Beyond Lifetime Value: A Customer Journey Analysis to
Fan Engagement and Spending in Professional Sports
Name
antonakakis-cea14-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
3.57 MB
Format
Adobe PDF
Checksum (MD5)
f37c8ddb5cc3004c8abaf9fef790e866
Author(s)
Antonakakis, Christina Elizabeth
Advisor(s)
Chase, Christina
Date Issued
February 2025
Publisher
Massachusetts Institute of Technology
Abstract
This research examines engagement and spending behaviors in a professional sports ecosystem, introducing Customer Journey Analysis (CJA) as a dynamic alternative to traditional customer lifetime value (LTV) models. Through an analysis of over 930,000 net new fans acquired from July 1, 2021, to June 30, 2024, this study identifies critical patterns in acquisition channels, spending behaviors, and engagement metrics over multiple seasons. Notably, the findings highlight the significant influence of early touchpoints, such as ticket purchases and email interactions, on fan progression. Metrics like email open rates and multi-channel engagement emerge as strong predictors of future spending, revealing nuanced insights into fan behavior. This research emphasizes the importance of integrating behavioral and financial metrics to sustain fan involvement. By transitioning from static LTV models to a multi-dimensional CJA framework, actionable strategies are proposed for optimizing engagement channels, improving retention, and driving long-term revenue growth. Key findings reveal that predictive modeling and customer segmentation analysis are instrumental in identifying high-potential fans and distinct audience profiles. Tailored retention strategies, including personalized follow-ups and exclusive engagement incentives, address churn risks while fostering ramp-up and loyalty across diverse fan groups. Future work should explore tenured fan behaviors and incorporate diverse data sources, such as in-venue spending and team performance metrics, to deepen understanding of fan evolution across different lifecycle stages.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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