This is the second case study in the Slot Recommendation Engine series from VizExplorer. The series follows the story of one casino’s journey to improve its casino floor. A new article will be released each month covering the results the casino has realized thus far, including improvements in slot performance (ROI), time savings and where the casino is a year later.
In the first post of this case study series, ‘Recommendations to Reality: Using Automated Analysis to Drive Slot Performance,’ we covered how one Pacific Northwest-based casino was looking for a new way to make more informed game change decisions. To enhance the effectiveness of the analysis process and drive better floor performance, the casino’s data analytics team decided to leverage VizExplorer’s Slot Recommendation Engine (SRE). And, after confirming that SRE worked as promised, the casino was ready to put SRE to the test and take advantage of the game change recommendations it provided.
While the casino’s data analytics team usually only had enough time to analyze the top and bottom 20 percent performing slot machines, SRE gave them additional insight into the 60 percent of slots in the middle and provided recommendations for which games to add, remove, reconfigure or move. These are games that would have otherwise been overlooked due to their performance but had the potential to increase returns.
For the first project, SRE recommended replacing one dozen units spanning across three different themes — each of which was performing under section and floor averages — with three themes that were currently outperforming section and floor averages in other areas of the casino.
End to end, the entire project took approximately one hour to identify, strategize and set into
motion, and then game changes were complete by the end of the week.
Had the casino’s data analytics team gone through the same process independently, it would have taken at least one month from analysis to implementation of the new games.
What further validated the team’s confidence in the tool was how detailed the recommendations were. The recommendations included comparisons across multiple KPI’s and against different benchmark averages. SRE didn’t just suggest which changes/moves to make with data but also estimated the expected lift. Even better, the changes made cost nothing because the recommendations were games that the casino already had available in its inventory.
One month after making the recommended game changes, the casino was already seeing the impact — the recommended changes not only met the expected lift outlined by SRE but far surpassed the projections.
After seeing the exceptional results of the first project return an average of +91% net lift, the casino is already looking at what it can achieve next with the Slot Recommendation Engine. The aggregated one-month total net lift of this single slot project provided an ROI that covers the monthly cost of SRE 17x over.
Now knowing the tool’s full capabilities, the casino plans to set more custom rules and parameters within SRE to deliver more targeted recommendations. The director of slot operations hopes that, in the future, they can simply export the recommendations and send them to the team for them to prioritize and execute. Doing so would give them more time for other value-add activities, including further developing the team and investigating other revenue streams.
Check out the next installment of the Recommendations to Reality case study series here.
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