PREDICTIVE ANALYTICSFOR THE CASINO AND

GAMING INDUSTRY

Predictive Analytics in the Casino and Gaming Industry

Predictive Analytics for the Gaming Industry

“In business, as in baseball, the question isn't whether or not you'll jump into analytics. The question is when. Do you want to ride the analytics horse to profitability...or follow it with a shovel?”

-- Rob Neyer, ESPN

Predictive Analytics for the Gaming Industry

Predictive analytics refers to a variety of statistical techniques that analyze current and historical facts to make predictions about future events. Using such techniques as predictive modeling, machine learning, data mining and game theory, predictive analytics can build models that exploit patterns found in historical and transactional data to identify business risks and, just as importantly, opportunities.

Predictive analytics can be broken down into three different types of models:

Predictive: these analyze past performance to predict the likelihood that an individual customer will exhibit a specific behavior in the future.

Descriptive: these identify different relationships between customers to group or segment them for marketing or other purposes.

Decision: these predict outcomes of complex decisions, relationships, products and/or processes.

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.

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.

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.

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

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.

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.

Applications for Predictive Analytics

Cross-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.

Predictive Analytics can help to:

– Identify – the casino's most valuable patrons.– Predict a patron's future worth and/or his or her future behavior. – 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 and then create marketing campaigns to exploit those behaviors.

Predictive Analytics can help to:

– Determine a patron's 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 to respond to.– Predict when a patron is likely to return.

Predictive Analytics

In their article “Knowing What to Sell, When, and to Whom,” authors V. Kumar, R. Venkatesan, and W. Reinartz (2006) showed how, by simply understanding and tweaking behavioral patterns, they could increase the hit rate for offers and promotions to consumers, which then had an immediate – and substantial – impact on revenues. By applying statistical models based on the work of Nobel prize-winning economist Daniel McFadden, researchers accurately predicted not only a specific person’s purchasing habits, but also the specific time of the purchase to an accuracy of 80% (Venkatesan and Reinartz, 2006).

Predictive Analytics

The potential to market to an individual when he or she is primed to accept the advertising is advantageous for both parties involved; marketers don’t waste time advertising to consumers when they aren’t primed to accept the advertisements, but do market to consumers when and where they might want to use the advertisements.

Predictive Analytics can help to:

With predictive analytics, gaming organizations can easily segment their customers and coordinate marketing campaigns to effectively target each segment across each outbound channel. A profit curve (shown in the following diagram) estimates the profit a casino operator will receive from a campaign guided by predictive analytics, depending on how many prospects are contacted. The profit this curve predicts depends on the ranking of the casino's customers given by a predictive model, the cost per contact (e.g., printing and mailing costs) and the average profit per respondent.

Manipulating Customer Behavior

Successful marketing is about reaching a consumer with an interesting offer when he or she is primed to accept it. Knowing what might interest a patron is half the battle to making a sale and this is where customer intelligence and predictive analytics comes in. Customer analytics has evolved from simply reporting customer behavior to segmenting customers based on their profitability to predicting that profitability, to improving those predictions, to actually manipulating customer behavior with target-specific promotional offers and marketing campaigns.

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.

Predictive Analytics – Regression Analysis:

Regression analysis is the process of predicting the continuous dependent variable from a number of independent variables. It attempts to find a function which models the data with the least error. Regression analysis can be used on data which is either continuous or dichotomous, but cannot be used to determine a causal relationship. Regression analysis focuses on establishing a mathematical equation as a model to represent the interactions between the different variables under consideration. Regression models are particularly effective to find patron worth because the model can be used to score historical data to predict an unknown outcome.

Predictive Analytics – Linear Regression:

These analyze the relationship between the response or dependent variable and a set of independent or predictor variables. This relationship is expressed as an equation that predicts the response variable as a linear function of the parameters. These parameters are adjusted so that a measure of fit is optimized. Much of the effort in model fitting is focused on minimizing the size of the residual, as well as ensuring that it is randomly distributed with respect to the model predictions. An important assumption of regression analysis is linearity, which defines a straight line relationship between Independent variables and dependent variables.

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.

Predictive Analytics – Linear Regression

For the casino and hospitality industry, regression models can be used to predict a patron's future worth (Sutton, 2011). Multiple regression models “utilize a variety of predictors and the relationships between those predictors to predict future worth” states Sutton (2011). As an example, Sutton (2011) explains that “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.”

