Authors’ Note: In this sixth article of our 12 part series on customer centric gaming floors, we will examine customer wallet size, and how it influences a whole host of slot floor pricing strategies. Please note these articles are meant to stimulate thought and that we are using some deliberately provocative metaphors and examples which should be taken with a grain of salt.


For operators looking to set a proper price on the gaming experience, an important first step is determining the customer’s wallet size.

To calculate this factor, we need to dig into the wallet measurement equations and the methods on how to estimate this needed number. Armed with the wallet size, we can then define a new metric: median cost of the gaming hour (MCGH). This MCGH is critical in understanding the player experience, as it shows the way that the size of the player wallet impacts on the gaming experience. We can then apply MCGH to illustrate how the volatility of the game impacts the price experience given by the game as a function of wallet.   

WORTH THE PRICE?

To start, let’s take a closer look slot floor game pricing and how it is usually determined within the casino industry. Here’s the traditional definition of “price” from the online version of Encyclopedia Britannica:

“Prices perform an economic function of major significance. So long as they are not artificially controlled, prices provide an economic mechanism by which goods and services are distributed among the large number of people desiring them. They also act as indicators of the strength of demand for different products and enable producers to respond accordingly. This system is known as the price mechanism and is based on the principle that only by allowing prices to move freely will the supply of any given commodity match demand. If supply is excessive, prices will be low and production will be reduced; this will cause prices to rise until there is a balance of demand and supply. In the same way, if supply is inadequate, prices will be high, leading to an increase in production that in turn will lead to a reduction in prices until both supply and demand are in equilibrium.” 

In the modern slot gaming industry, there is a long-held, and much debated, belief that hold percentage is equatable to the price of a game. In short: hold percentage is the percentage that is expected to be won by the casino in the very long term. From the casino’s perspective, equating price with hold is very tempting, as most slot machines come with different par settings, each of which has a well-defined hold percentage. So to simply call this number the “price” of the game is very neat and tidy.

However, the hold percentage is not something that players feel directly. What they feel is the win/loss impacts of the game they are playing.

To illustrate the challenges of hold percentage as price, consider a very small casino with two slot machines with the same hold percentage. The difference between the two games is the volatility, the first game (A) is highly volatile and the second is flat (B). Designers of games work for years to build the right math models to create good gaming experiences and a lot of this effort is spent managing the volatility of the game. In this case the players on game A will have a very different gaming experience to players on game B. In other words, while the statistical prices of the games are the same, the actual gaming experience is quite different. This means that the players experience volatility and will think of gaming more by how often they win or how much they win rather than the almost invisible long-term hold percentage or house advantage.

Now, from the customer perspective, the price of a purchase of a gaming experience is a very different story—it is more like a wild journey through a mathematical universe. This wild journey is largely controlled by the consumer and the amount that they decide to spend. To understand the impacts of winning and losing let’s consider the bet amount and the response to a winning or losing event in the following chart:

Gambler—Players that increase their gaming amount in response to winners are often real gamblers; they might say they are hot and they are on fire trying to draw out and increase the winning experience.

Money Manager—The world is awash with theories on how to make money from casino, often by increasing your bet to recover losses and therefore ensuring all gaming sessions end with winning; sadly these methods do not work and in the long term all money managers face the house advantage like all other players. They can however win in the short term by taking potential for a large (often catastrophic) loss.

Prudent—The prudent gambler takes their winnings and uses the money to pay for dinner or their hotel bill.

Wallet concerned—The wallet concerned player is likely to have a desire to play for a period of time and might decease their bet amount to extend their time on device.

Finally, there is the player who does not change their pattern of gaming in response to winning or losing. These players are steady and, we think, quite unusual in the gaming world. The authors experience is that players change their gaming either by varying the time at game or by the bet amount. Now the great challenge is that the data for the individual gaming wins and losses is not generally available and that most gaming systems only collect the data for the gaming session or by polling meters on an hourly basis. This lack of data means we have to resort to clues in the data to determine the impacts of winning or losing on player behavior.

VOLATILITY & WALLET

As we continue this wild journey towards understanding the price of a game, let’s take a moment to pull over to the side of the road and explore two very important but rarely measured metrics:  volatility and wallet.

Volatility is a metric that is treated by manufacturers as an attribute of the paytable of a particular slot machine. They run statistical analyses on this paytable and determine the statistical variation in the outcome data of the slot machine… assuming it is played with one coin and one line. Herein lies the challenge; our customers almost never play one coin / one line. So the volatility score provided by the manufacturers, in fact, has little relation to the volatility experienced by our customers.

As described in the above section, there are many different types of customers with many different betting habits. It is vital then that a casino apply statistical methods to the data from their customers when measuring volatility.

Next, we define wallet as the amount of money the player is prepared to lose in a gaming session. Like volatility, there are challenges in measuring this important statistic. As mentioned above, gaming systems do not track individual outcomes of every gaming wager, instead opting to roll the data up by session. Thus, without the detailed gaming data or asking people how much money is in their wallet, we just do not know what the customer is prepared to put out when they visit the property.

This is a huge analytical challenge, imagine two players with the same average bet (a number we know) and very different wallets—one large and one small. The large wallet player has a greater ability to ride out the volatility of the game and experience the full gaming math. Think about a player chasing a royal flush on video poker; if the player has a large wallet they can play aggressively for a long time in the hope of winning while the lower wallet player will simply have to walk away. This difference in wallet dramatically impacts player behavior.

NEW MATH

What we need are new metrics that move beyond the expected (or statistical) to the typical or median cost of a gaming experience. Over a long time, the average gaming experience will always tend to the theoretical or expected value of the game, however the median experience can be quite different, depending on both the volatility of the customer experience and the size of the customer wallet. In formula form, this would be stated as: Price of a Game = Median Cost to Play for an Hour.

We plant a stake in the ground and decide to define the cost of a game in terms of one hour of play. Certainly this time period isn’t required and, in fact, casinos should analyze their own database of customers and examine the typical length of a gaming session.

With this settled, as operators, we need to study how our customers experience an hour of play on our gaming devices. Here is where the “flaw of averages” gets deeply exposed. Imagine a slot machine with 10 customers each playing for one hour.  The first nine customers all lose $200.  The tenth customer wins $800.  Thus our customers as a group have lost $1,000 (as calculated by nine times $200 loss plus the $800 win), and on average they have lost $100.  But to say the cost of the game is $100 is madness!  None of our customers come close to that experience.

Instead, we need to use the median cost to play for an hour as our price metric. In the example above, the median loss for our customers was $100, which is highly representative of the experience of the vast majority of our players. In practice, the data will be much more volatile, but this measure of median experience will continue to hold up as the best measure of the aggregate customer experience.

Now let’s examine how this new metric interplays with volatility. A game with high volatility often has a large number of losing sessions combined with some very large winning sessions.  Taking the average experience gives excessive weight to these large winning sessions, and ignores that fact that a high percentage (often as high as 99 percent) of our customers do not get to experience these large winning sessions. The median metric will only take into account the number of winning sessions, not the size of the sessions, leaving us with a much truer picture of what the majority of our customers are experiencing.

BRINGING IT ALL TOGETHER

In practice, to calculate price, one needs to apply measure similar to volatility. We need to look at the outcomes of our customers, place them in bins approximating one hour of play, and apply the median statistic to the data set. In addition, we could (and should) create additional statistics that look at the median cost to play for losing session, for winning sessions, as well as percentage of winning sessions.

Empowered with these new metrics, we have a much clearer understanding of what our customers are experiencing and thus can devise our floors and purchase our products in ways that leverage this new understanding.