PREDICTING CROSS-GAMING PROPENSITY USING E-CHAID ANALYSIS

Cross-selling different types of games could provide an opportunity for casino operators to generate additional time and money spent on gaming from existing patrons. One way to identify the patrons who are likely to cross-play is mining individual players’ gaming data using predictive analytics. Hence, this study aims to predict casino patrons’ propensity to play both slots and table games, also known as cross-gaming, by applying a data-mining algorithm to patrons’ gaming data. The Exhaustive Chi-squared Automatic Interaction Detector (E-CHAID) method was employed to predict cross-gaming propensity. The E-CHAID models based on the gaming-related behavioral data produced actionable model accuracy rates for classifying cross-gamers and non-cross gamers along with the cross-gaming propensity scores for each patron. Using these scores, casino managers can accurately identify likely cross-gamers and develop a more targeted approach to market to them. Furthermore, the results of this study would enable casino managers to estimate incremental gaming revenues through cross-gaming. This, in turn, will assist them in spending marketing dollars more efficiently while maximizing gaming revenues

Customer Retention: Reducing Online Casino Player Churn Through the Application of Predictive Modeling

With the potential expansion of legalized online gaming in the United States as well as in the global market, customer retention is critical to the continued growth and success of an online casino. While customer churn prediction can be an essential part of customer retention efforts, it has received very little attention in the gaming literature. Using historical online gaming data, this study examines whether player churn (attrition) can be predicted through an application of a decision tree data mining algorithm called Exhaustive CHAID (E-CHAID). The results of this empirical study suggest that the predictive model based on the E-CHAID method can be a valuable tool for identifying potential churners and understanding their churn behavior. Additionally, this study shows how the classification rules and propensity scores extracted from a decision tree churn model can be used to identify players at risk of churn. The patron play and visitation parameters that are closely associated with churn are also discussed. This study contributes to the gaming literature by focusing on online players’ churn prediction through a data-driven approach. Finally, it discusses proactive approaches for churn prevention

Examining the Effects of Various Promotion Types On Slot Gaming Volumes

This study examines the effects of different types of casino promotions on daily slot volumes, using data from two riverboat casinos located in the Southern and Midwestern regions of the United States. Results suggest that promotions featuring drawings for big prizes, such as large amounts of cash, cars and boats, were more effective in generating incremental slot volumes than those with small prizes. However, drawing frequency and slot volume were not significantly related. Play incentives were positively associated with slot gaming volume, while slot tournaments and player events had no significant effect on slot volume. (C) 2013 Elsevier Ltd. All rights reserved