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Predictive analytics in the casino and hospitality business PowerPoint Presentation
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Predictive analytics in the casino and hospitality business

Predictive analytics in the casino and hospitality business

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Predictive analytics in the casino and hospitality business

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  1. Predictive analytics FOR the CASINO AND gaming industry

  2. Predictive Analytics in the Casino and Gaming Industry

  3. Predictive Analytics for the Gaming Industry

  4. Predictive Analytics for the Gaming Industry

  5. Predictive analytics extracts information from data sets and uses it to anticipate future trends and behavior patterns based on statistics and data mining (Ramakrishnan and Madure, 2008). The most important element of predictive analytics is the predictor, “a variable that can be measured for an individual or other entity to foresee future behavior” (Ramakrishnan and Madure, 2008). The real trick is to find the predictive model best suited for the outcome one is trying to study (Ramakrishnan and Madure, 2008) and this is no easy feat.

  6. Predictive analytics solutions include SAS's suite of analytics products, IBM's SPSS, EMC's Greenplum and Revolution's R open source product. Whichever solution is used, predictive analytics can enhance customer acquisition and retention, identify cross-sell and up-sell opportunities, identify customer lifetime value, spot fraud detection, determine the life cycle of a slot machine and help direct and improve marketing campaigns.

  7. Data Mining: An In-House Goldmine Data mining – the process whereby hidden patterns within data sets are discovered – is a component of predictive analytics that entails an analysis of data to identify trends and patterns of relationships among data sets (Ramakrishnan and Madure, 2008). To put is simply, data mining helps transform raw data into usable information.

  8. Data Mining: An In-House Goldmine By employing automated predictive analytics to sift through a casino operator’s customer database, data mining can discover hidden opportunities and connections that might otherwise be missed.Many casino operators have terabytes and terabytes of data – everything from customer player card information to information about a customer’s room preference – and sifting through this information to discover meaningful connections would be an impossible task without data mining

  9. Data Mining: An In-House Goldmine Data mining and predictive analytics aim to identify valid, novel, potentially useful and understandable correlations and patterns in datasets (Chung & Gray, 1999) by combing through copious amounts of data to sniff out patterns and relationships that are too subtle or complex for humans to detect (Kreuze, 2001). Data must be gathered from disparate sources and then seamlessly integrated into a data warehouse that can then cleanse it and make it ready for consumption.

  10. Data Mining: An In-House Goldmine Trends that surface from the data mining process can help in monetization, as well as in future advertising and marketing campaigns. For casinos, data mining can cull through data from such disparate sources and departments as sales and marketing, thereby allowing users to measure patron behavior on more than a hundred different attributes, which is a far cry from the three or four different attributes that statistical modeling used to offer.

  11. Applications for Predictive Analytics *Based on 167 respondents who have implemented predictive analytics solutions. Respondents could select multiple answers.

  12. Predictive Analytics can help to:

  13. Predictive Analytics can help to:

  14. Predictive Analytics

  15. Predictive Analytics

  16. Predictive Analytics can help to:

  17. Manipulating Customer Behavior

  18. Predictive Analytics – Survival or Duration Analysis:

  19. Predictive Analytics – Regression Analysis:

  20. Predictive Analytics – Linear Regression:

  21. Linear Regression As per the attached graph, we can make the assessment that an increase in average bet also increases actual win and, using the straight line, we could predict how much the actual win would be affected.

  22. Predictive Analytics – Linear Regression

  23. Predictive Analytics – Neural Networks

  24. Neural Networks Neural networks can be used to classify a consumer's spending pattern, analyze a new product, identify a patron's characteristics as well as forecast sales (Singh and Chauhan, 2009). The advantages of neural networks include high accuracy, high noise tolerance and ease of use as they can be updated with fresh data, which makes them useful for dynamic environments (Singh and Chauhan, 2009).

  25. Predictive Analytics - A/B Testing

  26. Predictive Analytics - A/B Testing

  27. Predictive Analytics – Decision Trees

  28. Predictive Analytics – Decision Trees For the casino and hospitality industry, decision trees can be used “to identify patron characteristics that can predict the likelihood of a patron (or segment of patrons) to abuse an offer” (Sutton, 2011). Figure 5 shows a decision tree for responses to a marketing campaign using age and zip code as the variables.

  29. Predictive Analytics – Time Series Model

  30. Predictive Analytics – Decision Trees A Time Series model can be used to predict or forecast the future behavior of a variable. These models account for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for. For the casino and hospitality industry, a Time Series Analysis can be used to forecast sales, project yields and workloads as well as analyze budgets.

  31. Predictive Analytics – Actionable Intelligence

  32. Predictive Analytics – Actionable Intelligence

  33. Predictive Analytics – Actionable Intelligence

  34. Predictive Analytics – Marketing Campaigns

  35. Predictive Analytics – Marketing Campaigns

  36. Predictive Analytics – Marketing Campaigns

  37. Predictive Analytics – Patron Worth

  38. Predictive Analytics for the Gaming Industry

  39. Predictive Analytics in the Casino and Gaming Industry • Other chapters include: • Customer Relationship Management • Casino Marketing • Mobile-izing your Marketing • Social Media • Table Games Revenue Management • The Asian Gambler • Compliance • A Winning Solution Book is available at

  40. Predictive Analytics in the Casino and Gaming Industry References: Chung, H. M. & Gray, P. 1999. Data mining. Journal of Management Information Systems, 16(1), 11-13. Kreuze, D. 2001. Debugging hospitals. Technology Review, 104(2), 32. Kumar, V., Raj Venkatesan and Werner Reinartz (2006). “Knowing what to sell when and to whom,” Harvard Business Review, 84 (3), 131. Ramakrishnan, Ramya and Madure, Rajashekharappa (2008). Predictive Analytics: Extending BI Structure. Information Management. December 16, 2008. Singh, Dr. Yashpal, Chauhan, Alok S., Neural Networks in Data Mining. Journal of Theoretical and Applied Information Technology. 2005 – 2009. Sutton, Scott. 2011. Patron analytics in the casino and gaming industry: how the house always wins. Paper 379-2011. SAS Global Forum 2011.

  41. For more information contact: Andrew Pearson