Part 18 of 18: Looking Back, Moving Forward

Authors’ Note: This article concludes our 18-part “Where is the Money Now?” series. This final article summarizes our journey, highlighting key points from the last 18 months and exploring the four major themes that we have explored: The Customer, Volatility, Big Data and Mobility.

The Customer
Our journey began with the entertainment customer and their macro trends. We described the potential for the industry to grow by expanding horizontal offerings and explored the potential risk and opportunities presented by online gaming.

The defining behavioral characteristic of an entertainment customer is that the entertainment customer spends both time and money. This distinction of spending both time and money completely differentiates the behavioral characteristics of an entertainment customer from, say, a retail customer.

This defining characteristic of entertainment customers creates a real set of analytical challenges. It is not enough to ask, “How much money is a customer spending?” We must also ask, “How is the customer spending their time and how can we interact with them while they are spending that time?” In fact, it is the spending of time that is core to the customer experience,1 and this becomes a critical factor in deciding how to drive optimization of the gaming experience. Real-time mobile-based interactions enable innovation, both on the gaming floor and in handling customer interactions.

Same Store Revenue Growth
When looking at gaming product innovation, one of the fundamental benefits is appealing to new and different customer groups. These customer groups extend our reach into other demographic groups; however, we must remember that these other groups have different expectations of customer service, including an expectation of mobility in the customer interaction.

This search for new customer groups is more important than ever in today’s world of seemingly endless growth in gaming operators. In fact, it seems like hardly a month goes by without a new casino being announced. In this new competitive environment, broadening your property’s appeal to different segments of the market is a powerful way to grow business without having to fight for the competitive patron dollar.

The retail industry is measured by its ability to drive same store revenue growth, and this growth is sometimes achieved by adding completely new product types. For example, Walmart’s addition of a tire center brought new same store revenue to the business that was purely incremental. In fact, tire customers may buy other products while they are in the store, creating cross-sell opportunities. Same store opportunities abound in the gaming and hospitality worlds as well, ranging from the addition of hotels to new gaming products. Within retail, same store opportunities can be analyzed purely by using the market basket.2 In the world of gaming, however, we need to understand the customer and their preferences before we can understand the market basket.

Who Are Casino Players?
While casino customers as a group are a similar age and gender mix to the overall United States, gambling is most popular in the 51- to 60-year-old age group. Furthermore, in our experience, this older group of players heavily outspends younger players and has very different game preferences. Following are three critical factors to consider when attempting to broaden the customer base:

1.    If we are to diversify the casino customer base, then a focus on different groups of players is required. In fact, the traditional analysis of spend per trip or spend per machine per day is dangerous, as we are averaging quite different age profiles and the numbers will be heavily skewed to the demographic profile that spends the mostly heavily.
2.    A wide range of customer age and gender mixes already try the casino experience. We speculate that this wider group presents an opportunity to diversify the customer base, but we will need careful analytics to isolate their desired player experiences.
3.    A powerful way of tackling a product innovation-based approach to diversification is to find products for younger players. When taking this approach of looking for new products for younger players, the first place to look is at existing products on the gaming floor and to see if any are already attracting younger players.

The second theme we explored was volatility. We described the incredibly volatile gaming customer, why these customers appear to be completely unlike customers in other industries and how we can effectively market to these customers.

As we consider the goals of diversification of the customer base, understanding the gaming experience is critical. One great way to understand the gambling experience is to liken gaming to fishing. Our Fisherman’s Analogy may help to explain some of the behavioral aspects of the gambling experience. In recreational fishing, the fisherman invests time (and money) in the hope of catching a fish. In fact, the time and money invested in fishing is, at least in our experience, nothing like the return received. However, the moment of catching a fish makes it all worthwhile. In fact, this moment is the reason that many fisherman continue to fish.

Fishermen remember, talk about and revel in those few exciting moments when they actually caught a fish, relegating the hours spent waiting for a bite or the trips where they caught nothing to a back corner of their consciousness.

The Fishing Analogy applies to the gaming experience, as gaming patrons spend considerable time (and money) at the casino, and this experience, for the most part, does not result in a winning experience. However, every so often, depending on the mathematical model of the game, the player does win, making it all worth it. This winning experience is like catching a fish; the player is excited, and it is the memory of this “big catch” or maybe even the so-close tease of “the one that got away” that brings the gamer to try again.

