In our last article, we started to denote the process for building mROI. We discussed how to evaluate your mROI (marketing ROI for every $1 of free play). For some years now, we have been advocating the use of mROI of free play to evaluate patron direct mail rewards. The biggest reason for this is that free play (also known as promotional credits) 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, they found that in 2015 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.

Our goal of the mROI analysis is to monitor overall mROI, reduce the mROI to reasonable levels where applicable which is accomplished by raising offers and stopping/reducing spending where the mROI is at or below the minimum threshold. Lowering mROI for those segments at or above an mROI of $5:$1 is targeted to increase offer values to induce more market share. It is a calculated best risk as these patrons are demonstrating they give back much more for every $1 you spend, so while lowering their current mROI, you should acquire a higher level of their revenue. Additionally, higher offers may just be enough to entice other patrons in the same high mROI segments to revisit your property.

Our last article covered three of the five steps involved in the process to get the mROI analysis completed which are:

  • Gather data from your player tracking data base 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.

As a reminder, 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 redeemed. Essentially, it shows the net budget that patrons are willing to put into play. In the case of our example database from the previous article, and in the following table, when expressed with per trip metrics, the $1,000/ADT+ generated $1,481 win per visit and redeemed $54 each visit in free play. This would make their free play reinvestment 4% and their mROI of $27:$1. On the opposite end of the spectrum, segments below $50/ADT all generate a free play reinvestment of 23% or more and average an mROI of $3:$1 or less. 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. Back to our results and discussion 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.

Database marketing is surgical, so let’s go get our knives out and see where we can make some incisions. The last two of the five steps involved in the process to get the mROI analysis completed are:

  • Compare the current reinvestment and mROI to the target reinvestment values
  • Determine where to elevate or lower reinvestment

We have prepared numerous tests for clients in multiple gaming jurisdictions, including FL, MS, NJ, NV, NY and PA, and the return on investment when using promotional coin is often an average of between $3:$1 and $4:$1 with $5:$1 or better not uncommon especially when viewing various segments.

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 and welcomes your comments and questions.


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