A/B testing in ad monetisation
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The document discusses the importance of A/B testing in ad monetization, emphasizing the need for user-level data to effectively optimize ad performance and revenue generation. It outlines strategies for ad mediation, including the use of algorithms to manage ad opportunities and mitigate data volatility. Finally, it highlights the critical role of analytics and testing in maximizing monetization potential for games.
A/B testing in ad monetisation
- 1. © Outfit7 Limited 2010 – 2019 AB testing in ad monetisation Ante Odić Deputy VP of Product Management
- 2. 9+ billion downloads, 22 games, 350 million MAU, 23 billion video content views <
- 3. Introduction
- 4. Analytics infrastructure ● BigQuery ● Python ● R Introduction AB testing What do you need to do? Randomly put users into two or more groups Have enough users BE User level data Configuration Collect user level data Change their settings from BE Analyse data Analyse a lot of data Understand scientific methodology
- 5. Introduction Ad monetisation What it is and why do you care? ● Rewarded videos, banners, interstitials ● Generating opportunity to show ads ● Ad mediation of partners ● Big revenue source for specific type of games
- 6. Introduction What are we optimizing Ad mediation AD TECH SIDE ● Showing the most valuable ad ● Improving data ● Creating algorithms Generating opportunity PRODUCT SIDE ● right position and frequency of banners ● right timing and frequency for interstitials ● right motivation of users for rewarded videos
- 8. Generating opportunity AB testing ● Number of generated opportunities ● Requests per user ● Impressions per user ● Percent users with impressions ● Retention ● Session duration ● Overall monetisation Focus “Easy” because user level data is available Big impact Good statistical power for most metrics
- 9. Ad Mediation
- 10. Ad Mediation AB testing Hard because user level data is NOT available Big impact when you start Testing revenue is tricky ● Waterfall algorithms ● Data processing algorithms ● ARPDAU Focus
- 11. Problems Ad Mediation No user level data means you depend on reports from partners Not real AB tests but testing revenue on segments Time series analysis Find pragmatic solutions to move quickly
- 12. Data processing algorithms Ad Mediation Use simple logic to understand and solve problems Simulate Implement and test Data is messy and comes in late. What do we base our algorithm on? Predicting current ECPMs Irregularities on the partner’s side make your data messy and volatile. Algorithms should not overreact. Volatility algorithm
- 13. Ad mediation algorithms Ad Mediation Waterfall algorithm, calculating the expected value In which order should we ask partners for ads? Impressions Revenue Requests Country Platform ... Algorithm Waterfall
- 14. Now what?
- 15. Where to start? Get yourself some data fast Test opportunity first Be pragmatic
- 16. Be careful! All of this is closely connected to a whole set of metrics Measure the big picture Assumptions are not facts, even if you really really believe in them If you cannot explain it to a kid, you do not understand it
- 17. What to expect? Without analytics and AB tests you are not aware of the potential Without AB tests you can do harm and not even know it If there is one aspect where you can do huge impact it is monetisation
- 18. Thanks!
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