Expected Catch Size
Theoretical win can also be illustrated using the Fisherman’s Analogy. Consider this example: If a fisherman catches one fish, on average, for every six hours spent fishing, then the expected value of a catch is one-sixth of a fish per hour. However, when we look at the fishing experience, it is dramatically different. The fisherman either catches a fish or does not; there is no one-sixth fish. The fishing experience is defined by the fisherman’s experience of catching or not catching a fish, just as the gaming experience is defined by winning or not. Furthermore, when our canonical fisherman catches a fish, he will think he is on a run and fish longer. In other words, catching a fish affects the amount of hours spent fishing, which in turn affects the expected catch size—just like a win at the casino.

Primer on Corrected Theoretical Win 
The Fisherman’s Analogy is an illustrator of how the calculation of theoretical win is a misrepresentation of a gaming experience—again, it is impossible to catch one-sixth of a fish, although mathematically, it is expected (see Part 2 of this series in the February 2013 issue of CEM for more details).3

Theoretical win is the expected value (EV) of the result of a gaming experience. This calculation is based on the probability of a win times the value of the win. The challenge is that most slot systems do not track the game performance, they track the asset performance. Specifically, if a customer plays a game on a slot machine that has multiple games, the system does not know which game the customer has selected. It only knows which machine, and it applies an average probability of win based on all the games on the machine, not the specific game that the customer has selected. A solution to this problem involves the application of computerized problem solving to provide the corrected theoretical win per game and thus by customer.

Demographics of Fisherman
Looking into demographic versus behavioral (DvB)4 data, we can further extend our Fisherman’s Analogy to ask some very basic questions, like does it make any difference how old the fisherman is?, or does it make any difference what gender the fisherman is? The answers are, quite simply, that once somebody has chosen to go fishing, what matters is: do they catch fish or do they come up empty? This leads to fundamental behavioral questions like does the person like to fish?, what kind of fishing to they like?, and where do they go fishing? Even questions such as how often do they fish? and how long do they spend fishing? are absolutely core to the behavior of the fisherman.

Player Experience: What is Volatility?
Stepping past the Fisherman’s Analogy, we can further dig into the gaming experience by looking at volatility. Our definition of volatility is “the spread of distribution of game play.” As such, it is not directly related to the statistical win of the game. To illustrate volatility, let’s look at two simple games: a low volatility game and a high volatility game. To do this, we will look at the roulette table.

Low Volatility: If a player bets black or red only and does not change their bet, they are playing a very low volatility game.

High Volatility: If the player bets on the number four only, they are playing a very high volatility game.

However, complexity arises because players can change their betting behavior; consider the low volatility player who is betting only on black but who doubles down on each losing bet.

This player has essentially taken a low volatility game and turned it into a high volatility game. This fundamental control over the volatility of the game makes pay table-based analysis of volatility mathematically suspect. Quite simply, to understand the game, we are mathematically required to look at the behavior of players and their different choices, as well as their winning or losing streaks.

Player Experience: Luck
If a player considers himself to be very lucky if he wins a sequence of games in a row, then the chance of being very lucky is directly related to the time that the patron spends gaming. What is remarkable is that seemingly impossible sequences of winning streaks, such as winning nine games in a row, are in fact not that uncommon and are going on continuously on our gaming floors. These seemingly impossible odds take what we consider to be very non-volatile games (such as red/black roulette or blackjack) and make these games very exciting. It is the analysis of the “streakiness” of sequences of play that allows us to see into this exciting dynamic.

Figure1: Chance of sequence occurring in 20 games with variable hit probability

A Sea of Randomness

Taking up the Fisherman’s Analogy again, players are like fishermen on a sea of randomness, and much like fishing, they often experience remarkable days. On these days, they might catch a whole series of huge fish or an even longer series of average-sized fish. As our players are subjected to this seemingly5 extreme probability, and as individual gaming data is starting to flow, we have the opportunity to increase our understanding of how players respond to their lucky streaks. This information can be used to create better jackpots and even targeted marketing activities that are able to move beyond simple averages and theoretical win-based numbers—numbers that are in fact normally far removed from the actual gaming experience. Figure 1 shows how the hit probably affects the occurrence of a streak of winning events.

The Human Side of Data
The human side of data, its dimensions and its structure are central to why we make money in a business. Consider the example of address: Approximately 14 percent6 of our customers move each year. Therefore, we have a huge ongoing flow in our customer databases. This constant churn in the customer base is simply due to physical movement in our customers. The inflow and outflow of people in our area is one of the primary reasons why we gain and lose customers. The challenge is that without seeing the human side, our analytics will always be driven by a factor that is not visible in our data. Under these circumstances, at best our results will be inaccurate, and at worst, misleading.

