Unlocking high-value football fans: unsupervised machine learning for customer segmentation and lifetime value.

ABSTRACT:

Introduction

In the modern competitive landscape of football, clubs are increasingly leveraging data-driven decision-making to strengthen their commercial positions, particularly against rival clubs. The strategic allocation of resources to attract and retain profitable fans who exhibit long-term loyalty is crucial for advancing a club's marketing efforts. While the Recency, Frequency, and Monetary (RFM) customer segmentation technique has seen widespread application in various industries for predicting customer behavior, its adoption within the football industry remains underexplored. This study aims to address this gap by introducing an adjusted RFM approach, enhanced with the Analytic Hierarchy Process (AHP) and unsupervised machine learning, to effectively segment football fans

SUBMITTER: Chouaten K 

PROVIDER: S-EPMC11378343 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature