Data-driven fitness member segmentation: One size does not fit all

Have you ever been in the middle of an early morning fitness class and thought “I know why I’m here – I’ve got to stay in shape all winter for outdoor cycling season—but what gets everyone else to show up for this particular 6am spinning class?” Is it because the instructor has the best playlist? Is it because they live 2 blocks from the gym? Or, is it just a part of their daily routine?

Beyond mere curiosity, the answers to these types of questions are crucial for fitness centers to understand what brings new members in the door—and what keeps them coming back week after week. Effective marketing campaigns for fitness providers depend on clear and actionable data about its members. And although the reasons members provide for going to the gym are qualitative, a data driven assessment can also help tell the story of their behavior to craft a better customer experience strategy.

West Monroe was recently engaged by a fitness provider who struggled to generate new-member sign-ups and to maintain a steady utilization of the facilities among existing members. The goal of the engagement was to analyze member data to identify a set of typical member types and understand their differing behaviors and key characteristics. By uncovering specific member types, we were able to equip the fitness provider with a strategy to design solutions aimed at the needs and behaviors of their current members.

Conducting a member segmentation analysis is an iterative process focused on understanding data and translating results into member types, which will later be used to create detailed member personas. The following outlines the primary best practices in this project:

• After analyzing the transactional data related to sign-ups and usage, we conducted several sessions to understand how initial gym engagement was measured and how to best leverage the client’s data to model member behavior.
• After defining how the available transactional data could model the business problem, we reviewed all additional attributes: geographic location, age, gender, proximity to other gyms, seasonality of visits, etc.
• The member segmentation algorithm identified groups of members based on their engagement with the gym, ranging from members who visited daily to those who went once and never returned.
• In interpreting results, we tied it all back together: What do we know about the people who go daily? Or the people who visit once and never come back? We began to formulate different stories about each of these member types based on the data— for example how they engage with the gym and what defines them compared to other groups.

The takeaways from the member segmentation analysis revealed several groups of members with distinct behaviors and character traits. These segmentation results then served as the input for our Customer Experience team to elaborate on the data-driven findings through qualitative and quantitative customer research to create member personas. Our Customer Experience team led “Engagement Sprint” workshops for each persona which resulted in ideas on how to improve the member experience and increase participation through intervention pilots. The ideas are scheduled to be piloted at the gym later this year.

This is one example of how a data-driven member segmentation analysis can provide deep insights for our clients. Beyond the health and fitness industry, West Monroe has conducted transformational, analytic assessments of customer behaviors across industries to enable clients to understand behaviors and needs of their customers, as well as how to better serve and retain them.