[ Case study ]
Lapsed members — people who had already loved the studio — received nothing after cancelling except a newsletter. Win-back relied on the owner remembering faces, and no record existed of who had lapsed and why.
CLIENT a boutique fitness studio group — FOCUS Segment lapses by reason, not by date
Representative examplesEvery case study in this library is an illustrative composite of the kind of engagement we deliver — written to show our method and standards, not to name clients.
Three neighborhood locations, coaches who know members by name, and a monthly membership — this boutique fitness studio group runs on personal relationships, and it loses members the way every studio does: people move, get injured, change jobs. Member records lived in a booking system the front desk used for schedules, with cancellations marked by date and little else. The owner knew lapsed members personally and win-back happened when he happened to see someone around the neighborhood. A monthly newsletter went to everyone, including people two years gone.
Lapsed members — people who had already loved the studio — received nothing after cancelling except a newsletter. Win-back relied on the owner remembering faces, and no record existed of who had lapsed and why.
Segment lapses by reason, not by date: the pipeline tags each lapsed member by departure reason drawn from exit notes, so every sequence speaks to the actual gap — schedule, injury, or price — instead of a generic 'we miss you'. A lapsed-member page with a two-week reactivation offer routes bookings straight into each location's real class calendar, with the front desk watching every step. Sequences are drafted from the owner's actual messages, so automation handles when a message goes, never what it says.
Just as important is what we ruled out, and why:
The pipeline tagged lapsed members by departure reason from exit notes, so each sequence speaks to the actual gap — schedule, injury, or price.
A lapsed-member page with a two-week reactivation offer routed bookings straight into the calendar, with the front desk seeing every step.
Sequences were drafted from the owner's actual messages, with automation handling when — never what.
Delivered by the growth pod — strategist + automation specialist over 4 weeks, with working increments reviewed with the client every week.
Obstacle
The exit-reason tags were only half populated — months of cancellations had been marked with whatever the desk had time to type at the time.
Handled: We ran a one-week tagging sprint over the old records with the front desk, then rebuilt the sequences to handle an 'unknown' reason gracefully instead of assuming.
Obstacle
The first full sequence read as broadcast — three scripted sends arriving on schedule whether or not the member had answered the message before.
Handled: We re-cut each message from the short voice notes he already recorded to himself between classes, then added a reply-pause to every step; the sequence stays his because the recording habit is ongoing, not because copy gets approved once.
Obstacle
The booking system's schedule did not expose holiday closures, and the first week's rebooked slots included a class that had been cancelled.
Handled: We added a closure-date block list the desk maintains monthly, and the calendar check now runs before each sequence's booking link goes live.
The headline: lapsed members rebooked within the first 60 days of the funnel — 118, read from Pipeline reactivation stage. A second check: reactivation rate across the full lapsed list at 9.1%.
The studio got some of its people back — not as a campaign, but as conversations the desk was finally equipped to have. Coaches noticed returning faces they had wondered about for months. The owner stopped carrying the win-back problem in his head; the list remembers now, and it remembers why each person left. The newsletter went back to being news for current members, and the front desk describes the Tuesday list as the easiest part of their week — ten names, one message each, done before the first class.
The result was read from Pipeline reactivation stage against the pre-engagement baseline over the stated window, with a guardrail check on reactivation rate across the full lapsed list. Where platform-reported numbers and business outcomes differ, this record says which layer it is quoting.
What we would do differently
We would have cleaned the exit-reason tags before building sequences — the first week of sends proved the tags were only half populated.
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