February 2007 Indian Gaming 46

For casino owners and managers, finding ways to boost revenues while trying to minimize financial risks to the casinocan be a day-to-day balancing act. Managers look for innova-tive and lucrative ways to feed customer demand for risk-tak-ing, such as introducing new games, strategic placement ofgames on the casino floor, targeted marketing/promotional campaigns, and offering customers goods and services to com-pliment their gaming appetites. Yet, managers simultaneouslytry to minimize the magnitude of swings in their revenuestreams that come from the same set of offerings.

Ideally, casino managers would like to know that a new strategy to attract customers or enhance revenues is a “safe bet”before they take it on. Predictive modeling can be a smart wagerfor identifying opportunities to increase revenues while low-ering revenue stream variability.

Increasingly retailers, wholesalers, finan-cial services institutions, and other customer-centered companies are usingpredictive modeling to unearth ways toprovide the goods and services that customers want, when they want them.Customer-driven strategies (such as cus-tomer segmentation or targeting) are notnew. Many casino owners and managerscurrently use strategies that target customers for advertising and promotionalcampaigns. However advanced analyticsuse the seller's own data to help measure how consumers may respond toprice changes, customers' potentialresponse to promotions and offers, theeffectiveness of advertising and marketing, and consumers' interest in complementary goods.

These advanced analytics may help casino managers createadvantages in the marketplace. In addition, such analytics support a casino's ability to analyze insights and “hunches” withempirical evidence. With the right data available, casino owners can also utilize modeling to help estimate optimalprice levels, implement strategic price discounting, or stream-line expenditures on marketing or advertising, all subject to own-ers' preferences or market constraints. These are the same thingsthat consumer products companies have been doing for years.

Let Your Data Lead the WayFrom a data perspective, the gaming industry shares much

in common with other industries, such as the retail industry,which relies heavily on predictive modeling. Both casinos andretailers capture detailed data on individual customers'

purchases, many of whom are loyal, repeat buyers who allo-cate their expenditures over discrete, identifiable quantities ofgoods (such as amount spent across distinct types of games).

Like their retail counterparts, casino operators and managersare potentially flush with data about their customers' buyingbehavior. Substantial information can pour in from records ofcustomers' winnings, floor managers' observations, usagereports from electronic gambling machines, customers' purchases at on-site retail and specialty stores, and customers'redemption of promotion and marketing offers. These alreadyrich data sources can be augmented by measuring directlyhow individuals respond to these factors over time, an experi-ence which retailers have found to be very valuable.

Predictive modeling can help casino managers sift throughmountains of data to analyze how customers'spending may change as a result of changesin the price of casino games or retail prod-ucts, changes in promotional campaigns,changes in advertising spend within andacross channels, changes to gaming floorconfigurations, and many other quantifiablefactors that may affect patrons' gamingbehavior.

Data-driven insights, combined with well-developed analytical models, can help casinomanagers in attracting and retaining customers. However, predictive modelingtechniques work better when casino ownersand managers understand how models willbe used. Both managers and model develop-ers should have a solid understanding of the

underlying data entering the models and the data's purpose.Several strategies for using predictive modeling have emergedfrom the retail and consumer goods sectors that can serve asbasic roadmaps for casino operators to help interpret andapply the data they collect.

Line Up Data Collection With Key Factors That Drive Revenues

Given their abundant customer data and wide range ofproducts, retailers and other customer-centered firms often usetargeted approaches to help identify factors that could enhancerevenues. They estimate price elasticities and cross-price elasticities for individual products and product groups, adver-tising and marketing elasticities, “mark-down” opportunities,and many other influencers. They use the information to helpestimate optimal price and expenditure levels, subject to specific constraints, to provide the set of goods and services that

Expanding Analytical Strategies in the GamingIndustry

BUSINESS ANALYSIS

by Kraig Singleton

48 Indian Gaming February 2007

their customers want at the right time and price. In our workfor a major international beverage provider, analytical modelsrevealed that the company was potentially charging too higha price, on average, for its premium product. Furthermore, thecompany's overall revenues could havepotentially increased significantly by reduc-ing the price on that product.

Understandably, casino owners and man-agers are less likely to change prices oncasino games as frequently as retailerschange prices on products, and they gener-ally are not marking down prices to clearinventories. However, they stand to bene-fit just as much from using their customerdata to help drive revenue gains for individ-ual gaming product lines or from consumers' cross shopping. For example,consider advertising and marketing elasticities and related activities. Data mayhelp estimate the optimal expenditure lev-els and types of activities that are revenueenhancing, and separate them statisticallyfrom those that are revenue neutral. Cross-purchase elasticities can reveal other variances. They may uncover whether sig-nificant revenue is derived from gamingpatrons' purchase of additional goods and services, and towhat extent price changes of those related items affect gam-ing revenues. Detailed analysis of individual patrons' gamingbehavior can help to identify additional games that may appealto existing clients.

