35 research outputs found

    PREDICTING CROSS-GAMING PROPENSITY USING E-CHAID ANALYSIS

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    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

    Estimating the impact of showroom entertainment on the hourly gaming volume of a Las Vegas hotel-casino

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    Along with the emergence of mega casino-resorts in the gaming industry, researchers have attempted to estimate the indirect gaming contributions of non-gaming casino amenities such as showroom entertainment and restaurants. However, the daily data of aggregate gaming volumes analyzed in previous gaming research did not allow exploring transient gaming volumes associated with casino amenities during a much narrower range of time periods (i.e., hourly). The current investigation addresses this limitation by proposing a model to examine the relationship between showroom headcounts and hourly slot gaming volumes for the hours falling adjacent to the show’s performance time. Considering a major investment in showroom entertainment at many casinos, the current study will help casino operators evaluate the showroom’s slot gaming contribution

    Maximizing the impact of sponsorship: An examination of sponsorship on attendees\u27 recognition of sponsors and their attitudes toward corporate sponsorship

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    The purpose of this study was to measure the impact of sponsorships on trade show attendees. This study sought to understand whether different types of sponsorships, number of days attending at a show, and demographic characteristics differently influenced attendees\u27 recognition of sponsors and their overall attitudes toward corporate sponsorship; For this study, a questionnaire was designed to measure trade show attendees\u27 recognition of sponsoring companies, overall attitudes toward sponsorship, preferences for specific sponsorship types, and demographic information. Attendees were queried while exiting the Association of Progressive Rental Organizations\u27 Convention and Trade show (APRO) in Las Vegas from July 24 and to 25, 2002; In the recognition test, names of actual sponsors and non-sponsors (companies who were exhibitors only) were listed on the questionnaire and attendees were asked to indicate whether or not they recognized the name of sponsors at the show by checking yes or no. In addition, measurements to detect whether these different sponsorship types influenced attitudes towards corporate sponsorships in general were made. (Abstract shortened by UMI.)

    Estimating the impact of entertainment on the gaming volume of Las Vegas hotel casinos

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    This study addressed the indirect effect of entertainment on gaming volume (i.e., coin-in). Specifically, this study attempted (1) to gain an understanding of the relationship between show patronage and gaming volume; and (2) to estimate the magnitude of incremental revenue for each show attendee. Conceptual models to examine the indirect effect of daily show headcounts on gaming volume were proposed, including other variables previously found or theorized to influence gaming volume. Secondary data (i.e., show headcounts, daily coin-in and daily cash drop) were collected from two different Las Vegas Strip properties. This study employed multiple regression models with the appropriate autoregressive (AR) and moving average (MA) errors, to adjust or correct for autocorrelation present in time series data. Hypotheses associated with the show headcount variables were tested at a .10 alpha level, given the exploratory nature of this research; In regression models associated with the first subject property, the show headcount variable had a significant effect on both coin-in and cash drop. This finding supports conventional wisdom that shows drive gaming volume. Despite the positive linear correlation between show headcounts and gaming volumes, the economic significance of the incremental win per show attendee was not substantial. For the second subject property, the impact of show headcounts on coin-in was not statistically significant, whereas show headcounts had a significant influence on cash drop. In general, the results of this study suggest that show-goers are not necessarily avid gamblers; The findings of this study point to the importance of careful selection, investment and management of entertainment options. If the purpose of a show is to complement casino gaming, it should produce a strong spillover effect on gaming volume. If not, the show should be profitable on its own. It also better position itself as a necessary component of a full-service resort. With the findings of the current work, casino operators could further evaluate whether show attendees produce sufficient returns on investment. Additionally, this study adds valuable empirical results to the limited literature base associated with the impact of entertainment on gaming volume. Finally, it provides a platform for future research in this area

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

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    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
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