The gaming industry is being modernized through a collection of computational tools and methods falling under the umbrella of “data science.” The usage of “data science” has grown to include almost all forms of analytics, forecasting, optimization, and even artificial intelligence. The main purpose of this effort is to maximize long-term profits through optimizing all the smaller decisions a hotel and casino operator make, ensuring the player has a great experience and continues to spend their time and money with that establishment.
Operators have long understood the value of customer relationships and are using data scientists to be even more precise. Operators know that making accommodations for high rollers can pay off and that a well-timed comp for a casual player can keep them returning again and again, and the operators’ data scientists figure out how to size the rewards optimally. Data science is not limited to loyalty programs though. Slot machine designers are using data science to finely tune game math designs to keep players gaming longer, offering just the right win frequency and spin timings to entice longer sessions. Hotel operators are using data scientists to figure out how much gaming activity on the floor is necessary in order to offset the cost of a free room and which players are most likely to do it. The same mathematics for recommending a movie on Netflix can be used for recommending where to eat in a casino with multiple dining options. A sophisticated recommendation algorithm can even send a promotion based on other known affiliates of the customer. For example, if the system detects both the patron and their spouse on the property, it might trigger a discount for a romantic dinner experience versus a discount at a sports bar for a group of friends. There is no limit to the careful application of mathematics in the gaming industry, and the most sophisticated operations are well on their way to achieving extremely optimized operations. Is there a way for smaller operations, without the budget for a huge team of scientists and software engineers, to take advantage of these techniques? Absolutely. To understand the time and cost for data science projects, operators should break down the project into development phases and estimate the requirements for each phase.