Qualex Asia's predictive analytics solution for the gaming industry


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Gambling – the wagering of money or something of material value on an event with an uncertain outcome with the primary intent of winning additional money and/or material goods – has been with us since ancient times. Greek mythology tells the story of Poseidon, Zeus and Hades dividing the world between them in a dice game; Poseidon won the sea, Zeus the heavens and Hades the underworld. The land, I suppose, was left to the rest of us.
Gambling has, of course, changed dramatically since those ancient times. Today’s casino operators are faced with a gambler who is much more sophisticated than the ancient Roman soldier who tossed a coin in the air and called “Heads or Ships.”

To succeed in today’s highly competitive gaming landscape, casino operators must understand their customers like never before. Luckily for them, we have entered a brave new world of gambling and entertainment where customers knowingly – and oftentimes unknowingly – leave clues to their gambling behavior. Player cards allow casinos to get a deeply personal view of their patrons, a view that can reveal not only the patron's gambling habits but also their dining, spa and shopping spend throughout the casino property. Predictive analytics can analyze this data, quantify every dollar a customer spends on property and then predict the customer’s unique value to the casino, while also providing clues that will make marketing campaigns directed to this individual much more effective than scattershot direct mailing campaigns.

Intensive competition in the gaming industry is making it more and more important for casino operators to not only pinpoint the small percentage of players who make up a large percentage of their profits, but also to create a long and lasting relationship with these players. By learning about a customer’s unique wants, desires and needs, a casino property can market directly to those wants, desires and needs, thereby making their marketing efforts much more effective.

Double Down On Your Data was written to show casino management how to best cull through their in-house patron data to discover who their most profitable patrons are, and also to show them how to market to these individuals to create a long, lasting and highly profitable relationship.

Double Down On Your data will help casino executives understand all of the current tools available to casino operators; tools that can help them understand their patron's better; tools that can help them create better prediction models; tools that can help them market to their players more effectively; and, most importantly, tools that can help them raise their casino's ROI.

Gambling has been with us since ancient times and it will undoubtedly be with us forever; it is just too ingrained within the human psyche to ever go away. Double Down On Your Data concludes with a description of an ideal solution that gives casino managers a clear understanding of their patrons. A solution design

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Qualex Asia's predictive analytics solution for the gaming industry

