Objectives.  A national health club franchise wanted to better understand the underlying factors that drive attrition and identify ways to increase its membership retention level.  The main objective of this study was to identify and evaluate the psychographic and demographic profile of members who leave the Club in order to provide a more comprehensive picture of key drivers for membership attrition.  Secondarily, the study would assess member satisfaction to gauge how the Club performed on the attributes members valued most.

Challenges & Methodologies.  Although attrition was cited as the primary concern for the Club, its exit interview process was not structured to capture adequate information about what impels different individuals to terminate their memberships. One challenge was that the membership prices and amenities varied dramatically by facility, therefore member expectations and satisfaction varied within the franchise.

Primary research was conducted to assess the reasons for Club membership termination while completing an initial analysis of potential remedies for attrition. From this a demographic and psychographic composite of members was generated by club location, and analyzed to determine what relationships existed between member satisfaction of Club attributes and the composite characteristics, which attributes members valued most, and how the Club was performing on those attributes. Quantitative techniques were employed, including factor and cluster analyses, to create actionable customer segments for the Club to address. 

Outcomes.  A final report outlined the impacts to membership attrition from implementing changes. Several meetings with the Club executive team and marketing groups were held to discuss study findings and suggested program development.

Based on our analysis, we identified the primary drivers of member dissatisfaction and attrition by member group and recommended programs to drive usage and improve communication programs. Methods of measuring ongoing member satisfaction levels and ways to address the concerns of the distinct, dissatisfied sub-groups were generated. The information could later be used in predictive analysis for the entire Club population to gauge which members had highest probability of termination and to proactively prevent members from leaving.