One Casino’s Journey to Optimizing its revenue

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Unleashing the power of VizExplorer’s Slot Recommendation Engine (SRE), a Pacific Northwest-based casino optimized its casino floor through recommended game changes that saved the casino time and money while increasing gaming revenue.


🤔 What was the challenge?                        

Wanting to make better decisions based on all the available data, the casino looked for an automated solution to analyze slot performance and player data and provide recommendations to identify new revenue generating opportunities on the casino floor.


❓ Why did they choose SRE ? 

Slot Recommendation Engine enables the team to focus on more value-add tasks through data-driven recommendations that save time and money. Customizable rules and filters provide the flexibility for the casino to apply its specific analytical requirements to uncover new revenue generating recommendations.


🛠️ How did SRE help solve the problem?

After selecting VizExplorer’s SlotRecommendation Engine, the slot team put the system to work, analyzing performance metrics, player data and preference play to make high value recommendations. The slot team quickly developed trust in the tool and was able to find new opportunities that quantified net lift to guide the team towards the most impactful changes.


📈 Results

+235% average project lift             

17x return on monthly subscription 

✅ Approximately 14 hours saved per slot project 


” It was great to see that not only were the recommended changes making more money but also that I was filling a need for my players.”  Director of Slot Operations 


🎰 Background

A Pacific Northwest-based casino with more than 1,500 slot machines was making hundreds of game changes each year, but the process was time-consuming and resource-intensive. As a result, it was nearly impossible for the gaming operations team to look at all the relevant data, meaning they often made decisions without considering player-related data and preferences. This approach meant the casino was missing key, but lesser-known, revenue opportunities.  




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