Where’s The Money Now? Part 1: The Youthful Player

We’re following up our popular “Where’s the Money?” series and looking forward to the upcoming 18 months, during which we will tackle more tough analytical questions. This article examines changes in player demographics, in particular the games that different age groups like to play. One notable example of player preference is that younger players are playing heavily on electronic table games (ETGs). We will show how ETGs and their small, but growing, market presence may well be a portent of future gaming preferences.

An interesting juxtaposition that this creates is that at least one slot machine manufacturer is heavily focused on Internet gaming. This focus on i-gaming creates a situation whereby a primary provider of gaming products is operating an online gaming operation that could, in some jurisdictions, compete directly for total gaming time—including in the brick-and-mortar casino. This article sets the stage for how we can analyze and market to potential i-gaming customers and how we can monitor the effects of the i-gaming product line on our brick-and-mortar properties.

Entertainment Customers
The defining behavioral characteristic of an entertainment customer is that he spends both time and money. This distinction of spending both time and money completely differentiates the behavioral characteristics of an entertainment customer from a retail customer. Apart from science fiction alternatives, such as the memory implants in Total Recall, there are limited counterexamples to entertainment being a combination of both time and money … and we will leave those examples to your imagination.

This defining characteristic of entertainment customers creates a real set of analytical challenges. It is not enough to ask the question, “How much money is a customer spending?” We must also ask the question, “Where is the customer spending their time?” In fact, it is the spending of time that is core to the gaming customer’s experience1 and, therefore, is the critical driver for optimization of the gaming experience.

“Same Store” Revenue Growth
Driving innovation is a core goal of many businesses. In fact, Parts 1, 2 and 3 of “Where’s the Money?”2 showed how innovation is a core principal of how a business evolves and changes over time. When looking at product innovation, one of the fundamental benefits is appealing to new and different customer groups. 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 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, though this growth is sometimes achieved by adding completely new product types. For example, Walmart’s addition of a tire center brought same-store revenue that was purely incremental to its business. In fact, the 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 world as well, ranging from the addition of hotels to new gaming products. Within retail, the same-store opportunities can be analyzed simply using the market basket3. However, in the world of gaming, we need to dig into the customer type and customer preferences to understand the market basket.

The Flaw of Averages
The casino business was built on averages. A casino makes money because, in the long run, the average amount of money that a casino takes in is equal to the amount of money wagered times the house edge (or hold percentage). However, the casino also relies on the fact that averages are a very poor statistical measurement tool. Imagine you are a customer and you put $100 in a slot machine, then receive $90. Then you take another $10 out of your wallet, add it to the $90, put another $100 in, and get another $90 out. You repeat this until you no longer are able to put $100 in. Doesn’t sound like much fun, does it? But because the customer does not experience the average result all the time, gambling is in fact an entertaining experience.

And yet, casino operators have fallen prey in the past to the flaw of averages as a statistical tool. An exhaustive survey of the misuse of averages would probably fill an entire book, so we will list only three here:

1. “Our slot floor holds 10 percent? Then we can give 1 percent cash back on all games, and it’s a 10 percent reinvestment in our customers!” Unfortunately, this type of thinking ignores the fact that a 10 percent floor likely has games ranging between 1 percent and 15 percent hold, thus for some customers, giving 1 percent back is a 100 percent reinvestment.

2. “That game has $500 WPU? Get more of them!” This logic fails to consider that some games have a high win with low play from a few wealthy customers. Getting more of these games dilutes their play across multiple machines, often to the point that instead of having one game with $500 WPU, the casino ends up with two games with $250 WPU.

3. “Our customers across all products are 53-year-old women [derived from an average age of 53 and a percentage of women customers above 50 percent]? Change our marketing and product to focus only on their needs!” Want to guess what will happen to your businesses if you follow this advice?

The next time you are in a meeting and someone is trying to draw big conclusions from averaged data, think about the distribution of data elements, such as product or customer segment that make up that average. Are their conclusions really only targeted to a small subset of the data that resides closely to the average numbers off which they are working?

Looking at ETG games for this article, numbers 2 and 3 above both potentially apply.

It is possible that the ETG games have a lower win per unit than traditional slots at some casinos today. The temptation for the operator is to remove these ETGs and replace them with traditional slot machines. However, as we demonstrated in “Where’s the Money? Part 13,” this type of WPU-focused optimization can often lead to lower aggregate revenues, not higher. The slot operator’s responsibility is to protect the revenue coming from ETGs, as this is an audience unlikely to shift their play to traditional slots if the ETGs are reduced or removed.

From the marketing side, at least one of the authors often hears statements like, “But that’s not our customer! Our customer is …” The fact of the matter is a casino has thousands of customer types, and deciphering them requires a deeper look into products as well as a behavioral analysis. There is no one customer type, and averaging thousands of different types into a single type leads to a marketing effort that only satisfies those types near the mean. This leaves many customers in the outlying customer types out in the cold.

Who are Casino Players?
Thomas Garrett’s “Casino Gambling in America and Its Economic Impact” study from 2003 noted, “the basic findings from the Harrah’s survey4 are discussed here to provide insight into the average characteristics of the casino gambler. An estimated 53 million people in the United States participate in casino gambling. This is equal to 27 percent of the population aged 21 or older. The median age of casino gamblers is 46, compared with a median age of 45 for the U.S. population. However, gambling is most popular among adults aged 51 to 60. The male/female ratio for casino gambling is 45/55, compared with 48/52 for the general population. Forty-six percent of gamblers graduated or attended college, compared with 43 percent of the general population.”5

As Garrett states, while the casino customer is a similar age and gender mix to the overall United States, the age group where gambling is most popular is the 51- to 60-years-olds. Furthermore, it is our experience that this older group of players heavily outspends younger players and has very different game preferences. From this analysis, the following conclusions can be drawn:

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 as the numbers will be heavily skewed to the demographic profile that spends the mostly heavily.

