Where’s the Money? Part 18: Series Finale

Authors’ Note: We would like to thank our readers for their wonderful support over the last 18 months. It is your support that inspires us to write and to share more. This 18-article journey has shown how we evolved our thinking about player experience-based analytics and how this behavioral analytics model has, in our experience, driven significant revenue improvements. It took us through the great games of gaming and all the way to the growth markets in Asia. This finale will now attempt to summarize the highlights from this series, while encouraging you to go back and revisit some of the source material.

There have been three core themes throughout this “Where’s the Money?” series: horizontal innovation, the customer experience and analytics. As the industry continues to innovate in many and varied ways, from the systems that tie the games together to the actual gaming devices, this series has provided real-world examples of how analytics is at the core of driving the right value from the customer experience.

Horizontal Innovation
We are defining horizontal innovation as “innovative technologies on the gaming floor that apply broadly across multiple gaming devices.” These horizontal innovations are often applied to the whole gaming floor at one time. In other words, the end customer experience is, in many cases, a mixture of influences from many suppliers.

Gaming Standards
We define the gaming product as “the technology and environment that defines the player gaming experience.” Key to enabling innovation with this product is the Gaming Standards Association (GSA) and its goal of enabling operators to choose between different suppliers for each component of their gaming systems. This GSA framework enables operators to select from competing products within different horizontal segments of the gaming ecosystem.

The kind of future GSA promises is not conceptually new. In fact, interoperability has been common in the industry for years. For example, customer management systems from a variety of systems vendors all work seamlessly across gaming machines from an even larger variety of manufacturers.

The key is that standards create the platform for horizontal innovation. This style of innovation can manifest itself in unusual ways. For example, the gaming industry has clearly been driven by architectural innovation—just look at the impact that themed properties have had on Las Vegas, where, according to Barry Thalden, “people still flock to the volcano at The Mirage, The Venetian’s canals, the Bellagio’s fountains and gardens, the New York New York’s skyline, and the miniature Eiffel Tower at Paris Las Vegas. And they still come because gambling is fun.”1

Gaming Product
Consider the example of Penny Alley, where Silverton Casino used a combination of floor reorganization and horizontal secondary device features to drive significant incremental revenue. This example establishes that customer behavior can be changed by the gaming offering. Contrast the effect of Penny Alley to some new gaming products that Silverton trialed in recent months. These trial games were among the highest-performing games on the floor; however, the calculations of the efficiency (or cannibalization) showed that these games did not add incremental revenue to the property. Of course, these games were returned.

Data and Databases
Data is the core of analytics. The key development in data in recent years is the buildup of third-party databases of information that is, or is likely to be, critical to the operator. For example, Facebook, with its billion users, is accumulating massive amounts of behavioral interaction data on customers, and this information is outside the domain of traditional transaction database information.

Horizontal innovation applies to databases as well as gaming products. For years, slot floor operators have relied on slot performance data to determine what products to place on their floors. Now imagine the power of combining these two data sets, which many casinos have already done or are in the process of doing. From the marketing side, knowing exactly what games are being played by each customer allows for improved segmentation. Coupled with the knowledge of new or changed slot product, marketers can reach out to their customers in far more relevant ways. The same can be said from the slot performance side. Knowing who is playing a particular game can guide product decisions. Have a handful of customers who love Game A? You may decide to keep it, even if it has below average win. Discovered a previously undetected association between Game B and Game C? Make sure to place them near each other!

The Customer Experience

A big part of our “Where’s the Money?” series focused on the customer. As operators, it is imperative that the customer experience is our highest priority.

Player-Centric Optimization
This is a central theme to our analytics story today. Quite simply, optimization should be done on metrics that the customer experiences, and should be measured by the outcome the business needs. This is defined as player-centric optimization. Table 12 lays the foundations for the different kinds of player optimization.

Player-centric optimization is defined as improving the player experience in ways that drive the most incremental player net revenue. Expansion of this definition of player-centric optimization results in two kinds of metrics: optimization metrics and outcome metrics.

Optimization metrics measure effects the players can observe. Furthermore, it is generally desirable to optimize these metrics to drive incremental revenue. In short, optimization metrics are metrics that the player notices. Utilization is one metric that players notice—you might hear statements from patrons like “I found my favorite” or “The best games are always occupied when I want them.”

