Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles

Abstract

Abstract-Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn.Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results.In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression.

Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE

“…E.g. [2,[18][19][20][21] investigate the prediction of player disengagement/churn, [22][23][24] focus on player retention and [1] predict players' purchase decisions in mobile free-to-play games. Player engagement and purchasing are at the core of players' value to a company, and so is their combined outcome: Monetary LTV.…”

From the Hands of an Early Adopter’s Avatar to Virtual Junkyards: Analysis of Virtual Goods’ Lifetime Survival

“…Another area that is being explored is churn prediction in mobile games using survival ensembles [52] and player-motivation theories [53]. While game-time survival analysis can be used as a predictor of user engagement, it can also provide knowledge regarding factors that affect gameplay duration [54].…”

Modelling Early User-Game Interactions for Joint Estimation of Survival Time and Churn Probability

“…For this reason we propose survival time and churn as behavioural approximations of future sustained engagement and disengagement. Generally speaking, survival time can be defined as the amount of playing activity occurring between the end of an observation period and the last activity recorded for a specific user [6], [19]- [22]. Churn can be defined as the decision of a user to stop interacting with a specific service due to internal or external reasons, usually formalized as a user entering a prolonged period of inactivity [1]- [4], [22].…”