As you might expect from the title of this article, I’m going to discuss the collection of gaming data—and what to do with it once you have it. It’s said that all tracking systems are created equal, but this requires clarification, as all tracking systems don’t perform the same functions.
In the beginning, operators recorded mechanical meter readings each day. Personnel were assigned the task of physically walking the casino floor, going from machine to machine and documenting the meter readings on paper. The information collected would later be transferred to a database and used to generate daily, weekly, quarterly, and annual reports on revenue and performance.
This manual system of collecting slot information became unnecessary after a couple of enterprising companies decided to assist the industry’s growth in slot gaming by developing hardware and software to automatically track the movement of the coin-in, coin-out, drop, jackpot and handle meters. This automated feature became known as slot information system or “SIS.” [Note: SIS data should not be mistaken for player tracking data; player tracking is an add-on module that is primarily driven by the SIS data.]
Depending on the desired results of your analysis project, selecting the correct data fields will enable you to generate the results you are looking for. To best illustrate this process, let’s plan a project and walk through the steps to complete it.
Our project is to find out which manufacturer on your casino floor offers the highest earning potential. This process will require data fields associated with the SIS you have installed. After we complete the initial analysis of manufacturer performance, we could also add customer data to see which market is the biggest driver for that manufacturer, but this isn’t necessary at this phase of the analysis.
First, we’ll need to acquire the information necessary to begin our review. The following data fields are required for this analysis:
• House Number
• Denomination
• Manufacturer
• Model (E Game, S92000, Game Queen, etc.)
• Game Type (Red Hot Sauce, Coyote Star, etc.)
• Hold Percentage
• Coin-in (YTD)
• Drop (YTD)
• Win (YTD)
• Handle (Games Played)
• Days on Floor (YTD)
• Date on Floor (Install Date)
Once we have this data downloaded to a spreadsheet, we can calculate the win per day per unit (W/D/U) by dividing the total win by the number of operating days in the days on floor field. The outcome of this calculation needs to be included for each game operated, as well as the handle per day and coin-in per day.
You can apply the same calculation to handle (H/D/U) and coin-in (CI/D/U) as we did for win.
The game characteristics might look something like Chart 1 in your spreadsheet. [Note: Manufacturer, model and game type names have been changed to maintain objectivity.]
With the addition of the three calculated fields, entered as W/D/U, H/D/U and CI/D/U, the revenue data will look like Chart 2.
Moving into our analysis requires us to first associate our revenue to each of the manufactures independently. To accomplish this task, we are going to use a powerful Microsoft Excel feature known as a “pivot table.” A pivot table organizes data in the requested format for quicker reporting. In Chart 3, you’ll see the outcome of our pivot table associating all coin-in generated by each of the manufacturers separated.
Chart 3 provides a high-level review of manufacturer performance as it relates to popularity. The popularity statement is based on coin-in, which is the first indicator of customer preference when playing electronic gaming devices.
In Chart 3 we see that “Manufacturer A” commands the popularity on our floor, with an average CI/D/U of $1,251.20. We can’t use the total coin-in of $25,213,202 to determine popularity because the large number of units operating on the casino floor exaggerates this number. To “true up” the analysis we must drill down to a single-unit comparison between the multiple manufacturers.
Once we have this information displayed in this fashion we can begin our second drilldown: discovering which of Manufacturer A’s game types drive the bulk of the coin-in.
Discovering the popularity of a game model through analysis that drills down to core data will help slot directors make their next purchasing decision—a decision that has the potential to motivate longer playtime in the casino. Of course, over-saturation of a game type or model will occur at some point, requiring a review such as this to reduce the negative impacts it will have on your operation.
But before we overreact to the model analysis we need to continue our drilldown process to the game themes as they reside on the top of the game models. It could also cause a negative effect on coin-in if too many of the same game themes reside on the casino floor.
As we continue to drill down into the data, we become increasingly aware of the value of each game type driving our revenue numbers. For example, Chart 5 reflects an average win of an AWP product at $105.32, but through the drilldown process we quickly recognize that the game themes offered on this model are revenue-share games associated with large Wide Area Progressive jackpots. They hold near 7 percent, compared to the Game Queen products with an average hold of 3 percent.
Without a drilldown process like this one to answer your questions, assumptions as to the answers may impact gaming revenues that might otherwise be offset through a careful review of game performance as compared to other gaming devices operated on your casino floor.
Rich Lehman is VP of Development for the Las Vegas-based Navegante Group. A 26-year gaming veteran, he has served as VP of Slots, VP of Casino Operations and General Manager.
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