# Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games

## TL;DR

This paper introduces a large-scale analysis of churn in mobile games, proposing novel models for micro-level prediction and macro-level ranking, validated on real-world data from Samsung's platform.

## Contribution

It presents the first comprehensive large-scale framework supporting both micro-level churn prediction and macro-level churn ranking in mobile games, with novel deep learning and graph-based methods.

## Key findings

- Effective micro-level churn prediction with a semi-supervised deep model
- Accurate macro-level churn ranking using graph algorithms
- Validated on real-world Samsung data

## Abstract

As mobile devices become more and more popular, mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. A critical challenge for these platforms and services is to understand the churn behavior in mobile games, which usually involves churn at micro level (between an app and a specific user) and macro level (between an app and all its users). Accurate micro-level churn prediction and macro-level churn ranking will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn analysis for mobile games that supports both micro-level churn prediction and macro-level churn ranking. For micro-level churn prediction, in view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user-app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To address macro-level churn ranking, we propose to construct a relationship graph with estimated micro-level churn probabilities as edge weights and adapt link analysis algorithms on the graph. We devise a simple algorithm SimSum and adapt two more advanced algorithms PageRank and HITS. The performance of our solutions for the two-level churn analysis problems is evaluated on real-world data collected from the Samsung Game Launcher platform.