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Author's profile photo Kristin Kufeldt

Predictive Thursdays: Actionable Intelligence—How to Influence Customer Behavior in Casinos

Our guest blogger today is Lelian Cestari, Head of SAP Practices, Qualex Consulting Services

poker_chips_Customer analytics have evolved from simply reporting patron behavior to segmenting customers based on profitability, to predicting that profitability, to improving those predictions (because of the inclusion of new data), to actually influencing customer behavior with target-specific promotional offers and marketing campaigns. Predictive analytics can graph a customer’s value over time as well as anticipate that customer’s behavior.

From this predictive analysis, a casino operator can tailor highly specific, laser-focused marketing campaigns to each customer in the casino’s patron database. By consolidating the various patron touchpoint systems throughout the casino property, the casino operator can create a full view of each patron.

How Does Predictive Analysis Work within a Casino?

Drawing on data from casino player cards, predictive models can set budgets and calendars for the casino’s gamblers, calculating their predicted lifetime value in the process. If a gambler wagers less than usual because they may have skipped a monthly visit, the casino can intervene with an email or text message offering a free meal, a show ticket, or gaming comps. Without these customer analytics, casino operators might not notice what could be a slight, almost imperceptible change in customer behavior that portends problems. For example, if a long-time customer decides to cash in all their player card points perhaps it’s because they are dissatisfied with their last experience at the casino.

Predictive analytics can quickly spot these trends and alert casino management to the issue so that they can approach the individual to find out if there is a problem. This kind of personalized attention can go a long way in appeasing disgruntled customers, which might be the difference between retaining or losing them as a customer.

Successful marketing is about reaching a consumer with an interesting offer when he or she is primed to accept that offer. Knowing what might interest a patron is half the battle of making a sale and this is where customer intelligence and predictive analytics come in.

The Evolution of Customer Analytics

Customer analytics has evolved from simply reporting customer behavior to segmenting customers based on their profitability, to predicting that profitability, to improving those predictions because of the inclusion of new data, to actually ‘hitting’ patrons with target-specific promotional offers and marketing campaigns.

Casino operators can enhance their customer relationships by cross-selling and up-selling items that the customer might actually be interested in, rather than offering them products they are likely to reject.

Predictive modeling is only useful if it is deployed and it creates an action. Taking advantage of the more powerful, statistically-based segmentation methods, customers can be segmented not only by dollar values but also on all known information, which can include behavioral information gleaned from resort activities, as well as patrons’ simple demographic information. Their more detailed segmentation allows for more targeted and customer-focused marketing campaigns.

When it comes to return of investment (ROI), it’s hard to get an exact figure with predictive analytic solutions because many companies who have implemented these solutions haven’t conducted formal ROI studies.

Modern casino analytics and patron management systems contain enormous amounts of highly detailed data about when, where, how often, and how much a casino patron is spending at a casino property. When it comes to casino patron analytics, casino operators must seek answers to the following questions:

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

Join us here on the SAP BusinessObjects Analytics blog every Thursday for new posts about all things predictive (and read the previous series posts ).

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