  1. 1. Predictive Analytics in the Casino and Gaming Industry
  2. 2. Predictive Analytics for the Gaming Industry“In business, as in baseball, the question isnt whether ornot youll jump into analytics. The question is when. Do youwant to ride the analytics horse to profitability...or follow itwith a shovel?” -- Rob Neyer, ESPN
  3. 3. Predictive Analytics for the Gaming IndustryPredictive analytics refers to a variety of statisticaltechniques that analyze current and historical facts tomake predictions about future events. Using suchtechniques as predictive modeling, machine learning,data mining and game theory, predictive analytics canbuild models that exploit patterns found in historicaland transactional data to identify business risks and,just as importantly, opportunities.
  4. 4. Predictive analytics can be broken down into threedifferent types of models:Predictive: these analyze past performance to predictthe likelihood that an individual customer will exhibit aspecific behavior in the future.Descriptive: these identify different relationshipsbetween customers to group or segment them formarketing or other purposes.Decision: these predict outcomes of complex decisions,relationships, products and/or processes.
  5. 5. Predictive analytics extracts information from datasets and uses it to anticipate future trends andbehavior patterns based on statistics and datamining (Ramakrishnan and Madure, 2008). Themost important element of predictive analytics is thepredictor, “a variable that can be measured for anindividual or other entity to foresee future behavior”(Ramakrishnan and Madure, 2008). The real trick isto find the predictive model best suited for theoutcome one is trying to study (Ramakrishnan andMadure, 2008) and this is no easy feat.
  6. 6. Predictive analytics solutions include SASs suite ofanalytics products, IBMs SPSS, EMCs Greenplumand Revolutions R open source product. Whicheversolution is used, predictive analytics can enhancecustomer acquisition and retention, identify cross-sell and up-sell opportunities, identify customerlifetime value, spot fraud detection, determine thelife cycle of a slot machine and help direct andimprove marketing campaigns.
  7. 7. Data Mining: An In-House GoldmineData mining – the process whereby hidden patternswithin data sets are discovered – is a component ofpredictive analytics that entails an analysis of data toidentify trends and patterns of relationships amongdata sets (Ramakrishnan and Madure, 2008). To putis simply, data mining helps transform raw data intousable information.
  8. 8. Data Mining: An In-House GoldmineBy employing automated predictive analytics to siftthrough a casino operator’s customer database,data mining can discover hidden opportunities andconnections that might otherwise be missed. Manycasino operators have terabytes and terabytes ofdata – everything from customer player cardinformation to information about a customer’s roompreference – and sifting through this information todiscover meaningful connections would be animpossible task without data mining
  9. 9. Data Mining: An In-House GoldmineData mining and predictive analytics aim to identifyvalid, novel, potentially useful and understandablecorrelations and patterns in datasets (Chung & Gray,1999) by combing through copious amounts of datato sniff out patterns and relationships that are toosubtle or complex for humans to detect (Kreuze,2001). Data must be gathered from disparatesources and then seamlessly integrated into a datawarehouse that can then cleanse it and make itready for consumption.
  10. 10. Data Mining: An In-House GoldmineTrends that surface from the data mining processcan help in monetization, as well as in futureadvertising and marketing campaigns.For casinos, data mining can cull through data fromsuch disparate sources and departments as salesand marketing, thereby allowing users to measurepatron behavior on more than a hundred differentattributes, which is a far cry from the three or fourdifferent attributes that statistical modeling used tooffer.
  11. 11. Applications for Predictive AnalyticsCross-sell/Up-sell – 47%Campaign Management – 46%Customer Acquisition – 41%Budgeting and Forecasting – 41%Attrition/churn/retention – 40%Fraud Detection – 32%Promotions – 31%Pricing – 30%Demand Pricing – 30%Customer Service – 26%Quality Improvement – 25% *Based on 167 respondents who have implemented predictive analytics solutions. Respondents could select multiple answers.
  12. 12. Predictive Analytics can help to:– Identify – the casinos most valuable patrons.– Predict a patrons future worth and/or his or her futurebehavior.– Plan the timing and placement of advertising campaigns.– Create personalized advertisements.– Define which market segments are growing most rapidly.– Segment patrons into groups based on their behaviors andthen create marketing campaigns to exploit thosebehaviors.
  13. 13. Predictive Analytics can help to:– Determine a patrons level of gambling skill.– Identify patrons who come together.– Identify the likelihood a patron will respond to an offer.– Identify the offer(s) to which patrons are most likely torespond to.– Predict when a patron is likely to return.
  14. 14. Predictive AnalyticsIn their article “Knowing What to Sell, When, and toWhom,” authors V. Kumar, R. Venkatesan, and W.Reinartz (2006) showed how, by simply understanding andtweaking behavioral patterns, they could increase the hitrate for offers and promotions to consumers, which thenhad an immediate – and substantial – impact on revenues.By applying statistical models based on the work of Nobelprize-winning economist Daniel McFadden, researchersaccurately predicted not only a specific person’spurchasing habits, but also the specific time of thepurchase to an accuracy of 80% (Venkatesan andReinartz, 2006).
  15. 15. Predictive AnalyticsThe potential to market to an individual when he or she isprimed to accept the advertising is advantageous for bothparties involved; marketers don’t waste time advertising toconsumers when they aren’t primed to accept theadvertisements, but do market to consumers when andwhere they might want to use the advertisements.
  16. 16. Predictive Analytics can help to:With predictive analytics, gamingorganizations can easily segmenttheir customers and coordinatemarketing campaigns to effectivelytarget each segment across eachoutbound channel. A profit curve(shown in the following diagram)estimates the profit a casino operatorwill receive from a campaign guidedby predictive analytics, depending onhow many prospects are contacted.The profit this curve predicts dependson the ranking of the casinoscustomers given by a predictivemodel, the cost per contact (e.g.,printing and mailing costs) and theaverage profit per respondent.
