Playtime Measurement With Survival Analysis

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. Playtime in games 1.Why Playtime is ImportantGame analytics is becoming increasingly important in understanding player behavior [1]. Widespread adoption * M. Viljanen, A. Airola, J. Heikkonen, and T. Pahikkala are with the Department of Information Technology, University of Turku, 20014 Turku, emails: majuvi@utu.fi, ajairo@utu.fi, jukhei@utu.fi, aatapa@utu.fi of games, internet connectivity and new business models have resulted in data gathering in an unprecedented scale. With increasing availability of data, researches and industry alike are motivated to gain insight into the data through game analytics.Focal point of analytics is player retention and churn [2]. Retention has been used in connection with many related measures and methods aiming to increase the length of product use [3][4][5][6][7][8][9][10][11][12]. Better retention simply means players are engaged with the game for longer. Player churn, meaning players quitting the game either momentarily or definitely, decreases product use and is therefore a counterpart of retention. It has also been extensively researched [13][14][15][16][17][18][19][20]. Retention metrics are popular because they are thought to reflect player enjoyment, and increased product use provides increased possibilities for monetization in free-to-play and subscription based games. Game success may be attributed to the process of acquiring new users and retaining these users with effective monetization [2].Of actual measures that quantify retention in analytics, total playtime is a highly useful overall retention metric [21,22] and session playtime [23][24][25][26][27][28][29][30] can be utilized to measure in-game retention. Discrete metrics such as session count...

Your Gameplay Says It All: Modelling Motivation in Tom Clancy’s The Division

“…Clustering is based on unsupervised learning methods with the aim to cluster players within groups of common behavioural patterns; approaches include k-means, self-organising maps [7], matrix factorisation and archetypal analysis [18], [19], and sequence mining [8], [20], [21]. Prediction uses supervised learning to predict patterns of playing such as completion time [22] and churn [23]- [25]. Predictive models can predict a player's behaviour (i.e., what would a player do?)…”

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]. Similarly, it can provide insight in how player activity and popularity affects retention within games [55].…”

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].…”

“…Additionally, we acquired a single context feature specifying the game context from where the metrics were originated. For determining the targets for our survival and churn estimation tasks, we leveraged existing literature on churn prediction [1]- [4], [19], [22], [23], [26] and survival analysis [6], [20], [21], [26], extending existing rules to accommodate the need to define churn and survival time in single player games with a defined life cycle (i.e. non-GaaS games).…”

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