Player Activity Sequence Analysis Using Process Mining : Player churn prediction and Abnormal player sequences detection using process mining on the data from a live game

Abstract: Background: Game analytics is a field that aims to analyze games and help in the enhancement of game development. Data mining is a prominent technique for game analytics. Recent advances in the field of process mining have motivated users to apply process mining to real-world scenarios in order to derive process-oriented insights. In this study, We provide a discussion on how process mining can be used in game analytics. Objective: The goal of this study is to apply process mining to player data from a live game, analyze the results, and determine whether these results can be interpreted, whether we can derive any patterns or insights that can be useful for game designers, and whether process mining can be used in-game analytics and, if so, what kind of versatility it can offer. Also, this study provides approaches on how process mining can be used in player churn prediction and determination of abnormal player activity sequences. Method: Firstly, a literature review is performed to comprehend all of the process mining techniques and metrics used to evaluate the discovered process models. Then experiments are conducted by applying process mining on data from a live game, determine a churn predictor using process mining and determining a technique to identify abnormal player sequences. Results: Process discovery algorithms are applied on data from a live game, the results are analyzed. Several process models are discovered to identify player churn and it is compared with a baseline machine learning churn predictor trained on the same data to that of process mining. Abnormal player activity sequences of the gameare determined using process mining and compared with expected player sequences and analyzed with the help of game designers. Conclusion: Process mining can be utilized in game analytics to discover new process-oriented insights. When compared to typical data mining techniques, the results gained by process mining are more versatile. It also has other capabilities such as detecting unusual sequences in data.