One of the largest fitness franchises in the world faced a high churn rate, with 40-60% of members leaving within three months of joining. Despite identifying 15 different potential strategies to reduce churn, the company struggled to determine which interventions would be most effective. Implementing all 15 strategies simultaneously was impractical, and there was a need to focus efforts on the members most likely to stay if targeted interventions were applied.
Process
We employed a two-pronged approach combining AI-driven big data analytics and ethnographic research. First, we used AI to analyze 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. 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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