Using Data Mining Methods to Improve Cross- Gaming Prediction in the Gaming Industry

• 영문명: Using Data Mining Methods to Improve Cross- Gaming Prediction in the Gaming Industry
• 발행기관: 한국관광학회
• 저자명: Eunju Suh Matt Alhaery
• 간행물 정보: 『한국관광학회 국제학술발표대회집』제78차 한국관광학회 국제학술발표대회집, 384~401쪽, 전체 18쪽
• 주제분류: 사회과학 > 관광학
• 파일형태: PDF
• 발행일자: 2015.07.01

영문 초록

Considering a variety of casino games and slot machines offered on many casino floors, high utilization of games through cross-selling is likely to lead to an increase in gaming volume and player visitation frequency. Using the real-world gaming data, this study examined various data mining methods to improve the prediction accuracy for cross-gaming. Cross-gaming in this study refers to slot players’ table game play and table game players’ slot play. Of the various data mining methods, C5 and an ensemble model consisting of decision tree classification models outperformed other models in accurately predicting potential cross-gamers. Using the cross-gaming propensity scores derived from these models, casino managers can improve their target marketing efforts for cross-gaming.

키워드

atabase marketing Data mining Predictive Analytics Classification Casino operations Cross-selling