Outcome metrics measure effects the players do not observe. For example, the theoretical win per unit per day on the gaming machine. The player experience differs dramatically from the expected or theoretical outcome, and furthermore, the theoretical win per day is an average from a number of different players. Quite simply, players do not experience the spending of other players. Another outcome metric is the slot floor hold percentage, or what is often incorrectly termed the “price” of our games. In our April 2011 article, “Gaming Floors of the Future, Part X: Hold Another Sacred Cow,” we debunked the myth that one can increase gaming revenue by simply tinkering with the overall floor hold percentage. Rather, the hold percentage of the whole gaming floor is an outcome metric that is more reflective of the mix of gaming products than it is of the player experience.3

Case Study: Jackpot Wharf
The Jackpot Wharf mini casino strategy4 showed how focused, analytical-driven insight and operational excellence can result in significant value. The case study followed a number of keys to executing a successful mini casino strategy, including naming the area, spacial management of physical spaces, locational intelligence and spatial data query, and seamless integration into relational data using implicit data relationships,5 all of which were explored in depth earlier in this “Where’s the Money?” series.

Great Games of Gaming

From IGT’s Wheel of Fortune® to WMS’ CLUE™ to Aristocrat’s Buffalo, we have studied the games that our customers love. Each of these games provides a different lesson and a different insight into our customers’ behavior.

Digging into Wheel of Fortune took us to a study of decay curves and patron growth. One of the key differentiators between decay curve categories (see Table 2) is the ability of a gaming device to attract new players and expand its popularity. Studying the success of Wheel of Fortune led us to a seeming paradox: according to decay curves of most games, we should constantly refresh our gaming floor, but an illustrative example indicated we should keep older games even if they are underperforming. Which approach is correct? In fact, both recommendations are sound. The contradiction exists because we have so many types of patrons, each with their own gaming experience preferences. In the end, the answer lies with the operator’s dual mandate: grow revenues and protect revenues.

With CLUE, we saw the merging of online and offline gaming. What is exciting about CLUE is that the player can leave the casino, drive home and then play online at www.playerslife.com to accumulate features that will change the in-casino gaming experience. While we cannot predict if this model of game play will be a long-term winner, it definitely creates a new kind of analytics challenge and a new kind of data. CLUE has real potential to help operators and manufacturers bridge the gap between online and real life, and to help operators understand what their patrons do online before and after their gaming experience. It will require special kinds of cooperation between the two, but the results could be remarkable.

Looking at the great game Buffalo led us to a study of outliers. Earlier in this “Where’s the Money?” series, we described these outliers and some of the mathematical challenges in analyzing them. Our review showed how some games, such as Buffalo, are so extreme in their performance, they are black swans (unpredictable events that bring a major impact). From the perspective of predictive models, it is extremely difficult to model what makes these games successful. Why doesn’t predictive modeling work perfectly? It’s partly because there is a common misunderstanding that predictive models see into the future. In fact, predictive models take historical trends and tell us what the future will be if these historical trends continue—and they very well might not. (See our article on Big Data in the March 2012 issue of CEM for an introduction to predictive models.)

Customer Value and Macau in 2032
It sure is an exciting time in the Macau market, with what seems like unlimited growth opportunities and seemingly endless opportunities to deploy capital. As this marketplace grows, we have illustrated how there are three strong follow-on effects to watch for. First, watch for management teams in this market to become the leaders in companies expanding across Asia. Second, watch for vendors establishing strong leadership positions locally to support this center of management. And, finally, watch the numbers, as a high volume of customer growth does not always mean growth in revenue.

We investigated this final effect in “Where’s the Money? Part 17,” specifically looking at the impact of whales on Las Vegas revenues. In addition to providing insight into the concept of a bimodal distribution, the study showed how a city can grow in volume of customers but that volume may not lead to growth in revenues. Las Vegas Convention and Visitors Authority6 data clearly showed that at the time of the article’s publication, although the gaming market in Las Vegas was at one of the highest points in number of visitors per month in 2012, gaming revenue was down significantly. In fact, this critical metric peaked in 2007.

