Patent ID: 11928921
Assignee: GAMING ANALYTICS INC.
Field: Control (Instruments)
Classification: CPC G | IPC G

Claim 10:
11. A method, comprising:
receiving, by a gaming machine analytics computing system, player data for each of a plurality of players registered with a casino;
aggregating and storing, by the gaming machine analytics computing system, the player data for each of the plurality of players;
determining, by the gaming machine analytics computing system, a player status classification for each of the plurality of players, wherein the player status classification is based on past and current casino visits;
determining, by the gaming machine analytics computing system, a churn status for each of the plurality of players, wherein the churn status is based on a visit pattern for each of the plurality of players, wherein the visit pattern is based on a number of visits to the casino and an amount of time between each of the visits;
training a model with a training data set comprising labeled data;
subsequent to determining the churn status for each of the plurality of players, predicting, by the gaming machine analytics computing system, a risk of churning for each of the plurality of players based on the model trained with the training data set comprising labeled data;
determining, by the gaming machine analytics computing system, a lifetime value prediction for each of the plurality of players using a neural network model that considers casino visit patterns and player data, wherein the lifetime value prediction predicts a player frequency, value at risk (VAR) and net win value for a period of time for each of the plurality of players; and
displaying, by the gaming machine analytics computing system, on an interface, a recommendation for a casino promotion, wherein the recommendation is based on a goal definition received by a user, wherein the recommendation for the casino promotion is based on past promotion performance metrics, wherein the recommendation for the casino promotion is based on the determination of player status classifications, risks of churning, and lifetime value predictions.