How players lose interest in playing a game: An empirical study based on distributions of total playing times

Abstract

Analyzing telemetry data of player behavior in computer games is a topic of increasing interest for industry and research, alike. When applied to game telemetry data, pattern recognition and statistical analysis provide valuable business intelligence tools for game development. An important problem in this area is to characterize how player engagement in a game evolves over time. Reliable models are of pivotal interest since they allow for assessing the long-term success of game products and can provide estimates of how long players may be expected to keep actively playing a game. In this paper, we introduce methods from random process theory into game data mining in order to draw inferences about player engagement. Given large samples (over 250,000 players) of behavioral telemetry data from five different action-adventure and shooter games, we extract information as to how long individual players have played these games and apply techniques from lifetime analysis to identify common patterns. In all five cases, we find that the Weibull distribution gives a good account of the statistics of total playing times. This implies that an average players interest in playing one of the games considered evolves according to a non-homogeneous Poisson process. Therefore, given data on the initial playtime behavior of the players of a game, it becomes possible to predict when they stop playing

Why Do Small and Medium-Size Freemium Game Developers Use Game Analytics?

“…Prior research has presented the vast possibilities and introduced sophisticated solutions for game analytics [1,3]. However, our results imply that the analytics processes used by small and medium-sized freemium developers are rather simple.…”

“…Use of big data and analytics has become pervasive in the video game industry [1]. The adoption of analytics has been driven by the fast development of cost-effective solutions that enable basic analytics even for start-up sized game developers.…”

“…The adoption of analytics has been driven by the fast development of cost-effective solutions that enable basic analytics even for start-up sized game developers. The second driver of game analytics is the diffusion of the freemium business model that has created a need to accurately measure, predict, and intervene player behavior 1 . For example, Supercell, the company behind the top-grossing freemium games Clash of Clans and Boom Beach, generated revenue of 1.8 billion USD from in-game purchasing in 2014 2 .…”

Identifying Learner’s Engagement in Learning Games - A Qualitative Approach based on Learner’s Traces of Interaction

“…As engagement influences the behaviour, some measurable quantities can be considered for identifying engaged-behaviours (Bauckhage et al, 2012). For studying the impact of tutorials on players' engagement in digital entertainment games, (Andersen et al, 2012) collect some raw data like the number of unique levels completed, the total playing time and the number of times players have loaded the game.…”

Design the churn analysis on games: a review on techniques for churn analysis

“…The most identifying characteristic is that the "failure rate", or the rate that players lost their interests in the games, is affected by the shape parameter, which refers to the first passage time. Since the beginning passage time is larger than 1, the rate that players lost interests is increasing with time (Wikipedia, 2018), (Christian Bauckhage, Kristian Kersting, Rafet Sifa, Christian Thurau, Anders Drachen, Alessandro Canossa, 2012).…”