Playtime Measurement with Survival Analysis

TL;DR

This paper introduces survival analysis techniques for analyzing player playtime in games, providing visualizations, metrics, and statistical comparisons that work well with censored data, especially useful during game development.

Contribution

It presents new methods for applying survival analysis to game analytics without covariates, including visualization, metrics, and cohort comparison tools.

Findings

Survival curves effectively model player retention.

Metrics derived from survival curves summarize playtime.

Methods handle censored data and enable confidence interval computation.

Abstract

Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various duration variables quantifying product use: total playtime or session playtime are popular research targets, and active playtime is well-suited for subscription games. Such research often has the goal of increasing player retention or conversely decreasing player churn. Survival analysis is a framework of powerful tools well suited for retention type data. This paper contributes new methods to game analytics on how playtime can be analyzed using survival analysis without covariates. Survival and hazard estimates provide both a visual and an analytic interpretation of the playtime phenomena as a funnel type nonparametric estimate. Metrics based on the survival curve can be used to aggregate this playtime information into a single statistic. Comparison of survival curves between cohorts provides a scientific AB-test. All these methods work on censored data and enable computation of confidence intervals. This is especially important in time and sample limited data which occurs during game development. Throughout this paper, we illustrate the application of these methods to real world game development problems on the Hipster Sheep mobile game.