  17. 17. Manipulating Customer BehaviorSuccessful marketing is about reaching a consumer with aninteresting offer when he or she is primed to accept it.Knowing what might interest a patron is half the battle to makinga sale and this is where customer intelligence and predictiveanalytics comes in.Customer analytics has evolved from simply reporting customerbehavior to segmenting customers based on their profitability topredicting that profitability, to improving those predictions, toactually manipulating customer behavior with target-specificpromotional offers and marketing campaigns.
  18. 18. Predictive Analytics – Survival or Duration Analysis: A branch of statistics involves the modeling of time to event data; in this context, death or failure is considered an event in the survival analysis literature – traditionally only a single event occurs, after which the organism or mechanism is dead or broken. Survival Analysis is the study of lifetimes and their distributions. It usually involves one or more of the following objectives: – To explore the behavior of the distribution of a lifetime. – To model the distribution of a lifetime. – To test for differences between the distributions of two or more lifetimes. – To model the impact of one or more explanatory variables on a lifetime distribution.
  19. 19. Predictive Analytics – Regression Analysis:Regression analysis is the process of predicting the continuousdependent variable from a number of independent variables. Itattempts to find a function which models the data with the leasterror. Regression analysis can be used on data which is eithercontinuous or dichotomous, but cannot be used to determine acausal relationship. Regression analysis focuses on establishinga mathematical equation as a model to represent the interactionsbetween the different variables under consideration. Regressionmodels are particularly effective to find patron worth because themodel can be used to score historical data to predict an unknownoutcome.
  20. 20. Predictive Analytics – Linear Regression:These analyze the relationship between the response ordependent variable and a set of independent orpredictor variables. This relationship is expressed as anequation that predicts the response variable as a linearfunction of the parameters. These parameters areadjusted so that a measure of fit is optimized. Much ofthe effort in model fitting is focused on minimizing thesize of the residual, as well as ensuring that it israndomly distributed with respect to the modelpredictions. An important assumption of regressionanalysis is linearity, which defines a straight linerelationship between Independent variables anddependent variables.
  21. 21. Linear RegressionAs per the attachedgraph, we can make theassessment that anincrease in average betalso increases actualwin and, using thestraight line, we couldpredict how much theactual win would beaffected.
  22. 22. Predictive Analytics – Linear RegressionFor the casino and hospitality industry, regressionmodels can be used to predict a patrons future worth(Sutton, 2011). Multiple regression models “utilize avariety of predictors and the relationships between thosepredictors to predict future worth” states Sutton (2011).As an example, Sutton (2011) explains that “a modelbuilt to predict future gaming trip worth might begenerated based on historical information abouttheoretical win, actual win, credit line, time on device,nights stayed, and average bet.”
  23. 23. Predictive Analytics – Neural NetworksArtificial Neural Networks (ANN) or just “NeuralNetworks” are non-linear statistical data modeling toolsthat are used when the exact nature of a relationshipbetween input and output is unknown.Neural networks can be used to find patterns in data. Akey feature of neural networks is that they learn therelationship between inputs and output through training.
  24. 24. Neural NetworksNeural networks can be used toclassify a consumers spendingpattern, analyze a new product,identify a patrons characteristicsas well as forecast sales (Singhand Chauhan, 2009). Theadvantages of neural networksinclude high accuracy, high noisetolerance and ease of use as theycan be updated with fresh data,which makes them useful fordynamic environments (Singh andChauhan, 2009).
  25. 25. Predictive Analytics - A/B TestingAlso known as split testing or bucket testing, A/B testing is amethod of marketing testing by which a baseline control sampleis compared to a variety of single-variable test samples in orderto improve response rates. A classic direct mail tactic, thismethod has been recently adopted within the interactive space totest tactics such as banner ads, emails and landing pages. Forcasino marketers, A/B testing is the most effective way to identifythe best available marketing offer. A/B testing involves testing twodifferent offers against one another in order to identify the offerthat drives the highest response and the most revenue/profit.
  26. 26. Predictive Analytics - A/B Testing
  27. 27. Predictive Analytics – Decision TreesUsed to identify the strategy that is most likely to reacha goal. Decision trees are a decision support tool thatuse graphs or models of decisions and their possibleconsequences, including chance event outcomes,resource costs, and utility. Decision trees are sequentialpartitions of a set of data that maximize the differencesof a dependent variable (response or output variable).They offer a concise way of defining groups that areconsistent in their attributes, but which vary in terms ofthe dependent variable.
  28. 28. Predictive Analytics – Decision TreesFor the casino and hospitalityindustry, decision trees can beused “to identify patroncharacteristics that can predictthe likelihood of a patron (orsegment of patrons) to abusean offer” (Sutton, 2011). Figure5 shows a decision tree forresponses to a marketingcampaign using age and zipcode as the variables.
  29. 29. Predictive Analytics – Time Series ModelA time series is an ordered sequence of values of a variable atuniformly spaced time intervals. According to the EngineeringStatistics Handbook, time series models can be used to:-- Obtain an understanding of the underlying forces andstructure that produces an observed data;-- Fit a model and proceed to forecasting, monitoring or evenfeedback and feedforward control.
