1. Patron Segmentation Model: This model segments the population of patrons using cluster analysis and decision trees. Segmentation is one of the first and most basic machine learning methods. Segmenting the population of patrons into distinct segments is important because creating separate model for separate segments may be time consuming and not worth the effort. But, creating separate model for separate segments usually provides higher predictive power.
2. Overall Property Preference Model: This model uses logistic regression to identify whether a customer is attracted by Slots, Tables or Bingo (or combination).
3. Table Games Preference Model: This model identifies a players’ game of preference (for those playing tables).
4. Table Bet Size Preference Model: This model identifies player’s preferential stake size by grouping stake sizes into manageable groups. This information helps casinos know whether limits are high enough or minimums are too low.
5. Customer Play Day Preference Model: This model looks at patron behavior by day and identifies the day preference for attending. This model can be extended to time of day preference. This model helps casino managers with staffing and layout requirements when mixed with other analysis.
6. Slot Denomination Preference Model: This model describes player behavior by denomination. It identifies from penny players through max stake players. This model can also look at loyalty across denominations.
7. Slots Reels Preference Model: This model groups reels into manageable buckets. It helps identify those patrons who like the max reels type game as opposed to the simple three reels. The model is used in combination with the slot denomination preference model to drill in further into player characteristics.
8. F&B Location Preference Model: This model looks at carded spend across food venues to identify patron’s preference for eating. This model is useful for menu changes, and for the creation of marketing offers around F&B. For instance, It could show the steakhouse could benefit from opening earlier in the week lining up with when the big spenders are hoping to play.
9. Casino RFM Model: This model forecasts VIP players using revenue for slots, tables, bingo, F&B and Entertainment as well as frequency of visits and days since last visit.
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