Primer on Churn
Churn, also known as customer attrition, is the simple loss of a customer. It can also be called turnover or defection. Churn is a critical measure in many businesses and is the subject of many marketing studies, as it is six to seven times more difficult7 to acquire new customers than it is to save customers from defection. Measures to protect customers range from improving customer service to improving marketing activities. There are two quite different kinds of churn: voluntary and involuntary. Voluntary churn occurs when the customer chooses to defect, whereas involuntary churn occurs when events such as change of address result in the customer leaving.

To add a twist to this, we can also classify a dramatic change in behavior, such as changing from visiting once a week to once a year, as a churn event. Involuntary churn can also be twisted—for a property that has both locals and destination customers, involuntary churn becomes the opportunity for the same player to change from a local customer to a destination customer. As described in Part 6 of this series (see the June 2013 issue of CEM),8this type of churn necessitates accurate monitoring of customer address attributes.

One-to-One Marketing
One-to-one marketing is a goal for each individual customer to receive a specialized marketing offer. This means that their specific needs are addressed. The main focus of one-to-one marketing initiatives is not products, but individualized messaging. In exploring the value of one-to-one marketing, the assumption is that each person should have a different offer, different creative images and different copy, as each consumer is different from the next. The 1996 book One to One Future9 describes a vision of selling to each customer over their lifetime rather than selling products to masses of customers today. The dream of one-to-one marketing has been around for at least two decades, though true one-to-one marketing might still be largely unrealistic.

Clustering is statistical method of creating groups of statistically similar things. For example, if a customer has 40 attributes (age, gender, recency, frequency, etc.), then is it likely that many customers share the same combination of attributes—and, in some cases, the attributes that differentiate between customer groups are counterintuitive.

Clustering is a statistical modeling technique that says, Let’s force the data into buckets. If we have, say, 20 buckets, we can divide the customers into groups, placing customers who are as different as possible within each bucket. This means that we have created buckets of customers that are both meaningful and distinctive. Marketing in clusters instead of a fixed segmentation strategy allows us to know that we have created segments wherein the customers are similar in meaningful ways—and the customers in different segments are different, again, in meaningful ways.

With Segments Comes Control
Now that we have shown that we can, in fact, group customers into similar segments, the beautiful thing is that these segments contain customers that we have mathematically determined to be similar. This similarity allows us to run experiments10 on different sub-segments of the customers and compare our results to a strong control.

As we described in Part 13 of this series, control is essential to the experimentation process. We won’t repeat the argument of the importance of test and control here, but we will re-state that the single most important thing a casino marketer can do is experiment on their customer base via this process, fine-tuning offers to maximize profit as often as possible.

The Marketing Dream
With our discussion of clustering, we see the importance of creating meaningful segments, where each segment contains groups of customers that are similar to each other and different from the customers in other segments. Furthermore, these segments can be the basis of different communication methods, including the very important customer dialog. In practice, one can apply the statistical method of clustering, but if that is not available, a segmentation strategy that sets out to create segments that are meaningful and distinct in this manner can suffice. However, as we move from clustered (or otherwise created) meaningful segmentation to one-to-one marketing, we quickly lose all that we desire in a segmentation strategy. Every time we add another dimension in order to get closer to one-to-one, we exponentially grow our segments and confound them with dimensions that are typically correlated (at least on some subset of the customer population).

Worse, the number of customers in a typical segment declines. By design, one-to-one marketing creates small segments of customers—ideally with only one customer per segment. But with small segmentation, we completely lose the ability to employ test and control strategies. As noted in the December 2013 and January 2014 articles of this series,11 in this area of discussion, gaming data is more volatile than in other industries, and thus it is vital to maintain a certain level of customers in any test and control segment.

Big Data 
In the third theme of this series, we looked at data, in particular big data, and what it means for the industry. Big data has become one of the world’s biggest industries. With companies such as Google and Yahoo!, entire businesses revolve around the use and exploitation of this data.

Customer Database Ownership
Before we dig into the definition of big data, it is important to understand that without linking it back to the customer and ownership of the customer relationship, we are fundamentally limiting our ability to apply these huge data streams.

There are many components to “ownership” of a customer. The first ownership right is that we own our customer database—we know the name, address, age and household members of our customers. As described in our June 2013 article in this series, we also track the slowly changing dimension (SCD)12 that describes change of address and other attributes over time. This right is fundamental and is normally heavily protected.

