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ORIGINAL RESEARCH article

Front. Sports Act. Living
Sec. Sports Management, Marketing, and Economics
Volume 6 - 2024 | doi: 10.3389/fspor.2024.1362489
This article is part of the Research Topic Emerging Digital Technologies as a Game Changer in the Sport Industry View all 5 articles

Clustering Analysis of Football Fans using Customer Lifetime Value and Unsupervised Learning Techniques in the Football Industry

Provisionally accepted
Karim Chouaten Karim Chouaten 1,2*Cristian Rodriguez Rivero Cristian Rodriguez Rivero 3Frank Nack Frank Nack 1Max Reckers Max Reckers 2
  • 1 University of Amsterdam, Amsterdam, Netherlands
  • 2 AFC Ajax N.V., Amsterdam, Netherlands
  • 3 Cardiff Metropolitan University, Cardiff, United Kingdom

The final, formatted version of the article will be published soon.

    Implementing data-driven decision-making to strengthen a football club's commercial competitive position, especially against rival clubs, is increasingly important in today's competitive environment. Allocating resources to attract and retain profitable, loyal fans is key to advancing a club's marketing strategy. The commonly used Recency, Frequency, and Monetary (RFM) technique is applied across various industries to predict customer behavior, but its application in football is under-researched. This study addresses this gap by proposing a weighted RFM model, where the relative importance of RFM variables is determined using the Analytic Hierarchy Process (AHP) method. Unsupervised learning techniques are used to cluster fans based on their weighted RFM values, applying a simple weighted sum approach to estimate Customer Lifetime Value (CLV) and identify fan segments. The dataset includes 500,591 merchandising transactions, with RFM weights of 0.409 for Monetary, 0.343 for Frequency, and 0.248 for Recency. Six clusters were identified, sorted by rank, and labeled accordingly. Clusters 1 and 2, termed "Golden Fans," are characterized by high recency, frequency, and monetary value, making them crucial contributors to the club's profitability. Efforts should focus on maintaining their loyalty through special services or loyalty programs. Cluster 3, labeled "Promising," has the potential to become Golden Fans but needs to increase spending; targeted marketing and incentives can encourage this. Cluster 4, "Needs Attention," consists of former loyal fans who have decreased their engagement; strategies to re-engage them are necessary to prevent churn. Clusters 5 and 6 represent "New Fans" who show potential for growth with increased engagement and personalized offerings. Lastly, clusters 7 and 8, termed "Churned/Low Value," contribute minimally and require price incentives to re-engage, albeit with lower relative importance compared to other segments. The approach's usefulness is verified using four viewpoints and has been applied to Amsterdamsche Football Club (AFC Ajax) using Customer Relationship Management (CRM) data. This study provides actionable insights and a useful method for clustering analysis in the football industry. The findings may assist marketing practitioners in enhancing their marketing activities by implementing data-driven decision-making for effective and efficient market segmentation.

    Keywords: Football, Clustering analysis, machine learning, Customer segmentation, customer lifetime value, RFM model

    Received: 28 Dec 2023; Accepted: 28 Jun 2024.

    Copyright: © 2024 Chouaten, Rodriguez Rivero, Nack and Reckers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Karim Chouaten, University of Amsterdam, Amsterdam, Netherlands

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.