Challenges
A leading sports club faced significant fluctuations in its season ticket renewals, ranging from 6,000 to 11,000 members annually. With churn rates as high as 30%, the club risked losing millions in recurring revenue. The team needed a data-driven approach to predict which fans were likely to lapse, understand the underlying causes, and intervene early to improve retention.
Solutions
The solution involved building a Predictive Modeling Framework to anticipate member churn and improve renewal outcomes.
- Data Collection and Preparation
- Consolidated data from CRM, ticketing, demographics, member behavior, and team performance systems.
- Conducted ETL processes to clean and unify disparate datasets for modeling.
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Predictive Engine Development
- Developed a Renewal Likelihood Model using Logistic Regression, Random Forest, and Gradient Boosting techniques.
- Combined these through an ensemble model, achieving over 84% accuracy.
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Insight Delivery
- Built dashboards & weekly reports segmenting members by risk tiers, identified key churn drivers, tracked year-over-year trends, and aligned insights with marketing actions.
Impact
- 34% drop in churn in high-risk segments
- ~$1.7 million in additional ticket revenue
- 6.4x ROI on the initiative
About Us
At Straive, we operationalize data analytics and AI for global enterprises. We leverage our unique people-process-tech framework to build the best-of-breed data analytics & AI solutions. By operationalizing this solution into your core workflow, we deliver real-world measurable impact and better ROIs through a combination of higher efficiency, elevated experiences, and enhanced revenues.
For more information about our services and how we can help you operationalize data analytics and AI, please visit our website: www.straive.com contact us at contact@straive.com.
