Impression Level Revenue Data by MoPub – Powering a Fresh Approach to Ad LTV

Today, May 21st. MoPub is announcing an important feature to the app ecosystem – offering impression-level revenue data (ILRD) to publishers who are using the MoPub monetization platform. I’m including a link to their official announcement so you can read the news in their words. In this blog post you can find information about what makes ILRD unique in comparison to the data reporting of other mediation platforms as well as what this means for SOOMLA customers who are using MoPub.


ILRD by MoPub – Unique Approach to Ad LTV

As you may be aware, MoPoub is not the first mediation to provide ad revenue data to the publisher. There are other companies who have started doing this over the past 12 months. That said, MoPub’s approach is different and in our opinion better. First and foremost, MoPub doesn’t sell media so there’s no bias. We believe that one who sells media, should not be providing the measurement as well. Otherwise, it is similar to grading one’s own homework.

The mediation platforms, by nature, have multiple demand sources that can be divided into 2 categories:

  • Sources with full visibility to impression level revenue data – these include: RTB based demand, header bidding demand and publisher static deals
  • Sources with no visibility beyond placement/ad-unit level revenue data – these include mainly ad networks as well as Facebook and Google demand

Typically, sources with full visibility to impression level revenue data account for less than 20% of the total publisher revenue. For this reason, it is important for the publisher to know what part of the ad LTV output is based on exact data and which part is estimated. MoPub’s solution addresses this issue by:

  • Providing the revenue at the impression level
  • Indicating for each impression whether the revenue is estimated or exact through the “Precision Field” included in the callback.

Why the Precision Field is so Important

Unlike other solutions offered by mediation platforms, MoPub’s ability to indicate whether or not the ad revenue is precise is both transparent and more practical. This comes into play in a few situations:

  1. If the publisher already has accurate ad revenue data directly from some of the ad providers
  2. If the publisher built their own estimation system and it is more advanced than simply taking the avg. eCPM of  the line item
  3. If the publisher is using a dedicated solution for Ad LTV like SOOMLA that receives true data from some networks and provides better estimations for other demand sources

In all 3 situations the ability to indicate the precision level is critical so that the publisher or the 3rd party Ad LTV solution can select the most accurate source for the data.

Let’s consider this example and see how SOOMLA addresses the situation.

Impression served byRevenue data from MoPub (ILRD)Revenue data from existing methods What SOOMLA Outputs
MoPub ExchangeExactEstimated (bid based)Exact
Network AEstimated (Avg. eCPM of line item)Exact (directly from network)Exact
Network BEstimated (Avg. eCPM of line item)Estimated but good (based on Installs + advertiser identity)Estimated but good

The precision field allows publishers as well as 3rd party solutions like SOOMLA to output data based on the most accurate source available. Based on our testing, the impact of being able to select the best method for each network improves accuracy from the 60% area to over 85% accuracy when evaluating accuracy across all revenue sources.

In comparison, solutions by other mediation platforms don’t provide this important indication so instead of improving the publisher’s visibility they may actually reduce it.

How is SOOMLA Planning to Support Impression Level Revenue Data

SOOMLA’s accuracy levels when it comes to MoPub’s exchange are already very high when SOOMLA associate revenue to a user on behalf of the publisher. Based on our own testing accuracy is:

  • 99% on a cohort level
  • Over 90% for D7 LTV of a single user

Obviously we want to be at 100% as our publishers deserve the most accurate data.  This is the reason why SOOMLA supports ILRD as of today. Publishers that want to enable it should upgrade their SOOMLA connector for MoPub to version 4.1.4 to enable it.

Unique Opportunity to Test SOOMLA Accuracy

This new trend of monetization providers that provide validated true data on the impression level presents a unique opportunity for publishers who are already using SOOMLA to test our accuracy levels. There are two ways to go about this:

  • Implement a listener for the call back of the monetization provider so that data is sent to your own BI for impressions that indicates an ‘exact’ precision level. Then compare this data set to the data you pull from SOOMLA data dumps
  • Ask your SOOMLA CSM to configure the data dumps so that you will receive both the ‘exact’ value as well as the SOOMLA estimation for these known values so that you can compare them

We recommend comparing the data sets in 2 ways:

  1. Cohort level comparison – simulating a marketing use case to decide on a campaign
  2. D7 LTV on a user level – simulating ad whale detection

We are confidant that this comparison will increase publisher confidence in SOOMLA data for networks that don’t share the exact revenue data. In turn, we expect it will allow customers to act more aggressively based on the data SOOMLA provides. In the meantime, you can download our internal accuracy test results by clicking on the button below.

Apr 2019 - Accuracy Test Results

If you are interested in the subject and want to understand why SOOMLA invested 3 years into perfecting Ad LTV measurement and how that translates into improved accuracy you can also schedule a demo below.

Feel free to share:
Previous articleMobile Attribution: 10 Things Every App Publisher Needs To Know
Next article5 Myths About Ad LTV
Raised in the Kibbutz and reborn in the city, Yaniv is a certified entre-parent-neur. When he’s not busy doing SEO, content marketing, administration, QA, fund raising, customer support… [stop to breathe], you can find Yaniv snowboarding down the slopes of France and hiking with his kids. Yaniv holds a B.Sc. in Computer Science and Management from Tel Aviv University. He is also an avid blogger and a speaker at industry events. Before SOOMLA, Yaniv co-founded EyeView


Please enter your comment!
Please enter your name here