In our last article we discussed the impact of existing and potential new com petition in light of how casinos must work to penetrate markets within their environments. We looked at how to prepare a very high-level market visualization of the demographics and competition. We also proposed that the process should include both a market penetration analysis, which is highly simplistic in its nature, coupled with performing the more advanced process of building potential market revenue.
In contrast to penetration rate, which is based on a relatively simple comparison of current customer headcount to overall population in target area, potential market revenue is different. It is determined by comparing Property A’s population by zip code based revenue generation to the potential revenue within the same zip code. These calculations are based on gaming revenue generated by Property A’s existing player database compared against the estimated gaming market potential based on zip codes for total gaming age population and their specific income levels.
With that said, we will discuss the steps for building market penetration analysis in the remainder of this article and in future articles will discuss all the steps involved in building potential market revenue models.
Steps to Build a Market Penetration Analysis
As part of the strategic planning process, you must continually review your local gaming climate to see what markets should be viewed as targets for increased spend as these markets may offer best potential for increased revenue.
There is plenty of data from which to build this market penetration analysis. We use the property’s rated database and contrast that to the amount of population within the trading range.
Generally speaking, market penetration is a measure of brand or category popularity. “The market penetration rate is the percentage of the relevant population that has purchased a given brand at least once in the time period under study.”
For the purposes of this article, market penetration is based on the number of people who have used the patron rating system and gamed at a facility in the recent year. Using geographical views, we segment those who reside within the zip code’s sub-sets and compare that to the results of the population within the same zip code sub-sets (a.k.a., trading range) from the property.
What we need to prepare the market penetration study is two-fold. First, we need the results by zip code for the property for the last twelve months. Second, we need population based on zip code. The first is simple and you likely have this information easily at your disposal. The second requires some research.
There are many zip code based demographic databases available to buy or lease, or you can simply look them up if you are not dealing with too many. These will have populations, ages, incomes and other information. Additionally, some will have the current-year data based on growth rates from the most recent census from 2010. If you do not have current-year data, you can fix that by impacting the last year’s data you have by proper growth rates. By keying on the same zip codes from your database to the demographics data, you can find out the penetration rates for the established markets for the property.
Once we assemble the results by zip code for the property for the last twelve months, we are able to study some of the tendencies.
For our hypothetical property, they had 95K patrons who made 705K trips and generated $35.7M for the year. When viewing by miles away from the property, patrons from 0 to 25 miles were 23% of the accounts, yet they generated 58% of the revenue. The 0 to 25 mile patrons generate their annual value based on their very high trip frequency of 18 annual trips, which is 3 times higher than the next highest group at 5 annual trips. Collectively, patrons from 0 to 75 miles generated 71% of the total revenue.
The tendency of those patrons living closer to the casino providing a disproportionately large share of revenue is similar in most regional gaming markets around the US. This reinforces one of the basic tenants of retail (and we consider casino gaming with similar attributes as retail), which is that customers closer to property will visit that property more than a property further distance away. This tenant is also a large portion of the basic logic for the process of building a gravity model, which we will cover in some of our next articles.
Now that we have the results of the patrons, we can compare their performance to the general population within their mileage bands to calculate the penetration rate. One thing we utilize when looking at penetration rate is population of 21+ year olds. This is reasonable and more focused than using the entire population as those less than 21 are considered ineligible to legally gamble in most states.
The following chart shows graphically the effect of proximity on customers. As mentioned earlier, as the distance increases from the property, the level of market penetration declines. We find this trait similar to most all properties in most regional gaming markets. The closer patrons reside to property, the more likely they will patronize property. The penetration results show that the areas within 50 miles are highly penetrated to the extent of little more expected penetration gain. As the areas are further from the property, there is less penetration offering some potential for exploring further to see if gains in penetration are feasible.
There are a variety of reasons for the property having a lesser penetration rate in the 2575 markets.
Some of these reasons could be: lower incomes and competition. Lower income would equate to lower disposable income to budget for gaming. Another factor could be competition intercepting traffic in the larger population areas within these markets while the competition is actually geographically pre-disposed to access the further distance 75+ mile areas for the property. The reason for the 75+ areas generating a higher portion of the revenue with a lower penetration rate appears to be due to the population being so much larger than the 25-75 areas.
In this article we discussed what data to assemble to perform a simple penetration analysis. Penetration analysis is useful for determining if there are select areas that may be able to offer growth for the property and for those areas in which maintaining the existing rates of penetration are important as they provide significant revenue streams to property. Unfortunately, using penetration analysis alone will not suffice. The results of the penetration analysis will point the analyst in a direction to dig for more sufficient data to possibly increase/decrease marketing spend based on the additional results research.
In analytics, this property was established by William C. Reilly in 1931, as he developed Reilly’s law of retail gravitation.
“In its simplest form, it assumes two market centers (towns, shopping malls, etc.), which will divide the market between themselves. In this simplified schema, customers will shop only at one center or the other (or, what amounts to the same thing, any crossover from one market will be exactly offset by crossovers from the other).”
Reilly’s Law is relied upon heavily to build gravity models and we will examine its implications in our next articles.
1 http://en.wikipedia.org/wiki/Market_penetration, Farris, Paul W.; Neil T. Bendle; Phillip E. Pfeifer; David J. Reibstein (2010). Marketing Metrics: The Definitive Guide to Measuring Marketing Performance. Upper Saddle River, New Jersey: Pearson Education, Inc.
2 Amal Datta, “Reilly’s law of retail gravitation”, https://www.scribd.com/doc/70608682/Reilly-slaw-of-retail-gravitation. Accessed 6/15/2015
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 and welcomes your comments and questions.