Ben Reiter’s book on the Houston Astros depicts the culture of data that helped win the 2017 World Series. The Astros are a great example of an organization that knew when to rely on data, and when (and why) to trust their gut. “Astroball” is a great book that provides some interesting insights for baseball fans – and also for anyone wrestling with how and when to use data in their decision process.
Game as service is not just about adding content. It’s about serving the needs of users. And that has implications for the way you design and monetize your game.
Designing gacha and setting up the probabilities is a much a matter of game economy as it is a matter of perspective. This point provides pointers to approach gacha design and suggests ways to improve the monetization of your gacha.
This post discusses how to approach the tuning of your progression and balancing of your gameplay. Gameplay balancing is not an abstract process of number tweaking. To be effective gameplay and progression tuning need to be driven by qualitative considerations and a strategic understanding of user experience.
This post suggests way to approach your in-game economy, and the guiding principles that can help you optimize your in-game prices without going through an AB test.
Colopl reports an interesting metric: ARPQU. In doing so, they focus only on the metrics of users who launched the app at least once after 7 days of its download date. In this post I reflect on what this might mean, and how you can use this as part of your OKR process.