On May 7, 2026, Planet Fitness beat revenue and earnings and lost a third of its value in a day. The number that broke was net member joins. Management called it attrition and could not explain it. Its members already had, in 9,186 reviews: the cause is nameable, rankable, and not where the money is going.
The company reported a strong quarter and still lost a third of its value in a day, on one number: net member joins. To find what actually drives members away, Dimension Labs read every one of the 9,186 Google reviews of the 33 Planet Fitness clubs in metro Chicago from January 2025 to July 2026, turned each into 28 structured measures of the member experience, layered in the company's public financial filings, and ran a formal causal analysis, the standard used in clinical and economic research, to separate what members complain about loudest from what actually makes them leave.
The complaint members raise most often is broken equipment, and it is the one the company's capital debate is about. It is also among the weakest causes on the two measures that matter. Two questions decide churn, and each has a different answer. What makes a member angry is the front desk, the largest cause of a one-or-two-star review. What makes a member leave is the billing: a member who hits a charge after cancelling, or cannot cancel at all, says they are gone half the time, thirty-six times the fleet baseline. Both sit at the front of the house. Neither is the equipment. The rating the company watches showed none of it, because for nine months it was a marketing output.
Planet Fitness beat consensus on revenue and on earnings on May 7, 2026, and the stock fell 31% that day. The miss was net member joins.
Net joins is joins minus cancels, the one number the model actually runs on, and it appears on no line the company reports. When it came in light, full-year guidance fell from 4-to-5% same-club sales to about 1%, and roughly $1.6 billion of market value vanished between two closes. Every reported metric was green.
A soft join number is existential because of the shape of the business. The franchise segment earns a 71.9% margin on a royalty levied on member dues, not on workouts. A member is an annuity. Management named the cause as higher-than-expected attrition and could not explain it. The explanation was not missing. It was unread.
PLNT ran to a $114 peak in mid-2025 on a premium-repositioning story, then slid as growth slowed. On May 7, 2026 it fell 31% in a single day: a beat on revenue and earnings, undone by net member joins.
Two questions decide churn. The first: what makes a member write a one-or-two-star review. The second, what makes them actually cancel, comes in section 04. They do not have the same answer.
Holding club, period, reviewer type and review length constant, each operational failure was tested as a cause of a one-or-two-star review, estimated twice by two independent methods, checked three ways that a false cause would fail to reproduce, and confirmed by a model that strips out every fixed difference between one club and another.
A rude or absent front desk raises the chance a member turns hostile by 36 points (26 to 52), the largest effect in the data. Billing failures and cleanliness each add about 25. Broken equipment, the complaint members raise most often, adds only 5. These measure anger, not exit. Anger is the front desk. Whether that anger becomes a cancellation is the second question, and its leader is billing.
Bad attitude and ignorance customer service Female worker very rude every time
Elmwood Park · 1 star
The dot is the g-computation effect on the chance of a one-or-two-star review; the whisker is the 95% range. Every effect was checked three ways against a false cause and held.
Rank the six failures two ways: how often members raise them, and how much each one actually causes an unhappy member. The two lists come out nearly upside down.
Broken equipment is the complaint members raise most, and it anchors the capital debate: the extended replacement cycle, the remodels, the recovery amenities. In a club fixed-effects model it also has the smallest effect of the six, an odds ratio of 2.0 against the front desk's 35.3.
The front desk is the reverse: raised less loudly, close to free to fix, and the single largest cause of an unhappy member. The investment is aimed at the noise, not the damage. The highest-return move in this analysis is not a machine. It is a trained front desk and a billing system members can trust.
The same six failures, ranked two ways. On the left by how often members raise them; on the right by their causal effect on a one-or-two-star review. Follow the equipment line and the front-desk line.
Here the ranking flips. The front desk causes the most anger, but anger is not exit. Billing is only the third-largest cause of a bad review, and by far the largest cause of a member announcing they are done.
A member who raises a billing or cancellation problem says they will leave or warn others 50% of the time, 36 times the fleet baseline and nearly four times the rate for the front desk (13%). The mechanism is not that cancelling is too easy. It is that the recurring billing is not trusted.
Terrible gym. Hot water is always out and they won’t let me cancel my membership. Worst decision I’ve made in years.
Logan Square · 1 star
This is the wound the company keeps touching. It made cancellation frictionless and, in its own words, watched attrition rise; the instinct will be to add the friction back. The data points the other way. Members are not leaving because the exit is easy. They are leaving because the billing cannot be trusted. The fix is the trust, not the exit.
