Compare your app against the rest

User behaviour before the install

See what users do before they install, and filter data by your chosen vertical and date range.

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.

Behind the Benchmarks

Who we are

Adjust is the world’s leading attribution and fraud prevention provider for mobile apps. We help marketers find user trends and get actionable insights with our attribution tracking, in-app event analytics, fraud prevention and audience segmentation tools. All of your most valuable marketing data is powerfully displayed in a single dashboard.

To create Adjust’s Global Benchmarks, we processed over four billion installs across thousands of apps, giving us all of the information we needed to offer you a better look at how your app stacks up against your competition.

We created the Global Benchmarks to help better educate marketers on how the ecosystem operates - and give them a better view of how they perform against their competition.

We’re market leaders in providing ongoing transparency, with several reports showcasing our full range of data. Adjust empowers marketers, giving them a better view into mobile app performance - and our Global Benchmarks tool is the first to provide everyone with the data above.

Adjust Dashboard graphic
Screenshot of Benchmarks tool


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.

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