Researchers use Samsung data and AI to predict mobile game churn

Churn — that all-important measure of attrition — is often challenging to predict when it comes to mobile games. There are two types of churn — one at the micro level, between an app and a specific user, and the other at the macro level, between an app and all of its users. And the two influence each other in ways that are not at all intuitive. That’s problematic for publishers and developers alike, considering the cash at stake — $70.3 billion in revenue was generated by mobile games in 2018, according to NewZoom’s recent Global Games Market Report, and it is expected to climb to $106.3 billion in 2021.

A new method promises to provide greater understanding with the help of machine learning. It’s described in a paper published on the preprint server Arxiv.org (“Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games“), which was coauthored by a team of researchers hailing from Samsung Research America, Texas A&M University, University of Pittsburgh, and the University of Arizona.