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Unlocking high-value football fans: unsupervised machine learning for customer segmentation and lifetime value
Karim Chouaten1,2, Cristian Rodriguez Rivero3,4, Frank Nack1
1Faculty of Science, University of Amsterdam, Amsterdam, Netherlands.
Frontiers in Sports and Active Living
|September 9, 2024
Summary
This study introduces a weighted Recency, Frequency, and Monetary (RFM) model using the Analytic Hierarchy Process (AHP) and machine learning to segment football fans by Customer Lifetime Value (CLV). It identifies key fan segments for targeted marketing and enhanced profitability.
Area of Science:
- Sports Marketing
- Customer Relationship Management (CRM)
- Data Analytics
Background:
- Football clubs increasingly use data for commercial advantage and fan retention.
- Customer segmentation is vital for marketing, but RFM application in football is limited.
- Identifying and retaining high-value fans is crucial for profitability.
Purpose of the Study:
- To address the gap in applying RFM analysis to football fan segmentation.
- To develop an enhanced RFM model incorporating AHP and machine learning for Customer Lifetime Value (CLV) segmentation.
- To provide actionable insights for football clubs' marketing strategies.
Main Methods:
- Employed a novel weighted Recency, Frequency, and Monetary (RFM) approach.
- Quantified RFM component significance using the Analytic Hierarchy Process (AHP).
- Utilized unsupervised machine learning for fan segmentation based on weighted RFM values and estimated Customer Lifetime Value (CLV).
Main Results:
- Identified eight distinct fan clusters, including 'Golden Fans' (high value) and 'Promising' segments.
- Derived specific weights: Monetary (0.409), Frequency (0.343), and Recency (0.248).
- Highlighted the need for targeted strategies for different segments like 'Needs Attention', 'New Fans', and 'Churned/Low Value'.
Conclusions:
- The proposed weighted RFM-AHP-ML method effectively segments football fans for CRM.
- Actionable insights enable prioritization of high-value segments and tailored marketing strategies.
- The framework supports enhanced fan engagement, profitability, and long-term commercial success in football.
Keywords:
artificial intelligenceclustering analysiscustomer lifetime value, RFM modelcustomer segmentationfootballmachine learningMore Related Videos
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