Gulfstream Park Casino in South Florida will reopen on June 18 with northern California-based Gaming Analytics.AI and its artificial-intelligence tools to improve decision making, marketing and profitability.
“We were looking for a product that would allow us to more efficiently and effectively analyze the slot floor, improve marketing and more accurately forecast revenue,” said Gulfstream Casino Manager Dana Leibovitz. “GA’s analytics capabilities, along with their prediction tools, were a perfect fit.”
“GA is so easy to use and blazing fast, what used to take hours and days to get information now takes seconds,” said Lisa Siples, Director of Slot Operations at Gulfstream.
With a casino located on two levels, conventional tools made it difficult to analyze the complex relationships and the differences between the players and products on each level. Gaming Analytics’ technology analyzes volumes of complex data to recognize hidden patterns. This information provides daily recommendations about machines, visitation patterns, marketing techniques and spend levels.
For Gulfstream Park’s marketing team, the software provides predictions of future player churn, giving them the ability to increase retention. Trends and predictions will help them identify the right player segments to target for their future marketing campaigns and with a value-at-risk metric that will give them the confidence that they are allocating their marketing budget strategically.
Gaming Analytics.AI CEO Kiran Brahmandam said that artificial intelligence and machine learning have helped them develop a product that automatically gets better as customers use the application.
“Our recommendations are based on an extensive analysis of existing data from the customers slot and guest tracking systems, along with any other software systems available in their data warehouse. Once strategies are implemented, the system does analytics against the resulting recommendations to adjust methodologies, if necessary, for future improvements.”
The learning curve is minimal; users can simply query GA with Google-like questions: “Which players are at risk this month?” or “What is the ideal denomination for this type of machine?” It’s the same process used by GA.
