Jay Sarno

What Is At-Risk Analysis?

In our last article, we discussed market break points and explained how you can calculate the pattern of geographic disruption when a competitor enters your market. We also provided a visual representation of how the market would be split between the two competitors. The final part of the modeling for breaking points, and a much harder process, is to calculate the potential revenue that is at-risk to your market due to the new competition. For purposes of this analysis, at-risk refers to the quantity of revenue that is currently associated with the zip codes in the areas that due to competition will be potentially lost to your property. Another way to look at this is that your competition, simply by virtue of geography, will own zip codes that you had previously dominated.

Our analysis of the at-risk revenue is an offshoot of gravity modeling. Gravity models are used for building simulations that show the potential revenue capture based on distance to the population and the tendency of that population to gamble. They are used in retail, hospitality and other similar venues where the distance between competition is the larger determining factor for patron capture. We have found that at the point where there is some distance between the properties, the gravity or the “size factor/attractiveness” of the property and its amenities is a lesser factor in the model than is closeness to population. In that regard, we will be discussing who owns a zip code once a new competitor enters the market for this analysis.

Gravity models are used for building simulations that show the potential revenue capture based on distance to the population and the tendency of that population to gamble.

The following table shows a review of the existing customer rated play database for the most recent year by the top 25 revenue producing counties. As you would expect, the closer in to your property, those counties produce the lion’s share of the revenue.

In our at-risk model, we then seek to determine which zip codes within the counties are owned by your property or owned by the competition. By using driving distance as the sole determiner of which zip codes are owned by which entity, we can then see which zip codes are closer to you and which are closer to the competition.

Based on layering the distance factors from your property and the competition, we estimate that of your total $33 million in annual rated revenue, your property has $7 million rated revenue “at-risk” or a reduction of -21% in the first year. Given that the rated revenue is about 70% of total gaming revenue, that would equate to a $10 million revenue reduction.

Our example assumes the competition is largely similar in size and amenities. While some may find this a bit too simplistic, for this demonstration it is necessary to provide the basic math that you can use to determine your potential losses. There are subsequent manners of analysis that can be much more precise such as determining the weighed differences in amenities and how those impact patron choice. These also become part of the who owns what battleground planning as well. We will discuss those in future articles.

Jay Sarno has 20+ years of experience in the Hospitality and Gaming Industry. Jay consults on casino marketing segmentation programs, software product development and technology solutions evaluations, selections and implementations. Jay has implemented over 20 data warehouse systems and currently also teaches courses in Hospitality Management for Richard Stockton College of NJ. Jay can be reached at JSA2002@comcast.net