Consider the job of a new casino opening their property without a customer database. As it launches its rewards card, every day it would be signing up new people and adding to its new database of customers. It takes months, or even years, to build the database of customers. This knowledge of the customers is central to the development of customer loyalty.

Consumer Adoption Curves
Consumer adoption curves are often said to be dramatically more rapid in recent years. Our key observation is that despite the hype, consumer adoption curves actually seem to have remained unchanged for 60-plus years. This counterintuitive observation is exemplified by the adoption of the radio, which has a similar adoption curve to the computer. The big differences could be that there are many more rapid adoption curves today and that the technologies of today generate data at a staggering rate.

Adoption curves are very important in the gaming world right now as we try to anticipate how new technologies will change the industry, from online gaming and new gaming machines.

Online Gaming Adoption Curves
Consumer adoption curves show that rapid adoption of popular new technologies (such as the radio or the Internet) spans a period of approximately 10 years, though arguments can be made for faster or slower adoption curves.

Profile of Internet Gamblers: Betting on the Future13 clearly shows that likely Internet gamblers are a younger (median age in the 30s) and lower-income group. Given this very different market profile and often lower spending power of this group, we see the adoption of online gaming as following a blend of typical adoption curves.

Defining Big Data
During the last century, the huge data was financial in nature and the grain of the data was typically one record per item (or number of items) sold. For example, in a retail environment, one grain of data was a single product/price/number of items sold. This century, the data volumes are amplified to a level that makes last century’s data look tiny. This century each grain of transaction data is surrounded by thousands of interactions. These interactions exist in web logs, social media and, in fact, nearly everything that we do, extending out all the way to remote sensing from satellite imagery. In addition, the grains themselves are no longer necessarily single numbers representing some aspect of a transaction—often data comes to us in the form of unstructured data (imagine, for example, trying to store every single web page on the Internet every day), which has forced some data collectors to move away from relational databases and toward other forms of data storage, like Hadoop.

Figure 2: Transactions to Interactions14 15
Figure 2 shows the exponential growth in data volumes and how we are now in the age of interactions.
Big Data and Location
Proximity to competition, or location, is the primary driver of a casino business, and proximity data combined with census data provides accurate market share numbers. Census data enables the analysis of the number of potential gamers by census area across a market catchment. What is fascinating is that spatial analysis also gives visibility to the catchment of competition and top the effect of this competition on market share in terms of both visits and revenue. To manage this locational data and these spatial relationships requires locational data storage, locational query and locational analysis tools. These locational tools are a basic building block of the big data analytical tool set.

The Future of Gaming: Big Data
Big data includes data from sensors, from social media and from online gaming, and it is interactional in nature. The data from last century, in contrast, includes data from point of sale, from daily slot activity and from carded sessions. Rough back-of-the-envelope estimates show that big data is 1,000 times greater in size than transactional data; however, the real value comes from the connection of big data to transactional data.

Bringing It All Together
The gaming industry is a business of selling mathematical model-based experiences to customers. As we do this, sometimes there is some very hard work involved in preparing the data. Heuristic methods are a viable option for this, and they can solve even the most difficult problems, albeit approximately. After all, this hard computer science, the results are fun, drive profit and, at the end of the day, are irresistible. Furthermore, given that these methods are now available, it is our opinion that it is fiscally irresponsible to build marketing programs—with their huge reinvestment costs—based on fundamentally flawed mathematics.

The fourth and final theme we explored in this series was mobility. Mobility and real-time business go hand-in-hand, and this final theme covered how the mobile world is becoming an intrinsic part of the gaming experience. The question is, how can the industry embrace this massive sea change?

Clearly, as the mobile, real-time, big data age hits gaming, there are a going to be a lot of options available to jump on the bandwagon. Now, like all new technologies, the build-versus-buy argument arises. Do we build the mobile apps ourselves, or do we buy them from a vendor?

Software is a Marriage
Once software is written, it is only the beginning of the journey. In fact, the writing of the software can be compared to a marriage, and the initial period is a honeymoon. The software marriage is a long-term commitment that requires ongoing work, great communication and hard work to keep it relevant and up to date.

Creators vs. Maintainers
Consider the example of building a smart phone application. The software engineers who are successful in the initial build are highly creative and effective. These A-grade developers are going to be fighting off job offers and are likely to move on to new exciting projects, leaving the marriage in the hands of the B-team developers, where it is likely to languish. Now, roll forward two years, and the mobile app is functioning but the world has moved on. Mobile phones have evolved considerably, and the app is looking dated. Roll forward another few years, and the entire technology platform will have moved on, and a massive effort will be required to keep the app current or rebuild it completely.

