User activity decay in mobile games determined by simple differential equations?

Viljanen, Markus; Airola, Antti; Pahikkala, Tapio; Heikkonen, Jukka

doi:10.1109/cig.2016.7860403

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…In the simplest case, the hazard h (t) = λ is homogeneous implying the churn rate is constant over time. This hazard can be used to derive two well-known distributions: 6 Table 3, Weibull is one of the most popular [38], and it also has wide applicability in games [11,12,22].…”

Section: Connection Of Hazard and Survivalmentioning

confidence: 99%

“…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.…”

Section: Playtime In Games 1why Playtime Is Importantmentioning

confidence: 99%

“…Since in the industry it is often unfeasible to wait until all users have churned to obtain their total playtime, censoring is also present. Furthermore, user retention may not always completely follow popular parametric models [12], making model-free approaches attractive. The widespread recognition of survival analysis in fields with similar data and the demand for scientific analytics suggests that game analytics will benefit from this approach.…”

Section: Playtime In Games 1why Playtime Is Importantmentioning

confidence: 99%

“…A notable example is the study of total playtimes [22] in over 3000 Steam games totaling 6 million players which utilized the Weibull distribution for archetype analysis. Research with parametric models has often investigated exponential [23,24,35], Weibull [11,12,21,22,25,26,30], Gamma [21], log-logistic [12], log-normal [21,30,34] and Pareto-type [12,23,24,29] distributions.…”

Section: Related Researchmentioning

confidence: 99%

“…In free-to-play games, it is often observed that the failure rate is very high during initial sessions, and stabilizes or steadily continues to decline as most dedicated players remain [12]. In pay-to-pay games with campaigns, one may observe playtimes that are more clustered [34].…”

Section: Hazard As a Metricmentioning

confidence: 99%

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Playtime Measurement With Survival Analysis

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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...

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Section: Connection Of Hazard and Survivalmentioning

confidence: 99%