Which targeted interventions most effectively reduce early membership churn in a large fitness franchise?
Process
AI-driven big data analytics:
• We analyzed membership data and identify the 10% of at-risk members most likely to stay with targeted interventions. This predictive analysis helped pinpoint key intervention points and risk scores.
Ethnography:
• Next, we conducted ethnographic research to gain deep insights into the behaviors and motivations of these members. This qualitative research informed the development of 2-3 highly impactful retention strategies tailored to the specific needs of the identified members.
Results
By focusing on the 10% of members most likely to be retained, the company implemented 2-3 targeted intervention strategies that sustainably reduced churn by 2-5%. This approach led to a significant improvement in EBITDA, adding millions to the company’s bottom line. The targeted strategy not only addressed immediate churn issues but also ensured long-term member loyalty and financial stability.
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