Gaming

Casino Games

Machine learning models can use historical players’ data to make different types of predictions. Casinos can gain insights such as how likely a player is to play a game, or how much time and money they may spend on the game. These insights will help casinos create better marketing strategies and promotion programs, as well as can help create a lifetime worth to identify valuable customers. Examples of different variables used to train the model include date, special offers, game type, and more.

Cheating Opportunities

Machine learning models can use data from games to understand unusual patterns. Unusual patterns can allow security teams to investigate suspicious user behavior, and prevent future cheating opportunities.

Demand for Puchasing Decisions

Machine learning can build predictive models to forecast purchasing decisions. For example, a trained model can forecast how much staff is required on a given day and time, or how much food and beverages must be purchased. Accurate predictions for supply to fulfill the demand based on the day and time can help businesses optimize their purchasing strategy.

Gaming Features

Machine learning can help businesses understand how satisfied customers are with changes made to games. For example, if a current gaming strategy is changed it may impact a customer’s engagement. Machine learning models can utilize data variables such as customer’s time spent on a game, and gaming features utilized to better understand the customer’s behavior pattern..