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This is no BS gaming podcast 2.5 gamers – What payback should I aim for? Mastering CPI & LTV in Mobile Gaming by Matej Lancaric

Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let’s not forget this is a 4 am conference discussion vibe, so let’s not take it too seriously.

The Team:

Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r- Game design consultant

Felix Braberg⁠ – Ad monetization consultant

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Matej Lančarič – User Acquisition & Creatives consultant

Join our slack channel here: SLACK CHANNEL

Summary

In this conversation, Matej Lancaric discusses the critical relationship between Cost Per Install (CPI) and Lifetime Value (LTV) in mobile gaming. He explores how different game genres affect these metrics, the importance of churn prediction for user retention, and strategies for optimizing user acquisition. The discussion also covers the significance of payback periods and how to utilize LTV predictions for effective marketing strategies, ultimately emphasizing the need for informed decision-making in the competitive mobile gaming landscape.

Takeaways

  • CPI is a critical metric for user acquisition budgets.
  • LTV represents total revenue expected from a user.
  • Churn prediction can help retain users effectively.
  • Different game genres have varying payback periods.
  • User engagement directly influences LTV.
  • Regular game updates can enhance user retention.
  • CPI should always be lower than LTV for profitability.
  • Predictive modeling aids in targeting high-value users.
  • Analyzing competitors can provide valuable benchmarks.
  • Understanding the CPI-LTV equation is essential for success

 

Chapters


00:00 Introduction to Game Genres and Revenue Models
01:14 Understanding CPI and LTV in Mobile Gaming
12:09 Churn Prediction and User Retention Strategies
14:37 Decision-Making Based on CPI vs LTV
18:01 Payback Periods Across Game Genres
22:30 Utilizing LTV Predictions for User Acquisition
28:09 Conclusion and Key Takeaways

 

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