  30. 30. Predictive Analytics – Decision TreesA Time Series model can be usedto predict or forecast the futurebehavior of a variable. Thesemodels account for the fact thatdata points taken over time mayhave an internal structure (suchas autocorrelation, trend orseasonal variation) that should beaccounted for. For the casino andhospitality industry, a Time SeriesAnalysis can be used to forecastsales, project yields andworkloads as well as analyzebudgets.
  31. 31. Predictive Analytics – Actionable IntelligenceCustomer analytics have evolved from simply reporting patronbehavior to segmenting customers based on profitability, topredicting that profitability, to improving those predictions(because of the inclusion of new data), to actually manipulatingcustomer behavior with target-specific promotional offers andmarketing campaigns.Predictive analytics can graph a customer’s value over time aswell as anticipate that customer’s behavior. From this analysis,a casino operator can tailor highly specific, laser-focusedmarketing campaigns to each customer in the casino’s patrondatabase. By consolidating the various patron touchpointsystems throughout the casino property, the casino operatorcan create a full view of each patron.
  32. 32. Predictive Analytics – Actionable IntelligenceDrawing on data from casino player cards, predictive modelscan set budgets and calendars for the casinos gamblers,calculating their predicted lifetime value in the process. If agambler wagers less than usual because they may haveskipped a monthly visit, the casino can intervene with a letteror phone call offering a free meal, a show ticket or gamingcomps. Without these customer analytics, casino operatorsmight not notice what could be a slight, almost imperceptiblechange in customer behavior that portends problems. Forexample, if a long-time customer decides to cash in all theirplayer card points perhaps it’s because they are dissatisfiedwith their last experience at the casino. Predictive analyticscan quickly spot these trends and alert casino management tothe issue so that they can approach the individual to find out ifthere is a problem.
  33. 33. Predictive Analytics – Actionable IntelligenceThis kind of personalized attention can go a long way inappeasing disgruntled customers, which might be thedifference between retaining or losing them as a customer.Predictive analytics can glean data from a variety of disparatesources, including:-- Data integrated throughout the casinos gaming systems.-- Feedback information derived from post-visit surveys.-- Web data mining from customer’s individual online behavior.Social media websites.
  34. 34. Predictive Analytics – Marketing CampaignsWith predictive analytics, gaming organizations can easily segment theircustomers and coordinate marketing campaigns to effectively targeteach segment across each outbound channel. For example, if a casinocustomer is scheduled to receive all of his or her event promotions viae-mail, the predictive analytics solution will automatically remove himfrom concurrent campaigns being run through other channels. Thisensures consistency and also improves customer satisfaction, since theorganization respects the customer’s contact preference and doesn’tinundate him or her with multiple offers. Moreover, a predictive analyticssolution monitors channel capacity and usage to eliminate overload,while distributing campaigns equally across the various channels. If onechannel is at risk of overload, the solution automatically shifts theremainder of a campaign to a different channel to ensure completion.This enables organizations to maximize the capacity and value of eachchannel without resorting to time-consuming manual monitoring.
  35. 35. Predictive Analytics – Marketing CampaignsBy utilizing data from past campaigns and measures generatedby the predictive modeling process, casino operators can trackactual campaign responses versus expected campaignresponses, which can often prove wildly divergent.Additionally, casino operators can generate upper and lowercontrol limits that can be used to automatically alert campaignmanagers when a campaign is over or underperforming, lettingthem focus on campaigns that specifically require attention.
  36. 36. Predictive Analytics – Marketing CampaignsSutton (2011) claims that, when it comes to casino patronanalytics, casino operators must seek answers to the followingquestions:How much is a patron worth, how much can we expect a patronto 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 the most and least likely to respond to anoffer?-- Which offers perform the best?
  37. 37. Predictive Analytics – Patron WorthOnce patron worth has been defined, data mining and modelingtechniques can be used to estimate predicted worth in the future(Sutton, 2011). “Simple metrics based on historical behavior, suchas Average Daily Theoretical Loss or Average Trip TheoreticalLoss, will produce fairly accurate predictions of future worth,”Sutton (2011) notes. “However, advanced predictive models areable to predict worth with more accuracy and power byaccounting for both patterns in behavior over time andrelationships between predictive inputs that exist within casinodata,” Sutton (2011) argues
  38. 38. Predictive Analytics for the Gaming IndustryCasino operators should keep in mind that data mining willonly be successful if their casino patrons are willing to provideinformation on themselves. Privacy is a big issue and willalways remain so in the mobile age. Casino properties that canhonor a patrons privacy demands will find patron loyaltycomes with voluminous amounts of priceless patron data. Thisis data that can be used to create marketing campaigns thatshould prove highly effective. By understanding what type ofpatron is on its property, why they are there, and what theylike to do while they are there, casino operators canindividualize their marketing campaigns so that thesecampaigns are more effective than normal campaigns, therebyincreasing the casino propertys ROI.
  39. 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 Amazon.com
  40. 40. Predictive Analytics in the Casino and Gaming IndustryReferences:Chung, H. M. & Gray, P. 1999. Data mining. Journal of Management InformationSystems, 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 whenand 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 ofTheoretical and Applied Information Technology. 2005 – 2009.Sutton, Scott. 2011. Patron analytics in the casino and gaming industry: how thehouse always wins. Paper 379-2011. SAS Global Forum 2011.