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Intelligencia.co

Casino & Hospitality

Intelligencia executives have been involved in the casino and hospitality for the past five years. With staff based in Hong Kong, Macau, Manila and Australia, Intelligencia's consultants have been intimately involved with projects at casinos and sports books throughout the region.

Intelligencia has been involved with BI, CI, marketing automation and analytics implementation at several casinos in Macau as well as offsite with clients in both Australia and the United States. Intelligencia's executives are some of the most knowledgeable consultants in the field of data mining, analytics and business intelligence.

These are the questions casino executives need to have answered when it comes to predictive analytics in the gaming industry:

  • How much is a patron worth, how much can we expect a patron to lose in the future, and who are the most valuable patrons?
  • What patrons come together?
  • What patrons are most likely to abuse an offer?
  • What patrons are most and least likely to respond to an offer?
  • What offers perform the best?

Once patron worth has been defined, the business can then use data mining and modeling to estimate predicted worth into the future. Simple metrics based on historical behavior, such as Average Daily Theoretical Loss or Average Trip Theoretical Loss, will produce fairly accurate predictions of future worth. However, advanced predictive models are able to predict worth with more accuracy and power by accounting for both patterns in behavior over time and relationships between predictive inputs that exist within casino data. There are a variety of techniques that are used to develop models to predict future worth, the most common being regression models.

Multiple regression models are the most commonly used for this because they utilize a variety of predictors and the relationships between those predictors to predict future worth. For example, a model built to predict future gaming trip worth might be generated based on historical information about theoretical win, actual win, credit line, time on device, nights stayed, and average bet. Regression models can also be built using such categorical variables predictors as gender, ethnicity, age range, or other demographic variables. Developing separate models based on categorical variables, such as separate models predicting worth for slot and table players might produce models with less error and better predictions. Regression models are particularly effective because the model can be used to score historical data to predict an unknown outcome, which is worth in this case, within a certain degree of confidence.

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