This page provides you with instructions on how to extract data from AppsFlyer and analyze it in Google Data Studio. (If the mechanics of extracting data from AppsFlyer seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is AppsFlyer?
AppsFlyer is an attribution stack for mobile marketers. It lets businesses attribute every install of their apps to the marketing campaign and media source that drove that install. It also provides an analytics dashboard that shows which users engage with an app, how they use it, and how much revenue they generate.
What is Google Data Studio?
Google Data Studio is a simple dashboard and reporting tool. It's free and easy to use, but it lacks the sophisticated features of higher-end reporting software. Many of the connectors it supports are for Google products, but third parties have written partner connectors to a wide variety of data sources. Its drag-and-drop report editor lets users create about 15 types of charts.
Getting data out of AppsFlyer
AppsFlyer exposes data through its Pull API, which developers can use to extract information. Each API call, which is made in the form of an https query, must contain the user’s external API Authorization Key, as well as from and to dates that specify the date range of the data requested.
Additional parameters can request information like media source, currency, and specific fields. The parameters must be added to the https query – for example:
Each successful API query returns a CSV file of data that you can use as an import source to your data warehouse. The query you use will determine what fields you receive.
Loading data into Google Data Studio
Google Data Studio uses what it calls "connectors" to gain access to data. Data Studio comes bundled with 17 connectors, mostly to pull in data from other Google products. It also supports connectors to MySQL and PostgreSQL databases, and offers 200 connectors to other data sources built and supported by partners.
Using data in Google Data Studio
Google Data Studio provides a graphical canvas onto which users drag and drop datasets. Users can set dimensions and metrics, specify sorting and filtering, and tailor the way reports and charts are displayed.
Keeping AppsFlyer data up to date
At this point you’ve coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.
Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in AppsFlyer.
And remember, as with any code, once you write it, you have to maintain it. If AppsFlyer modifies its API, or sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.
From AppsFlyer to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing AppsFlyer data in Google Data Studio is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites AppsFlyer to Redshift, AppsFlyer to BigQuery, AppsFlyer to Azure Synapse Analytics, AppsFlyer to PostgreSQL, AppsFlyer to Panoply, and AppsFlyer to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate AppsFlyer with Google Data Studio. With just a few clicks, Stitch starts extracting your AppsFlyer data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Google Data Studio.