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2020 Mobile App Engagement Benchmark Report

Apptentive’s annual mobile app engagement benchmark report serves as a baseline to help app publishers across categories understand their app’s engagement strengths and areas for improvement.

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We tailor each demo to your specific business needs. See it for yourself and contact us today!

Thanks for reaching out! While you wait for confirmation from an Apptentive team member, you may find these free resources to be of interest:

Guide

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Guide

2020 Mobile App Engagement Benchmark Report

Apptentive’s annual mobile app engagement benchmark report serves as a baseline to help app publishers across categories understand their app’s engagement strengths and areas for improvement.

Download Now

Loyalty & Retention

App Retention: Where Does Your App’s Category Fall?

Stuart Owen Axelbrooke  //  February 9, 2016  //  3 min read

Retention is a core metric for any app: it’s a direct driver of revenue and a core component of app business profitability. The trouble is, app retention benchmarks can be hard to come by in the app business, as many companies hold these critical metrics very close to their chest.

In recent research Apptentive has been doing around retention, we’ve come up with benchmarks for many app categories, which will help app owners understand how their app stacks up against the competition.

We’ve analyzed a representative sample of apps on our platform and present the results below.

The Retention Metric

As you probably know, there are many retention metrics, each one showing a different facet of your business and user behavior. We chose 28-day return retention, as it proved to generalize over app category better than the other retention metrics.

As a quick refresher for those who may not know it: 28-day return retention measures what percentage of people return to an app within 28 days of their last visit. 28 days is an important number—it’s long enough that lower frequency apps (like banking apps) still get meaningful numbers, and it’s a multiple of 7, meaning we avoid week-day fluctuation that is present in retention charts for most apps.

By saying that an app has 70% 28-day return retention, we mean that 70% of the users that came to the app 29 days ago have returned to it at least once since then. For more information on different retention metrics, check out this post from our friends at AppLift.

Top Performers

Now for the juicy bit! Based on our research, the highest retaining categories are:

Top Performers for Mobile App Retention by app category

  • News (82.78%)
  • Finance (82.16%)
  • Shopping (78.66%)
  • Medical (77.45%)
  • Music (75.89%)
  • Health & Fitness (74.89%)

These categories come in with return retentions in the mid 70 to low 80 percent range. These categories form habits that keep users coming back consistently. For example, news apps become people’s “I’ve got 10 minutes to burn” apps. Also similar are finance apps, which people check habitually to ensure their financial well being.

Poor Performers

On the other side of the retention spectrum, we have the poor performers. These categories have trouble making habitual users, with less than 60% of users coming back to the app in 4 weeks:

Poor Performers for Mobile App Retention by app genre

  • Casual (35.18%)
  • Tools (42.51%)
  • Simulation (44.73%)
  • Photo & Video (51.73%)
  • Adventure (52.16%)
  • Family (55.66%)
  • Food & Drink (59.75%)

Some categories like Food & Drink are spread fairly evenly, with some high retainers and and equal number of low retainers, but others are tightly distributed in the low range, like Casual and Simulation.

Conclusions

Conclusions here depend greatly on your vertical and business model, so general conclusions are hard to draw. Even in the low performing categories, we find high retaining apps along with the lows, showing us it is possible to create a valuable app in most categories.

There is one easy conclusion, though: Think twice before making a Casual app.

Dig in Deeper!

If you want to dig into the data deeper, we provide it in the following Google sheet. Categories that had fewer than 10 samples were excluded.

Let’s keep the conversation going in the comments around the additional learnings you find. We’d love to hear from you!

About Stuart Owen Axelbrooke

One of Apptentive’s Senior Engineers, Stuart is an avid machine learning and data science practitioner who finds patterns in data. A Seattle native, he enjoys climbing, photography, and thinking about elegant representations for cluster computing.
View all posts by Stuart Owen Axelbrooke >

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