Predictive Analytics – Neural Networks

Artificial Neural Networks (ANN) or just “Neural Networks” are non-linear statistical data modeling tools that are used when the exact nature of a relationship between input and output is unknown. Neural networks can be used to find patterns in data. A key feature of neural networks is that they learn the relationship between inputs and output through training.

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).

Predictive Analytics - A/B Testing

Also known as split testing or bucket testing, A/B testing is a method of marketing testing by which a baseline control sample is compared to a variety of single-variable test samples in order to improve response rates. A classic direct mail tactic, this method has been recently adopted within the interactive space to test tactics such as banner ads, emails and landing pages. For casino marketers, A/B testing is the most effective way to identify the best available marketing offer. A/B testing involves testing two different offers against one another in order to identify the offer that drives the highest response and the most revenue/profit.

Predictive Analytics - A/B Testing

Predictive Analytics – Decision Trees

Used to identify the strategy that is most likely to reach a goal. Decision trees are a decision support tool that use graphs or models of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. Decision trees are sequential partitions of a set of data that maximize the differences of a dependent variable (response or output variable). They offer a concise way of defining groups that are consistent in their attributes, but which vary in terms of the dependent variable.

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.

Predictive Analytics – Time Series Model

A time series is an ordered sequence of values of a variable at uniformly spaced time intervals. According to the Engineering Statistics Handbook, time series models can be used to: -- Obtain an understanding of the underlying forces and structure that produces an observed data;-- Fit a model and proceed to forecasting, monitoring or even feedback and feedforward control.

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.

Predictive Analytics – Actionable Intelligence

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 manipulating 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 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.

Predictive Analytics – Actionable Intelligence

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 a letter or phone call 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.

Predictive Analytics – Actionable Intelligence

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. Predictive analytics can glean data from a variety of disparate sources, including:

-- Data integrated throughout the casino's gaming systems.-- Feedback information derived from post-visit surveys.-- Web data mining from customer’s individual online behavior.Social media websites.

Predictive Analytics – Marketing Campaigns

With predictive analytics, gaming organizations can easily segment their customers and coordinate marketing campaigns to effectively target each segment across each outbound channel. For example, if a casino customer is scheduled to receive all of his or her event promotions via e-mail, the predictive analytics solution will automatically remove him from concurrent campaigns being run through other channels. This ensures consistency and also improves customer satisfaction, since the organization respects the customer’s contact preference and doesn’t inundate him or her with multiple offers. Moreover, a predictive analytics solution monitors channel capacity and usage to eliminate overload, while distributing campaigns equally across the various channels. If one channel is at risk of overload, the solution automatically shifts the remainder of a campaign to a different channel to ensure completion. This enables organizations to maximize the capacity and value of each channel without resorting to time-consuming manual monitoring.

Predictive Analytics – Marketing Campaigns

By utilizing data from past campaigns and measures generated by the predictive modeling process, casino operators can track actual campaign responses versus expected campaign responses, which can often prove wildly divergent.

Additionally, casino operators can generate upper and lower 'control' limits that can be used to automatically alert campaign managers when a campaign is over or underperforming, letting them focus on campaigns that specifically require attention.

Predictive Analytics – Marketing Campaigns

Sutton (2011) claims that, 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 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 an offer?-- Which offers perform the best?

Predictive Analytics – Patron Worth

Once patron worth has been defined, data mining and modeling techniques can be used to estimate predicted worth in the future (Sutton, 2011). “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,” Sutton (2011) notes. “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,” Sutton (2011) argues

Predictive Analytics for the Gaming Industry

Casino operators should keep in mind that data mining will only be successful if their casino patrons are willing to provide information on themselves. Privacy is a big issue and will always remain so in the mobile age. Casino properties that can honor a patron's privacy demands will find patron loyalty comes with voluminous amounts of priceless patron data. This is data that can be used to create marketing campaigns that should prove highly effective. By understanding what type of patron is on its property, why they are there, and what they like to do while they are there, casino operators can individualize their marketing campaigns so that these campaigns are more effective than normal campaigns, thereby increasing the casino property's ROI.

Predictive Analytics in the Casino and Gaming Industry

Book is available at Amazon.com

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

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.