2. We can also conclude that a wide range of customer age and gender mixes are already trying the casino experience. We speculate that this wider group is an opportunity to diversity the core customer base, but we will need careful analytics to isolate their desired player experience.

Looking at the difference between optimization and outcome6 showed how it is simply suboptimal to optimize based on theoretical win or, in fact, based on revenue of any kind. This is further reinforced by the following analysis, which shows that there are quite different demographic profiles playing in the casino—an issue that is compounded by the need in today’s world for an ever-increasing supply of gaming product.

Let’s consider a very simple example where there are only two groups of customers, one older and one younger. The distribution of spending is likely to be a bimodal distribution, and as such, it is dangerous to average these players. In fact, even the idea of averaging an older player and a younger player to create a middle-aged player seems counterintuitive. Furthermore, when we look at the lifetime value of players, clearly the younger player has a higher potential. Finally, the younger player is an example of how we might be able to increase same-store growth without fighting with neighboring casinos for the same customer base. Let’s take a look at these younger players, as a powerful way of tackling product innovation-based changes is to find products for players who are younger. When taking this approach, the first place to look is at the existing products on the gaming floor to see if they are attracting a younger player.

Figure 1: Age/Gender Profile of Gaming Customers by Game Type
Figure 1: Age/Gender Profile of Gaming Customers by Game Type

In Figure 1, we show the typical age ranges of different gaming product segments from a real but unnamed casino. The dominant gender is also indicated by color—blue for male, pink for female. The core slot players are in their 50s and 60s. Video slot and mechanical reel players skew more female. Video poker and slots have fairly equal gender distributions.

The interesting observation from Figure 1 is that the ETG player is the youngest band and is heavily dominated by male players. This youthful group is playing the new ETG games, and this suggests that we should explore the ETG market to find “same store” opportunities.
The ETG player is actually very similar in profile to the traditional table player—not as young as roulette players, but younger than blackjack and craps players. There is a high mix of 20-something players, comparable to roulette, craps and blackjack. Thirty-seven percent of ETG players cross over to traditional tables, but only 11 percent of traditional table players cross over to ETG. The segment of ETG-only players is still quite small at 16 percent of ETG and traditional table players.

The actual performance numbers are obfuscated for privacy reasons, but we can say that the performance of these ETGs is just not the same as the performance of a traditional slot machine. However, as is indicated by the age variation, our working hypothesis is that the ETG player is simply a new category of player and does not play traditional slot machines. To gain an understanding of these players, let’s dig deeper into the public data that can help us understand this new player and, we argue, potentially incremental market segment.

Our comparison of typical gaming customers to ETG customers indicates that these players are significantly different in their age/gender profile. In simple terms, the players of ETGs are younger and predominantly male, and furthermore, for some games (for example, craps) are significantly younger. (A deeper and probably required analysis has been left for future articles.)

Figure 2
Figure 2

The Pew Research Center shows some interesting numbers in Figure 2. Social networking is the most variant between different age groups, with very low (65+; 29 percent) adoption of social networking. This difference in social behavior opens up some quite different avenues for communication—for example, social communication, and potentially a need for looking at innovation around social networking for the younger customer.

What’s more confusing is a demographic versus behavioral data paradox that is arising in gaming. First, changes in the ways that customers want to be treated based on demographic data have never been starker. From mass media to direct mail to e-mail to Facebook to Twitter, the once-simple communication channel choices have become immensely complex. Games like ETGs bolster the argument that customer demographic data, particularly age, is vitally important to understanding what product to provide. And now the paradox: When one builds a predictive model on what characteristics of a customer are likely to impact their response or lack of response to a marketing offer, the importance of demographic data is dwarfed by the importance of behavioral data. Put another way, it is far more important to a marketer how many times a customer comes to the casino than how old they are.

We leave discussion of this paradox for another time, and hope it sparks debate amongst our readers. To be sure, the key to the future of the casino business lies in unlocking the secrets behind the “DvB paradox.”

But when looking at the younger players, there is little doubt that they will be more open to i-gaming offerings. This is both good news and bad news for the gaming industry. The good news is that the current customer base, which tends to be older, is less likely to include early adopters of i-gaming products. The bad news is that the future of the industry rests on its ability to build a product offering that continues to attract new players. As time passes, the natural progression is likely to be that all ages will become more comfortable with social networking and i-gaming offerings. It is the job of the industry, then, to develop products that appeal to the more social network-aware player who will make a decision to visit the property instead of consuming gaming entertainment at home.

Bringing it all Together
Our example with ETGs shows how traditional views of customers based on their theoretical win per day may result in substandard revenue. Instead, we propose a more “category-focused” customer and product analysis. This category approach looks at each product category in the same way that a retailer looks at different departments, considers the revenue and customer groups of each category, and builds optimization models that rely on these differing product demands. The example of ETGs is only the first step. Future articles will build on this analysis to show how incremental revenue is generated by this new and different product line.

1 CEM December 2011, Cardno, Thomas, Where’s the Money, Part 6: Player Experience and Slot Optimization.
2 CEM July 2011, Cardno Thomas DeRaedt, Where’s the Money, Parts 1, 2 and 3.
3 CEM December 2008, Lewin, Cardno, Singh. Let’s Talk Turkey: Applying Retail Market Basket Analysis to Gaming.
4 The Harrah’s 2002 survey titled, Profile of the American Casino Gambler.
5 April 2003, Casino Gambling in America and Its Economic Impacts, Thomas A Garrett.
6 CEM December 2011, Cardno, Thomas, Where’s the Money? Part 6: Player Experience and Slot Optimization.

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