Extracting gamers' cognitive psychological features and improving performance of churn prediction from mobile games

With the continued growth of the mobile game market, many game companies aim to make money through mobile games. In this situation, knowing the tendency of gamers and predicting the churn in advance can maximize profit through effective game services. For this reason, much study has been conducted for the purpose of gamer analysis and churn prediction. However, the study was mainly conducted using surveys, bio-signals, and PC online game logs, which are likely to make detailed information. In this study, we extracted seven cognitive psychological features from the game logs of Crazy Dragon, a commercial mobile RPG game, and used these to predict the churn. In addition, we analyzed the effect of purchasing feature by comparing the churn prediction performance according to presence or absence of purchase feature. In the conclusion, we obtained higher performance when predicting the churn using cognitive psychological features than using the basic raw logs. Also, we obtained high churn prediction performance using only cognitive psychological features without purchase feature.