Articoli correlati a Game Analytics: Retention and Monetization in Free-to-Play...
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"A must-read for anyone working in game analytics." - Eric Seufert
“Truly one of the most useful things I have ever read about game development.” - Sergei Belkov, iLogos Game Studio
Mobile games are big business, and the landscape is more competitive than ever. With an in-depth focus on the core areas of user retention and predicting customer lifetime value, Game Analytics contains the hands-on SQL queries, R scripts, statistical theory, full-colour Tableau visualizations, and insider tips and tricks you need to succeed as a data analyst, product manager, or user acquisition manager in free-to-play games.
Game Analytics describes in detail how successful game studios make money, collect and query player data, define key performance indicators (KPIs), build dashboards and predictive models of retention and monetization, measure and predict return on ad spend (ROAS), and use statistics to analyze A/B tests designed to improve retention and monetization.
By the end of this book, the reader will understand:
- The business of mobile games: how they monetize, and what key performance indicators (KPIs) should be measured (and why);
- How SQL is used to query, clean, and prepare mobile game data for analysis;
- How to predict customer lifetime value using both retention and monetization curves;
- The relationship between classical statistics and A/B testing, and how A/B test are used to improve mobile games;
- ROAS and D7 ROAS targets; and,
- Why spending money on user acquisition also results in more organic installs.
- Conduct an A/B test and evaluate the results for statistical significance using SQL or R;
- Use regression in Tableau to fit retention and spending curves to historical data, and use these curves to predict various KPIs for new players;
- Model and predict churn with a Markov chain;
- Build KPI dashboards that are relevant to the mobile games industry;
- Build diagnostic dashboards to reveal problems with the tutorial;
- Use the Big Mac Index to optimize pricing in countries around the world; and,
- Predict LTV and ROAS from early signals.
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