Big Data and Locational Intelligence

Locational data was once strictly the domain of land surveyors7 and geographers. Today, it is generated by nearly every application on any mobile device. Consider this example: A customer named Andrew is sitting at a slot machine in his favorite casino, Casino Y. He stops playing for a moment and uses his smartphone to search online for a place to dine. Andrew is creating GPS data that records his current location and the fact that he is looking for a restaurant. The app provider, to generate advertising revenue, makes this data available to third parties, who in turn use it to place their marketing offers and events. So, Casino Z could be watching for these searches and sending locationally targeted marketing events to Andrew. Figure 1 (from the April 2012 issue of CEM) shows a screen shot of the Foursquare application Andrew used for his search. As you can see, he is shown several location-based offers in his vicinity—most of which are not at Casino Y.

War Room Analytics
Figure 1: Location-Based Offers on Foursquare
Figure 1: Location-Based Offers on Foursquare
Analytics and reporting are completely different things, although on the surface they share many attributes. Table 3 (from the May 2012 issue of CEM) illustrates the difference between analytics and reporting. One of the core ideas of analytics is that it often involves team work with operational components of the business. In the April 2012 issue of CEM, we described the management practice of war room analytics and how it fosters this change.

Big, Complicated, Art
When looking at analytics, we described how analytics is big, complicated and is an art, not a science. In the example in Step 1 (see Table 3), one is pouring over thousands of data elements, trying to find areas of opportunity to improve the customer experience. In Step 3, one is measuring the impact of perhaps hundreds of games and trying to remove the biases created by seasonality, changes in customer preferences over time, and cannibalization. Reporting, on the other hand, is much simpler and smaller, and thus lends itself well to miniaturization to a smartphone or tablet. However, analytics should not be miniaturized.

You heard it here first! Analytics is an art, not a science. Reporting is a science. If you want to know how many widgets you sold yesterday, you take your source system tracking data, ETL it into a data warehouse, push the data into a front-end business intelligence system, then access that system via a computer, tablet or smartphone, and—voila!—you know how many widgets you sold yesterday. However, if you want to know how you can drive incremental widget sales, the task becomes much more difficult.8

Figure 2: Location of Wheel of Fortune on the Casino Floor
Figure 2: Location of Wheel of Fortune on the Casino Floor
Case Study: Participation Games Optimization
One of the biggest questions on any slot floor is how to decide on the right level of participation games. This question of participation is a matter of huge debate and underpins an enormous rift between manufacturers and operators. One party might say that these games bring incremental revenue, while the other is questioning if participation games are just reallocating revenue that the casino would have collected. We dug into this question and outlined some real ways that, through customer preference and experimentation, we can discover the true value of a participation game.

The “2 percenter” operator believes that participation games should be less than 2 percent of the total gaming floor, while the “10 percenter” operator has huge numbers of participation games that dominate the gaming floor and are a central part of the overall gaming strategy. Figure 2 (from Where’s the Money Part 139) shows a case study of how participation games can be moved to a high-volume area and that this high-volume area drives incremental revenue. This does not resolve the difference between the 2-percenters and the 10-percenters, but it does show that careful placement of participation games is essential to getting value from these games.

1 “Of Truths and Consequences: How Las Vegas Forgot How to Make Money,” Casino Enterprise Management, January 2011, and “Where’s the Money? Part 3” Casino Enterprise Management, September 2011.
2 “Where’s the Money? Part 4: Gaming Density and Yielding the Floor,” Casino Enterprise Management, October 2011, Cardno, Thomas, Gordhan.
3 “Where’s the Money? Part 6: Player Experience and Slot Optimization,” Casino Enterprise Management.
4 “Where’s the Money? Part 8: Player Preferences Learned From Jackpot Wharf, Part 2,” Casino Enterprise Management, February 2012, Cardno, Thomas, Evans.
5 “Where’s the Money? Part 7: Finding the Money in Jackpot Wharf, Part 1,” Casino Enterprise Management, January 2012.
6 www.lvcva.com/stats-and-facts/, extracted October 2012.
7 At least one of the Authors was a land surveyor.
8 “Where’s the Money Part 11: War Room Analytics,” Casino Enterprise Management, May 2012.
9 “Where’s the Money, Part 13: Great Games in Gaming—Wheel of Fortune,” Casino Enterprise Management, July 2012, Cardno, Thomas.

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