Perform advanced measurement by exporting attributed raw user-level ad revenue

By Danielle Nissan
exporting user level ad revenue

App developers today typically use more than one monetization strategy to meet their business goals; one of which is revenue coming from in-app advertising. Some app developers choose to use the valuable app real-estate to capitalize on views or clicks from ads shown within their app, and thereby generate another source of revenue for their business.

Return on ad spend (ROAS), an important KPI for app developers, reflects a campaign’s marketing performance via the app’s monetization strategy and can be extremely important when trying to analyze the revenue at the user-level.


Today, most app developers are left with an “estimated” calculation on which users are generating the best ROAS for their business. Since ROAS is a major KPI for most advertisers, having an estimated calculation just won’t cut it.

Providing user-level revenue data allows app developers to better understand which user-acquisition source is generating the most revenue down the line for their business.

User-level data will allow AppsFlyer to accurately tie back ad revenue from those users coming from a certain user acquisition campaign.

Once this is done, the app owner can better understand which users are most valuable to them and where exactly they were initially acquired. These highly-valuable users (or ‘ad whales’) hold the key to performance efficiency, and come to carry a lot of weight for gamers out there or any other apps generating an important source of revenue from in-app ads.

A while back we announced the user-level ad revenue API integration with a list of partners: ironSource, MoPub, Max by Applovin, and Appodeal and the list keeps growing.

We are more than excited to announce yet another partner – Tapdaq, starting this month.

Considering that there is a rise in mobile app developers who are increasingly generating revenue from in-app advertising, the task to measure accurate ROAS, calculate true ROI and understand exactly which user is most valuable, has become critical for advanced optimization.

AppsFlyer’s Ad Revenue Attribution: New capabilities

As part of AppsFlyer’s ad revenue attribution product, we now offer our customers the ability to export raw ad revenue data! 

This allows app developers to accurately see their top performance and tie back to the users who are generating revenue as mentioned above. Other than understanding their top-performing user acquisition sources, they can build robust predictive revenue models with granular raw data at their fingertips.

Furthermore, they can locate important trends in user behavior, optimize their ad formats, ad timing and model predictive LTV.

But that’s not all.

There are more benefits to having user-level ad revenue data.

By being able to export the revenue data from AppsFlyer, together with the attribution data, app developers have unprecedented insight into exactly which users generated a specific amount of revenue for them at a much more granular dimension, as well.

What can you do with this granular analysis?

Well, predictive models, for one. Sophisticated app developers (let’s say for a hyper-casual app) are craving the ability to go one step further and build predictive models (eCPM models and eCPC models) to truly understand how to maximize their ROAS and optimize the true LTV of their users and future users. Predictive modeling leverages historical data in order to make accurate predictions.

The more data you have, the more accurate your model will be. 

To better understand the importance of analyzing user-level ad revenue data, let’s take a gaming example where the user acquisition team understands that their best performing users (those who result in the most views or actions from app ads), come from Snap and engage strongly with video content.

The monetization manager now has the ability to optimize the monetization strategy according to the channel through which this user was acquired.

Looking at raw ad revenue data, the monetization manager can see that users acquired through Snap, generate more revenue from video ads compared to interstitials. As a result, a decision can be made to show users acquired through Snap a video ad as their first ad impression.

Attributed user-level ad revenue data, unlocks a whole new level of monetization optimization strategies and collaboration between both UA and monetization managers.

To get started, our customers can head over to the data export page or via the Raw Pull API today!

Important: To enable user-level ad revenue API for a supported mediation network, you must disable the ad revenue APIs of the monetization networks that it mediates.

Providing our customers with advanced data management means that we commit to cover a wide range of data and go further with data granularity.

We know how advanced our customers are when it comes to data management and optimization and so, we are out to accomplish the technology necessary to provide them with the top capabilities in the market. 

Danielle Nissan

Danielle Nissan is a seasoned digital marketer and mobile tech junky, who has been working in the advertising technology space for over 7 years, starting as a technical project manager and later, as a client success director at Kenshoo. She is now a product marketing manager, passionate about leading strategic initiatives through collaboration between clients, agencies, partners and publishers alike. Danielle is also best known for her excessive use of coffee in the office.

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