Supervisor
David Gonzalez
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
Churn rates are remarkably high in the gambling industry, an extremely competitive landscape coupled with a severe lack of brand loyalty among its customer base makes churn prediction one of the main problems an operator will face. This paper explores the range of possible modelling solutions with a key emphasis on ensemble learning to improve on existing methods. During this exploration, a host of modelling techniques are formulated with a focus on scalability facilitated by Apache Spark distributed computing language. Thirteen variations of models, including single classifiers and ensemble families are evaluated as to their suitability in solving the problem. The limitation of the data set provided is that it is not diverse enough to encapsulate the true dynamism relationship between the customer and an operator. Nevertheless, the paper can provide a host of solutions that satisfy the goal of scalability. The project provides three separate recommendations that are flexible based on the firm needs recording ensemble classification precision scores of 86%, 87% and 95% respectively. The author proves that ensemble learning is a stronger predictive solution in the context of churn prediction in the gambling industry. In addition to demonstrating the power of ensemble learning the paper provides an application based on the author’s strongest modelling approach that is applied on a unseen validation set. The output of the application returns a list of customer account numbers who are predicted churners that internal CRM teams can use to improve processes.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Corcoran, P. (2025) Assemble the ensemble: A multi model approach for customer churn prediction in the gambling industry. CCT College Dublin.