

“Are you sure?” the analyst asks. “These charts will be quite noisy. Just look how much the metrics alter from day to day. Maybe it’s a better idea to monitor the weekly retention rates instead. In this case, any random fluctuations will be smoothed out. ”

They called it a deal.

Now a new dot appears on the dashboard once a week. This dot has “Everything is fine, nothing has changed” written all over it. But sometimes, the storms of everyday life are hidden behind this apparent calm: days of ups and downs, victories and defeats that happen during weekdays and weekends.

But no one on the product team finds out about them because everyone is looking at weekly metrics. Consequently, they’re missing both the random and meaningful fluctuations.

P.S. If you want to learn how data can help you build and grow products, take a look at GoPractice! Simulator.

Looking for spikes. How to increase the effectiveness of your dashboard

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Analytics without numbers: Viewing products through users’ eyes

Here are two things you’ll hear a lot from product teams:

1. A lot of data is needed to reduce uncertainty and get an accurate picture of users’ needs and behavior.

2. People working on products understand and know their users well.

The above statements are misconceptions, and in both cases, the reverse is true. In this post, I will discuss how analyzing user behavior without big data (debunking the first premise) will help us avoid the unpleasant outcomes of thinking you already know your users (debunking the second premise).

P.S. If you want to learn how data can help you build and grow products, take a look at GoPractice! Simulator.

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ASO optimization in practice: How a game I made over the weekend amassed 2 million downloads

A couple of years ago, I wanted to turn my theoretical app store optimization (ASO) knowledge into a working skill.

So I decided to develop a mobile game. My goal was to validate the hypothesis that in the super-competitive mobile gaming market, you can launch a product that will grow into something large solely through organic traffic.

Let me say right away that I did validated this hypothesis: A mobile game we created over the weekend ended up amassing over 2 million downloads, and received over 30,000 new users per day at its peak, all through organic traffic.

But the path to success looked nothing like the original plan.

Here is the story of how the project evolved and how one small change increased the number of downloads by 200%.

If you want to learn how data can help you build and grow products, take a look at GoPractice! Simulator.

ASO optimization in practice (App Store Optimization)

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