How to Improve Your Game's Retention & Monetization Through Data-Driven Experimentation
summary
Explains a stepwise A/B experimentation loop: start with a goal and GQM; collect targeted events into Redshift or BigQuery; identify bottlenecks (tutorial dropouts, store visits, push CTR, ad placement); prioritize ideas by effort vs. expected lift; validate with A/B tests and keep secondary metrics in view. Recommends tools like Firebase, deltaDNA, Molecular, BigQuery/Redshift, expects low pass rates (about one in seven tests may win but can yield ~50% lift per visitor), and uses 5–10% safe rollouts for new features.
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chapters (10)
- 0:00Introduction and agenda
- 2:41Defining experimentation
- 7:36Data collection strategies
- 14:28Testing and validation
- 18:20Case studies and applications
- 25:43Pitfalls of experimentation
- 28:14Q&A session part 1
- 31:40Personalization via AI
- 37:56Q&A session part 2
- 44:14Final takeaways
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