Compare your app against the rest

Benchmarking app engagement metrics

With even more granularity, you can filter app engagements with four different options to find unique and comparable datasets.

About Adjust's Global Benchmarks

Curious about the competition? So were we. That's why we gathered data from thousands of apps and billions of installs to create this overview of the mobile marketing landscape.

Discover app engagement statistics by vertical

Analyze the statistics most relevant to your app by filtering app categories. Understanding user engagement is key to knowing an app’s audience - ultimately helping to reduce marketing costs and supporting the bottom line of mobile-focused business. With Adjust’s mobile marketing statistics, you can observe engagement averages for each vertical: Just filter between entertainment & news, travel, gaming, social, business & finance, utilities, health & fitness and ecommerce to gain intel on specific verticals.

View average user sessions statistics and retention rate by segment

Our benchmark tool allows you to learn from the mobile industry’s average user sessions, including time spent and frequency of use. Insight into the average user sessions gives a greater impression of user loyalty, which can therefore improve the user experience and maximize retention rates. Unique sessions are counted per day after install, per use, and are separated by at least 30 minutes. Based on retention rates from Q2 and Q3 of 2018, our updated tool provides insight on current users habits after installing an app. Take a look at the likelihood of retaining a user after 24 hours, one week and 30 days.

Screenshot of Benchmarks tool
Screenshot of Benchmarks tool

Gain insight on fraudulent installs per category

Perpetrators of ad fraud will create users who aren’t real in order to create entirely fake engagements. Other methods try to get paid for organic traffic. Our benchmark tool allows you to see the total percentage of installs tracked by Adjust which are known to be fraudulent. If you’d like to see how each vertical is affected by fraudulent traffic, you can also filter by category and check out the statistics. The tool also shows the distribution of various fraud types. With an increase in certain fraudulent activities, you can see what’s most common within the industry. With the updated metrics, you can follow the current scale of SDK Spoofing, Click Injection, Click Spam and Fake Users within the industry.

Compare Cost per Install statistics by app category

We’ve added five new metrics to Global Benchmarks 2.0: Distribution of Fraud Types, Click Through Rate (CTR), Conversion Rate from Click to Install, Cost per Mille (CPM) and Cost per Click (CPC). These metrics allow you to create tailored reports for your specific needs. Whether you’re looking at the Click Through Rates in Europe or Cost per Mille in Latin America, our latest tool gives you customizable reports using a data set of over +4B installs, free of charge.

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Behind the Benchmarks

Who we are

Adjust is the industry leader in mobile measurement and fraud prevention. The globally operating company provides high-quality analytics, measurement and fraud prevention solutions for mobile app marketers worldwide, enabling them to make smarter, faster marketing decisions.

Adjust is a marketing partner with all major platforms, including Facebook, Google, Snap, Twitter, Line, and WeChat. In total, more than 25,000 apps have implemented Adjust's solutions to improve their performance.

Founded in 2012, today Adjust has global offices in Berlin, New York, San Francisco, Sao Paulo, Paris, London, Moscow, Istanbul, Seoul, Shanghai, Beijing, Tokyo, Mumbai and Tel Aviv.


A look behind the data

We analyzed data taken between January 1 and September 30, 2018, with a slightly different sample size for each vertical (the maximum sample size for a single category was over 7,000 apps!). To ensure a clean data set, we calculated an average retention rate and sessions per user on a per-app basis for every combination of vertical, region, user type and platform (an average of all 31 cohort days’ values). We calculated a permitted range of values using the interquartile range in every case and removed outliers outside of the permitted range for apps of each specific vertical.

After removing the oddballs, we chose to only present data for selected combinations of the available categories if the sample of apps fit three conditions. First, the sample must be greater than 30 apps for at least 75% of days after install for the given metrics. Next, the app cohort size must be at least 2,500 users or more.

For any avoidance of doubt, all data we analyzed was anonymized during this process.

Screenshot of Benchmarks tool