Users acquired - Now what?: Maximising revenue from existing players
summary
Argues that building a personalization stack using machine learning-driven personalization and dynamic pricing lifts spend from existing players without acquiring more users. Describes a system that runs predictive models, automated A/B testing and loyalty workflows, and claims case results like 50% uplift in key markets and a regional test that sells 17x units after price and offer optimization. Presents this as production software that integrates with CRM and game economy data and that works.
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chapters (8)
- 0:00Introduction and context
- 2:28Mobile gaming growth and challenges
- 5:16Optimizing revenue from existing players
- 8:05Dynamic pricing strategies
- 10:30Loyalty programs and consumer behavior
- 13:19Machine learning for consumer decisions
- 15:05Case studies and final insights
- 17:16Q&A and closing remarks
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