The unreasonable effectiveness of data for better monetisation
summary
Claims that dynamic, ML driven pricing and reused behavioral data boost revenue with real results: an SDK captures hundreds of pricing triggers, models run in the cloud to segment players in real time, and a case study using Facebook value lookalikes yields 6x ARPU (a 400% lift over 45 days) while acquisition cost is 1.5x. Needs scale — roughly 100k DAU and ~1% conversion — and also uses the same signals to time ads and cut wasted UA spend.
AI-written summary · about
chapters (14)
- 0:00Intro
- 1:11Morpheus Moment
- 1:58Active Revenue
- 2:20Dynamic Pricing
- 4:04Data Science
- 4:19Machine Learning Pyramid
- 5:52Prediction
- 6:54churn
- 8:08solvable questions
- 9:39you not be perfect
- 10:50how machine learning works
- 13:02results
- 14:43user acquisition
- 16:18summary
related talks
20mUsers acquired - Now what?: Maximising revenue from existing players
19mMachine Learning is a new age of Personalized Game Monetization
15mSession: Embracing New Technologies – A Must-Have For Ad Monetisation And UA Managers
37mAd Monetization, IAP, Subscription Model – What is the Future of Monetization?
21mDemystifying Machine Learning for Programmatic UA & Retargeting
19mSession: Re-Engagement In 2026: How To Design & Scale Winning Retargeting Campaigns
17mHow AI automation increases games IAP LTV by 25%
24mReimagining Games Advertising