The gym had 340 members and had been at roughly that number for two years. Revenue was flat — $47,000 one month, $49,000 the next, $46,000 the one after. The owner called it stable. The business was not stable. It was a treadmill.
Related retention guide: Turn the ideas in this article into a repeatable staff-review workflow with identify at-risk members before revenue falls, built around observed return cadence, historical context, privacy-safe cohort comparison, and auditable internal recovery work.
Evidence note: Current first-party pages document SMS and email auto-nudges, churn-risk scoring, and dunning and payment-retry workflows. The page-specific scope statement conflicts with current product pages; documentation is vendor-authored and plan-specific. Date the scope, distinguish Pulse from Mako CRM, and state only verified plan features. Sources: Mako first-party: Mako Pulse: Member Retention; Mako first-party: Mako Automated Dunning for Fitness Studios.
When we pulled the actual cohort data, the picture was different. The gym was losing 8–10 members a month and gaining 8–10 members a month. Net member count: flat. The owner had no visibility into this because her software showed her the balance, not the movement. She could see that she had 340 members. She could not see that she was replacing 100 of them every year.
Evidence note: The displayed arithmetic is 110 times $1,680 minus 60 times $1,680 equaling $84,000. Case records, replacement tenure, and gross-versus-contribution basis are unverified. Label it a modeled case and show cohorts, tenure, and cost assumptions. Source status: No primary source was found for the earlier universal claim as of 2026-09-01.
She knew her monthly revenue. She did not know her annual churn rate, her member LTV, or how long the average member actually stayed. Her software — a mid-tier platform she'd been on for three years — showed her booking counts and monthly revenue totals. It did not show her the cohort breakdown that would have made this visible.
The number that changed her thinking wasn't the $84,000. It was the LTV chart.
Evidence note: Publish an anonymized cohort table with denominators and confidence intervals. Sample, period, churn definition, offer assignment, and underlying cohort records are unavailable. Source status: No primary source was found for the earlier universal claim as of 2026-09-01.
Three operational changes came from this analysis, none of which required new staff or major program changes.
Evidence note: Label this as an unverified case observation and run a controlled local test. No case records, control, intervention fidelity, attribution, or cohort definitions are provided. Source status: No primary source was found for the earlier universal claim as of 2026-09-01.
Evidence note: Report dated pre/post cohorts and contribution margin. Seasonality, revenue definition, member flow, concurrent changes, and records are unverified. Source status: No primary source was found for the earlier universal claim as of 2026-09-01.
None of this required a marketing budget increase. It required visibility into what was already happening — and a system that surfaced at-risk members automatically rather than requiring the owner to manually scan a spreadsheet every week for attendance drops she might catch or might miss.
The gym wasn't doing anything dramatically wrong. The owner was competent, her coaches were good, the facility was clean and well-run. The problem was informational: she had no way to see churn as a rate, members as cohorts, or LTV as a measurement. Her software gave her a snapshot — how many members today, how much revenue this month. She needed a film, not a photograph.
Most gym owners are in the same position. They can tell you how many members they have right now. Very few can tell you what their average member LTV is, what their 6-month retention rate is by cohort, or what the difference in churn rate is between members who were acquired through an intro offer vs. members who signed up at full price. That information exists in the data. Getting to it requires software that's built to show it.
The $84,000 gap wasn't created in one bad month. It was the accumulated cost of making decisions without the numbers that would have changed them.
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