In summary, the initial investment in software is just the start of the work, and software requires significant, ongoing drive, as well as creativity, leadership and vision.
Figure 3: Growth of 50- to 64-year-olds using smart phones
Application to Gaming
In the world of casino gaming, data is everywhere, and casino operators are faced with a decision on what to do with it. Should they build a BI/CRM solution themselves, or should they outsource it?

Both solutions are fraught with difficulty. Custom, internally built systems have the advantage of meeting the needs of the casino operators at the time they are built. However, these systems suffer from the following vulnerabilities:

1.    They are often built in tools like Access and Excel, meaning that there is very little programming done and a great deal of hands-on data manipulation is required by analysts. In this scenario, analysts spend a ton of time creating reports and little to no time analyzing what those reports mean to the business.
2.    More sophisticated builds are at the mercy of those who build them. These undocumented systems rely on an institutional knowledge that is often very thin—only a small handful of individuals truly understand how to make the tools work. In this scenario you often hear discussion like, “If person X is hit by a bus, what do we do?” Or, as one of us authors prefers to put it, “What if person X wins the lottery?”
3.    Finally, even the most stable, well-documented internally built system is still at risk from the fact that a business’s needs are ever-evolving and business analytics consumers are ever-changing—particularly in gaming. So, once the engineer(s) of this system leave, even though it is stable enough to survive, there is no one left to make the improvements that are needed to keep pace with the evolving business.

On the other hand, when outsourcing your BI/CRM solution, there are still vulnerabilities:

1.    Many solution providers require a data warehouse. These projects tend to be massive in scale and suffer from the same “half-life” rules as internally built projects. Six to 12 months is a long time for consultants to be working onsite, and yet this is typical of many data warehouse projects.
2.    Some solution providers insist that you know, today, everything that you are going to need a tool to do for you for the next five years. They send in a SWAT team to configure your system, expect you to tell them everything it’s going to do, and then leave. Then, a few months later when your business needs change, they either are nowhere to be found or want to charge you to update the tool.

The casino industry is full of horror stories of well-intentioned BI/CRM builds that simply did not work—or, if they did, required extensive (and expensive) modernization just a few years after completion.

The Dreams and the Reality
The dream of software is to materialize your ideas, and the world is full of dreamers with endless ideas for new applications. The reality of software engineering is that building simple applications is very straightforward, but in a competitive and fast-moving world, the harsh reality is that we need to learn the highly specialized software engineering skills to meet the needs of the dynamic market in which we live. In addition, as we enter into software engineering projects we also need to remember that our customers are also changing in usage and functional expectations.

The harsh reality of software is that elimination of bugs is nearly impossible and that user requirements are constantly changing. Furthermore, the reality is that building software is a specialized business probably best left to the experts.

The Next Series
We thank our readers for sticking with us through this year-and-a-half journey of discovery and debate. The topics varied wildly but always focused on finding the money in the gaming business. Clearly the industry is going through a radical transformation with the adoption of mobility and online gaming experiences. This transformation creates an uncertain and exciting future.

From here, we are embarking on a new 36-part series that will build out the core mathematics of the money of gaming. In this new series we plan to cover everything from the definitive mathematics of player response rates to the true analysis of gaming pay tables. As we embark on this journey, we will do so with a twist, exploring how knowledge of the mathematics of our industry can enable us to grow and thrive for the next decade and beyond.

1    CEM December 2011, Cardno, Thomas, Where’s the Money, Part 6: Player Experience and Slot Optimization.
2    CEM December 2008, Lewin, Cardno, Singh. Let’s Talk Turkey: Applying Retail Market Basket Analysis to Gaming.
3    CEM Feb 2013… Cardno Thomas.
4    CEM Where Is the Money Now Part 1 January 2013, Cardno Thomas.
5    It is all perspective, actually the probability is normal it may be the players understanding of streaks that make it appear extreme.
6    Refer to extracted May 2013.
7    Extracted from…on October 2013.
8    Refer to CEM….
9    The One to One Future, Don Peppers and Martha Rogers 1996.
10    CEM 2013 December, Cardno Thomas. Where is the Money Now? Part 12 of 18.
11    CEM 2013 December, CEM 2014 January.
12    CEM June 2013, Cardno Thomas, Where is the Money Now Part 6, The Human side of Perspectives of Data,….
13, extracted July 2013, Woodruff & Gregory.
14    The Math That Gaming Made, Cardno, Singh Thomas 2013.
15    CEM Where is The Money Part 9 of 18, Cardno, Thomas.….

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