These and other data-driven models rely on the collectionof a sufficiently large cross section and/or historical set of customer data. Advertising and marketing elasticities, forexample, require detailed information on each type of thecasino's activities associated with these areas as well as detailedcustomer revenue data. Cross-purchase and gaming behavioranalyses require data series where individual patrons' purchases are observable over time.

Larger, consistently collected data series often supportgreater reliability. They can help to analyze whether there are sufficient data to build and test models, as well as data that canbe held back from modeling efforts to compare forecasts orbackcasts to actual results. By aligning their analytical goals withtheir data capacities first, casino managers can begin to extractsome of the insights retailers have culled from their customerdata.

Keep it Simple: Avoid Brain FreezeRetailers have discovered that one of the keys to using

analytical models is to develop and estimate models at a levelwhere results are easy to use and are actionable. They havefound that finding the right level to perform analytics and reportresults is a critical challenge. Many retailers, for example,

have implemented predictive modeling disaggregated byindividual SKU (stock keeping unit) and/or store. This can helpreveal insights about product lines that may have been maskedby data aggregation. Estimating models for large numbers of

casino games, their individual locations in thecasino, or the sensitivity of individual repeatcustomers to changes in prices or winningpercentages can require the developmentand interpretation of many analytical mod-els. The level of detail from a “fully saturatedmodel” can be overwhelming for reporting,monitoring and decision making purposes.

Casino managers should generally begintheir efforts with a simple mode, but not sosimple that it misses key factors driving con-sumer behavior. Casino managers who areforecasting gaming demand by time periodand type of game may obtain more robustresults using aggregated forecasts, comparedwith estimates for individual games. Alter-natively, instead of developing demand fore-casts specific to individual product types,managers can build statistically-based systemsof analyses that identify key elements acrossproducts, channels and types of consumers,and that incorporate important product

interactions and changes over time.

Adam Smith, the Father of Economics, RecommendsMaximizing Profits, But Some Managers Try to MaximizeRevenues

Typically, the goal in developing predictive models ofdemand is to identify the key factors that drive demand and toquantify the market response to these factors. Using this infor-mation, managers try to maximize profits by spending resourcesup to the point where the marginal revenue from expenditurein each channel equals its marginal cost. As a variation, some ofour clients have chosen to try to maximize revenues rather thanprofits, due either to internal incentive structures or to limita-tions in internal costs and profit margin data.

Analysis of potential revenue maximization, in some cases,may be an appropriate shorthand measure for the potential ofa new game or other offering, segments targeted as growthopportunities, or to expand add-on services for current patrons.Profit maximization may be a more appropriate goal for wellestablished products or for analyzing service offerings in aggre-gate. With the appropriate analytical framework, casino managers can help differentiate between these strategies andtheir potential trade-offs.

Based on the Findings, Keep the “Winners” and De-Prioritize the “Losers”

Robust predictive modeling can help casino managers distinguish the underperformers from the unwavering products in the casino manager's portfolio of offerings.

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February 2007 Indian Gaming 49

Retailers may eliminate a product altogether from a locationor limit its availability to selected periods in the season.Similarly, casino managers can replace low-value offeringswith high performers (up to the point where they don'tover saturate demand), or eliminate them altogether andintroduce new services that may help to stimulate demandfor other products.

Putting It Into PracticePredictive modeling has become an effective means for

many retailers to use their customers' buying behavior to findstrategic ways to help build up the bottom line. Analyticalapproaches help provide the empirical backbone for managers making decisions on pricing, advertising and marketing expenditures, or the right mix of goods and services.In addition, they help align managers' actions that affect revenues with customer demand.

Casino managers who want to add robust predictive modeling to their toolkit should consider the following steps:

• Work with your data owners to clearly identify what the models are designed to estimate.

• Assess availability of the data you need. If there is no data, start collecting it systematically.

• Consider embracing a leaner analytic framework rather than oversaturated models. Start with a simple model and build from there.

• Assess your own profit motives and incentives. Depend-ing on the incentives in play, some managers may choose to attempt to maximize revenue instead of profit. Make efforts to align the models with those incentives.

• Use the models' findings to differentiate among service offerings. Solid analytical results help to make an easier case for determining which service offerings to keep and which need to be overhauled or abandoned.

Given their rich historical customer data, casino managersmay be able to make predictive modeling an odds-on favoritefor helping to generate stronger financial results. ¨

Kraig Singleton is a Senior Manager in the Economic and Statistical Consulting Group of Deloitte Financial Advisory Services, LLP. The views expressed in this article are those of theauthor, and do not necessarily reflect the views of Deloitte Financial Advisory Services, LLP. Kraig can be reached by calling (312) 486-1651 or email ksingleton@deloitte.com