Based on years of ad monetization and big data analytics technology, UPLTV released a “User-level Ad Revenue Tracking” (UART) and has shown how this new functionality has brought significant ROI improvement for mobile games predominantly with IAA (In-App Ads) strategy. Marketers can easily find their ad “whales” and execute their user acquisition through Facebook’s Ad Impression and Ad Click App Events to generate Lookalike Audience for more targeted campaigns.
Current common practices on ad revenue distribution are based on two types of ad revenue breakdown methods which are both flawed:
1. “Weighted Average Method” shown as below. This model assumes that each impression is valued equally and does not differentiate between CPC or CPI models, assuming users contributing more impressions generate more ad revenue.
2. Segmented “weighted average” follows the same weighted average logic as the above method but before averaging it out, creates more segmented layers or price control settings on a specific ad placement. This method gets a bit closer than a pure “weighted average”, but still has the following issues:
- Due to its calculation method, it still has all the same problems as the weighted average method as above.
- The more layers of segmenting on your ad settings can create reverse results monetization, in practice, this could lower ad revenue.
Read More: How Augmented Reality is Helping in Enhancing Customer Experiences
Introducing UART (User Ad Revenue Tracking)
UPLTV has been building our expertise on mobile game ad monetization. Accumulating billions of data points for user ad behavior, UPLTV has completely abandoned the “weighted average” method to estimate for user-level ad revenue.
UPLTV’s approach to user level ad revenue:
- Accurately identify, segment and track in-game “ad behavior” points
- Differentiated algorithms for different ad types and formats
- Our mediation platform can effectively tag and obtain data from each user-ad interaction
The 80/20 rule still applies
Based on the UART function, UPLTV can see that a minority of users contributed to the majority of ad revenue. Therefore, treating all users equally cannot yield accurate insight.
Similarly, from switching to an “impression” level view of the UART dashboard shows that the top 20% of users (in terms of number of impressions) only contributed to 49% of ad revenue. Clearly, the number of impressions cannot be taken to equate to ad revenue.
UPLTV’s improved ROI with UART
When it comes to UA strategy, UPLTV no longer needs to make subjective evaluations based purely on CPI and retention, but to accurately see the ROI of each channel based on UART.
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