Authors’ Note: In this second article of our 12 part series on customer centric gaming floors, we dig into the player experience and why theoretical win is just plain wrong when looking at game performance.

The casino industry’s methods of analyzing gaming devices are based on metrics, such as win per unit per day or theoretical win per unit per day. In this article we dig into two types of metrics, optimization and outcome, and we show why traditional outcome metrics, such as theo win per day, are just not effective at optimization. In short, if you are still using outcome metrics to optimize your gaming floor you are leaving money on the table.

We will also take a deeper look at customer experience and uncover optimization metrics that are indicative of customer experience, and with proper usage these metrics will drive improved outcomes.


According to Statista (, U.S. gaming revenue hit $73 billion in 2015. This figure puts domestic gaming revenue growth numbers back at the pre-recession levels of approximately $3 billion per year (see Figure 1: U.S. Gaming Revenue Chart pg. 15). For the purposes of our discussion, please note that before the recession growth in many markets occurred with a similar level of supply, leading to situations where many casinos experienced significant growth by simply being great operators and managing their casino floors by ensuring they got more games with high theo win per day and removed games with low theo win per day—in other words, by using outcome metrics to try to optimize these same outcome metrics. 

But for better or worse, many of these markets have changed post-recession; gaming has expanded supply significantly in the last 10 years, leading to an increased number of increasingly competitive markets.  These markets require that the casino understand its customers when making slot decisions.


When thinking about optimization, we simply cannot think of slot machines as proxies for players; we have to think about the player experience as he or she plays their game. Players do not sit at their game and think about the theo win per unit per day of the gaming machine, they sit and think about the gaming experience that has engrossed them. Players do not sit and think about the hold percentage of the game, they instead watch the amount that they have won or lost and how fast this gaming experience is happening. Finally, the amount that a player bets is highly variable based on choices that the player makes. All this brings us to a bold statement: The player choice of game speed, the player choice of amount bet, and the reaction of the player to that gaming experience are central factors in our gaming floor optimization.

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

Optimization metrics:These are metrics that 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. Occupancy is one such optimization metric… this is a metric that the player experiences directly as they are either able or unable to find their game of choice.

Outcome metrics: These are metrics that the players do not observe. For example, the theoretical win per unit per day on the gaming machine. 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.  Hold percentage ignores game speed although, clearly, it is at least a combination of the speed of play and the hold percentage that determines the price of the game.


Digging deeper into our player-centric slot optimization philosophy, the goal is to improve the player experience, thereby driving revenue and beating the competition. One cannot improve the player experience by watching win per unit (WPU) numbers. Rather, we must make changes to our slot floor to improve the player experience, and do so in ways that drive incremental player spend.

Consider this illustrative example: Imagine having an extremely popular slot machine. WPU for this slot machine is four times the floor average in WPU. Looking at some of the optimization options we encountered previously, it is tempting to look at that game and say “great, it’s doing its job,” and then focus on improving the WPU of lower-performing products. But now, looking at optimization from the players’ perspective, a game with a WPU of four times the house average is a potential problem and, furthermore, a revenue opportunity.

If this high-performing game is in the high-limit room, it is likely to have low occupancy. But if it is in a high-volume area of the floor, it might be a problem. Quite simply, it may have a negative effect on player experience. In an extreme example, if the game is 100 percent utilized, players are in effect always competing to play. Furthermore, these players may choose to play at a competing property where the product is more available. In this situation, the opportunity exists to optimize occupancy, enhance the player experience and drive incremental revenue. As such, we can apply occupancy to drive incremental revenue by enhancing the player experience.

Of course, that’s all fine and good as a philosophy for this simple example, but in the real world our patterns are finer-grained, our data is large with many dimensions, our players numerous and our capital is limited. The next section introduces some methods for handling these real-world challenges.


Let’s take this to the next level by looking at optimization metrics in more detail and showing how these metrics can be applied to data to provide a new view of the gaming devices and the players that play them.

The player cares about time on device and their gaming experience. The gaming experience is expressed as expected loss over an hour of play, otherwise known as Theo per Hour (TPH). The factors that drive TPH are:

• Average bet—Certainly how much the player spends per play is a big part of TPH.  Average bet itself is a complicated function of the configuration of the game including denom, minimum bet, maximum bet and maximum bet to cover all lines.

• Game speed—A slow game can provide for longer time on device, whereas a fast game can provide for more exhilarating action. The player can choose which speed suits their play style better, but this factor is vital to understanding game performance in general and to TPH in particular.

By cross tabulating the quadrants player TPH versus TPH of the machine, four categories of playing experience are created (see Figure 2: Cross Tabulation of Player vs. Game TPH):

Grinders:These players are spending below their typical TPH. If this is because they cannot find the gambling experience they want, then we have an opportunity.

Losers: These players are being hammered. Unless they are lucky, they are unlikely to have the wallet to continue this gaming experience.

Gamers: These players are spending at low amounts and often represent the majority of the occupancy on the gaming floor.

Gamblers: These players probably drive a large portion of the revenue in the property. It is critical that we optimize their gaming experience to ensure that they find the gaming devices they want.

To optimize this we want patrons to have the gaming experience they are looking for. This experience should be neither above nor below their “expectations” and should be on the game they desire at the location they desire. The outcome of this optimization is that the casino will make more money; however, it is clear that the decisions that will be made given the understanding of price from TPH will be different to those made with the traditional outcome metrics.


The price of the gaming experience is not enough on its own to do the optimization, we also need to look at availability. Players experience these metrics as much as they experience price, consider the simple question “Is my game available?” To answer this question, we leverage occupancy or what percentage of the time is the game occupied. The challenge with occupancy is that most gaming systems do not report the number. This lack of fundamental data forces us to build models to approximate the true occupancy. These models need to combine the carded play with the uncarded play to build a best guess at the occupancy of the game, a calculation we will tackle in a future article. 

Let’s finish this particular discussion with an illustrative example. Suppose there are three games available and we need to decide which game needs more units. In terms of our metrics, we have:

• 50 percent occupancy and $15 TPH;

• 20 percent occupancy and $50 TPH; and

• 5 percent occupancy and $250 TPH.

Let’s calculate WPU for each scenario, taking the occupancy x 24 hours in a day x the TPH. The WPU for Game A is $180, Game B is $240 and Game C is $300. A traditional WPU analysis of these games would leave an analyst thinking that Game A is the weakest and that no more units are required. However, what if it was discovered that incremental spend from the player occurs once occupancy crosses a certain threshold, say 30 percent? If that were the case (and significant slot change analytics needs to be done to determine this threshold), then only Game A could provide incremental spend via incremental units. Adding more of Games B and C may only result in diluting the performance of the existing games.

One thing to keep in mind when considering occupancy: it can potentially be as dangerous as overall floor hold percentage as it does not consider peak times. When looking at optimization metrics it is also important to consider peak time occupancy as the extremely high-demand games can fill quickly creating yield optimization opportunities. But it is the experience of the authors that the peak time occupancy tends to be highly correlated to the all of time occupancy so it is a reasonable starting point for your optimization.


An increase in theoretical win is a wonderful outcome and we all strive for it when we optimize the gaming environment. We have shown how it is quite wrong to use this important metric in optimization, we need to look deeper into the player experience and look to optimize the floor using metrics that the player can feel. It is these optimization metrics that truly give us a competitive advantage. In an industry that currently sells math model-based experiences to players, it is critical that we also use the correct math in determining how we run our gaming floors.