In my last article that ran in July Slot Management & Marketing, I covered the importance of data segmentation in creating fair share reports that chart strengths and weaknesses across the casino floor. The next step… building on your slot analytic basics by adding indexing and confidence intervals. Though simple, the application of indexing reports and dashboards can be pivotal to gain performance insights.

The most basic indexing is the Win Per Unit Per Day (WPU) of an individual game compared to your floor average WPU. For example, if a game is performing at $150 WPU and your floor average is $100, the index would be 1.5 or 150 percent. While indexing to floor average may seem trivial because you can most likely recite your house average WPU as easily as your home address, it does allow for a simple comparison which can then be layered in various ways. Adding additional indexing by denomination, section and game category, much like segmentation, allows for fair comparisons and realistic performance standards. 

Indexing based on a games’ performance in comparison to the section it is placed in can help segment your performance standards much like you zone your floor. Just as you wouldn’t place a penny game in your high-limit room because it would fail, you shouldn’t compare a penny game to a game placed in your high limit room because in comparison it would again fail (with a few exceptions like Wheel of Fortune and Buffalo Grand). Indexing creates accurate and balanced standards of game performance to help you more effectively manage your slot floor.

 

Central Game Performance Database (GPD)

 

In the Central Game Performance Database (GPD), we aggregate game level indexing to casino and, more importantly, zone (section) average; effectively gauging game performance to produce and publish the monthly EILERS-FANTINI Game Performance Report. Operators provide individual game level indexes to their relative floor and zone performance, so we can efficiently standardize and aggregate performance across differentiating operator types, jurisdictions and sizes.

In addition to aggregating performance by zone indexing, GPD users can also filter down to specific regions, jurisdictions, and operator types to gain insights. Standard filters available in the GPD include base denomination, own status and supplier as well as others to allow for further segmentation. In the Low Denom/Video Reel table below (video reel, base denomination less than $0.25, and casino owned status), you may notice another metric—the 95 percent Confidence Interval (CI) column. While this metric is vastly important when dealing with a large scale of performance metrics across various operators, it can also be used on an individual casino level, assisting with incremental unit placements. A CI displays how close all individual games index to their grouped overall average index. 

For example, an average index for two slot themes, Game A and Game B, can both be 3x but have very different individual reporting indexes. Say both A and B have three units reporting—Game A unit’s report indexes of (2x,3x,4x) and Game B unit’s report (1x,1x,7x). Both sets average to an Index of 3x, but we are more confident that Game A will perform at 3x and less so that Game B will because of how close each game reports to the overall average. Thus, we display Game A with higher confidence and more likely to return 3x results.  

Utilizing CI will help determine which themes and cabinets to keep adding to your floor without getting swayed by outliers. More importantly, CI paired with Indexing allows you to look beyond basic WPU and determine which games on your floor to renovate and which to demolish completely.