USING mROI TO DRIVE EFFICIENCY IN MARKETING PROGRAMS
For some years now, we have been advocating the use of ROI of free play to evaluate patron direct mail
rewards. The biggest reason for this is that free play has become the biggest marketing player expense
by double or triple, as compared to comps and cashback programs. Along that line and from key observations with our regional casino clients in 2015, they found that while growing overall revenue, in many cases their middle market patrons (those with $50-$300 Average Daily Theoretical (“ADT”) were not keeping pace. They felt this was largely due to the competition eroding loyalty in that these customers are prized on the competition’s player database, as well.
The competition remark is important because in most, if not all, regional jurisdictions, free play can account for upwards of 75% or more the total reinvestment value in players. We know from experience that we can utilize better reward mechanisms for free play to move the revenue needle. With that
said, how can properties gain back loyalty in their middle market?
It has become apparent to us that the traditional method of evaluating play to generate levels of rewards using ADT is insufficient. Using solely ADT is an old method that does not consider modern technology and the ability to track free play costs to the player level. The good news is that there are a couple of
methods to increase your free play efficiency. The first is to use Net ADT, which deducts the free play (also called promotional credits) from the gross win reported in most player tracking systems. The second method, and the one we will focus on, involves using Marketing’s Return on Investment (“mROI”). The process for mROI will be detailed in the balance of this and its follow-up article.
What does mROI do? It reviews each individual patron to evaluate their use of free play against the revenue they return. From there, offers can be evaluated to see where too much and, more importantly, where too little free play is being spent. Essentially, it shows the net budget that patrons are willing to
put into play. Until player-tracking systems started carrying the exact values of the redeemed value of free play, this process was,at best, challenging to build. While not the simplest data set to gather, it is available for those wanting to utilize it as part of their marketing analysis.
The steps involved in the process to get the mROI analysis completed are:
- Gather data from your player-tracking database for the past year, covering accounts, trips, casino win (we normally use theoretical win) and free play redeemed.
- Build analysis of your segments showing the win and redemption of free play.
- Calculate mROI and profit before other player expenses.
- Compare the current reinvestment and mROI to the target reinvestment values.
- Determine where to elevate or lower reinvestment.
Building the mROI segmentation, we generate results that look remarkably familiar to a normal segmentation. Where this segmentation differs is the additional metric of free play. As we view the results of our sample database in the following table, some segments have significantly high mROI values. The $1,000/ADT+ segment is generating an mROI of $27. This means that for every $1 dollar of free play this segment redeems, it generates $27 in casino win. Using the same metric, we can see that this database has values above $5 to $1 for every segment that is $50/ADT. Also, the database shows some segments with values below $3:$1. We have found that as a rule, segments generating lower than $3:$1 ROI should be considered below the minimum acceptable value and reviewed for cost containment initiatives as the reward is not worth the risk of not gaining more share of higher segments.
The database’s average is $7:$1 however, when looking into the segments, there are segments much lower than the $3:$1 mROI.
Another twist is that in our experience, mROI above $5, while very efficient, actually means that there is the potential to access more of your higher wallet patrons by increasing free play offers. We have discovered that the higher mROI is indicative of lesser redemption in free play and suggests these patrons simply do not perceive enough value in their offers to make trips to play.
Speaking of trips, the analysis needs to be viewed on a per trip basis to make simpler comparisons to our mailing/promotional strategies. From there we can start to evaluate against our existing offer structure and actual redemptions to see what gaps exist that we can change.
In the case of our example database, when expressed with per trip metrics, the $1,000/ADT+ generates $1,481 win per visit and redeems $54 each visit in free play. This would make their free play reinvestment 4%. On the opposite end of the spectrum, segments below $50/ADT all generate a free play reinvestment of 23% or more. This shows the upside down nature of the redemptions for this database.
While this is a fairly common occurrence on the first evaluation of mROI, it does not mean that the management should find this acceptable. At this point, investigating where the lower segment patrons are receiving free play is warranted and revisiting the whole process of “who gets what free play offer” needs to start. So far, this article has covered:
- Gathering data from player tracking to prepare a segmentation of your database for the past year, covering accounts, trips, casino win (we normally use theoretical win) and free play redeemed.
- Building analysis of your segments showing the win and use of free play.
- Taking the analysis above and calculate mROI and profit before other player expenses.
For our next article, we will go into detail about how to finalize the mROI analysis and show examples of comparing the current reinvestment and mROI to the target reinvestment values; and determining where to elevate or lower reinvestment.
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