Challenges Of User-Level Ad Revenue Reporting

In recent weeks, our industry has entered into a vibrant discussion surrounding user-level ad revenue tracking.

It’s easy to understand the excitement: for app developers who rely on mobile ad monetization, it’s intuitive this would be the Holy Grail of ad revenue data analysis. It's intuition allows precise ad revenue LTV calculations for each acquired user. The hope is that it enables developers to forecast revenue more reliably while invoking sophisticated strategies that wouldn’t otherwise be possible.

The fact is that there are several major technical barriers to 100% accuracy with user-level ad revenue reporting. The biggest is that advertising networks serving revenue generating ads need to give developers (including solutions like mediation partners) access to user level conversion and bid data.

For example, if an advertiser pays a network and publisher on a CPI basis, and an install/conversion never happens from a click on the ad, the publisher and network doesn’t get paid (ad revenue). Today, the best any third party can know is an impression or a click. No 3rd party solution provider has access to conversion/install data to know if payouts will be made (where ad revenue is generated). The only entity that knows the conversion data is the network working with the advertiser.

As a result, solution providers from all sides of the industry have devised their own models for addressing this challenge, but the fact remains that there is no fully accurate, standardized approach currently available. Until the conversion and bid data is accessible to all parties, estimation is the only alternative to user level ad revenue. Ideally, user-level ad revenue would be tracked by linking revenue data to a specific device ID, but with different bid types and conversion requirements for advertiser payments. Transparency is not entirely possible in today’s ecosystem.

Ad revenue on the user level is still an estimation

Tracking ad revenue by user level is not a new concept. Several mobile attribution/analytics companies offer some variation of the service. The problem is these solutions aren’t consistent with each other -- they’re based on their own estimations. Don’t get fooled. What you will want, until bid level data is accessible by third parties, is internal estimation consistency among your teams.

The reality is that only the ad networks that serve ads directly from the advertiser have access to revenue data that can be assigned on the user level. Simply put, if a conversion needs to happen, only the advertiser’s network has a full picture of that conversion event and can deliver revenue to the customer. All other parties have:

  • Aggregated ad revenue from networks (Facebook, Google, ironSource, Tapjoy, etc.)
  • The number of app events generated by each mobile user grouped by advertising source

These data sources cannot generate a 1:1 comparison of revenue per user, but they are enough to fill in knowledge gaps using an algorithmic model. We have already seen more than 10 approaches of calculating that are marketed as “user-level ad revenue”.

Problems start when the publisher isn’t consistent with estimate calculations. If you use two different estimate calculations from different providers, confusion is inevitable. You are not comparing apples to apples in this case, which can lead to incorrect business decisions.

Is there a perfect solution?

Although estimates are not perfect, they’re good enough to still generate a massive return on investment. As Tenjin works with data from all of these parties, internal studies have shown that there is less than a 10% difference between estimation models, indicating that there isn’t much optimization necessary in the modeling. In fact, it’s not even possible to say which estimation is “more” accurate -- since you’re comparing two estimates.

Right now the mobile industry isn’t mature enough to support universal user level ad revenue tracking. Until ad networks that pay out publishers with ad revenue are able to share actual conversion data at the user level (along with the bids associated with those conversions) the industry is forced to estimate.

Our advice: choose a system and process that’s internally consistent and reliable. Consistency leads to a more stable way of executing your business objectives. Unless you know the inner workings of estimates and know how to deal with issues and discrepancies, keep things simple and internally consistent. It’s much more effective to keep business processes standard and understandable across teams.

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