Analytics-driven dynamic game adaption for player retention in Scrabble

Abstract

This paper shows how game analytics can be used in conjunction with an adaptive system in order to increase player retention at the level of individual game sessions in Scrabblesque, a Flash game based on the popular board game Scrabble. In this paper, we use game analytic knowledge to create a simplified search space (called the game analytic space) of board states. We then target a distribution of game analytic states that are predictive of players playing a complete game session of Scrabblesque in order to increase player retention. Our adaptive system then has a computer-controlled AI opponent take moves that will help realize this distribution of game analytic states with the ultimate goal of reducing the quitting rate. We test this system by performing a user study in which we compare how many people quit playing the adaptive version of Scrabblesque early and how many people quit playing a nonadaptive version of Scrabblesque early. We also compare how well the adaptive version of Scrabblesque was able to influence player behavior as described by game analytics. Our results show that our adaptive system is able to produce a significant reduction in the quitting rate (p = 0.03) when compared to the non-adaptive version. In addition, the adaptive version of Scrabblesque is able to better fit a target distribution of game analytic states when compared to the non-adaptive version.