US 11,928,921 B1
Machine-learning platform for marketing promotions decision making
Kiran Brahmandam, San Rafael, CA (US); Krishnan Srinivasan, Chennai (IN); and Boyue Shen, Albany, CA (US)
Assigned to Gaming Analytics Inc., Novato, CA (US)
Filed by Gaming Analytics Inc., Novato, CA (US)
Filed on May 18, 2020, as Appl. No. 16/876,320.
Application 16/876,320 is a continuation in part of application No. 16/338,012, filed on Mar. 29, 2019, abandoned.
Application 16/338,012 is a continuation of application No. 16/028,865, filed on Jul. 6, 2018, granted, now 10,311,670, issued on Jun. 4, 2019.
Claims priority of provisional application 62/849,186, filed on May 17, 2019.
Claims priority of provisional application 62/530,131, filed on Jul. 8, 2017.
Int. Cl. G07F 17/32 (2006.01)
CPC G07F 17/3234 (2013.01) [G07F 17/32 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer system including a processor and a memory, the memory containing software instructions configuring the system to perform acts including:
receive player data for each of a plurality of players registered with a casino, wherein the player data is recorded and monitored periodically or in real-time;
aggregate and store the player data for each of the plurality of players;
determine a player status classification for each of the plurality of players, wherein the player status classification is based on past and current casino visits;
determine 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, gaming activity at the casino, and an amount of time between each of the visits;
train a model with a training data set comprising labeled data;
subsequent to determining the churn status for each of the plurality of players, predicting a risk of churning for each of the plurality of players based on the model trained with the training data set comprising labeled data;
determine 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
cause to be displayed 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, lifetime value predictions, and any of historical player data and real-time player data.