Share of members raising each issue who say they will leave or warn others. Billing towers over everything else.
The failures are not spread evenly. Strip the inflated ratings away and the fleet is two different companies at the same price.
Same brand, same fifteen-dollar membership, same eighteen months, same metro. The Outer Suburban clubs average 4.84 stars with a 3% one-star share. The Urban clubs average 3.46 with a 34% share, and their broken-equipment, cleanliness and safety rates run ten to twenty times higher. About a fifth of the fleet sits in areas the government calls low income, exactly the members a surprise charge or a rude desk drives away for good.
This place is gross and unsafe.
Avondale · 1 star
Trailing star rating by club type, with the one-star share beneath each bar and the per-driver failure rates at right.
The reason none of this reached the boardroom is the instrument. The figure leadership watches is part memory and, for nine months, part marketing.
Between February and October 2025 about 4,903 five-star reviews arrived above the normal rate, more than half of everything on record, while the count of unhappy members never moved. It was a review-generation burst, not a change in the gyms: a scraper cannot pick only happy reviews, and January, the busiest sign-up month, is the quietest for reviews. The displayed rating rose on solicited applause, then fell when it stopped, telling leadership nothing true either way.
Underneath, the honest signal is buried. Google shows one all-time average per club built on 36,279 reviews; the last eighteen months are a quarter of that. At the weakest urban club the door still reads 4.1 while recent members give it 2.8. A metric that cannot move cannot warn. The count of who writes, and why, moved months before the rating did.
Monthly reviews by star band. Five-star volume and the total move together; the one-and-two-star line barely breathes. The surge is marketing, not experience.
Purple is the rating Google shows. Red is what recent members actually gave. At the weakest clubs the gap is more than a full star, and it is frozen.
Churn cannot be fixed while it stays invisible. The members can see it, they named the cause, and it costs less than a treadmill.
Real reviews, exact and unedited, from members explaining why they were leaving. Spanish reviews appear in the original.
Terrible in person cancelation practices that don't even allow for cancelation on the phone
Bolingbrook · Outer Suburban · 1 star · 2025-01-11
The lady at the front desk is rude!!!!
Logan Square · Urban · 1 star · 2025-07-19
The women's bathroom was dirty.
Crystal Lake · Outer Suburban · 1 star · 2025-01-26
Always stinks in the locker rooms/ bathrooms
Waukegan Lewis · Outer Suburban · 1 star · 2026-06-02
La atención es mala, una vez que tengas tu membresía nunca te van ayudar para cancelarla tengo 2 años pagándola por obligación
The service is bad. Once a member has signed up, staff never help them cancel. Two years paying it out of obligation.
Addison · Inner Suburban · 1 star · 2025-05-19
This location is disgusting! & Adem is very rude!
New Lenox · Outer Suburban · 1 star · 2025-03-24
Demasiada gente siempre está lleno pésimo lugar para hacer ejercicio estoy pensando seriamente en cancelar mi membresía
Logan Square · Urban · 1 star · 2026-02-25
Someone broke into my car and I wasn't even there for 30 minutes
Washington Square · Urban · 1 star · 2025-10-18
Not the order of complaint volume. The order of proven, dollar-relevant impact.
Track, monthly and per club, the count of members raising each failure and the share who signal they will leave. This is the leading indicator missing in Q1, and it is built from data the company already generates.
Staff conduct is the largest cause of an unhappy member and among the cheapest to fix. Redirect the management attention freed by the slower equipment cycle into hiring, coverage and training at the weakest clubs.
Billing is the strongest leaving signal in the data. Members leave because the recurring billing cannot be trusted, not because the exit is easy. Fix the charge-after-cancel and surprise-fee failures; adding friction treats the symptom and invites regulators.
The damage concentrates in a nameable set of urban clubs at ten-to-twenty times the failure rate. Fixes there are operational, not capital, and they move the blended number the market watches.
This is not a survey read or a topic count. Each of the six causes was estimated by five independent methods, cross-checked against a false cause three separate ways, and confirmed by a model that removes every fixed difference between clubs. The record:
| Cause of an unhappy member | Reviews | Effect (pp) | 95% range | Fixed-effects odds | Refutations | Confidence |
|---|---|---|---|---|---|---|
| Staff conduct | 107 | +36 | 26 to 52 | 35x | passed x3 | Strong |
| Billing or cancellation | 107 | +25 | 17 to 36 | 15x | passed x3 | Strong |
| Cleanliness | 107 | +25 | 16 to 36 | 13x | passed x3 | Strong |
| Safety, theft, harassment | 41 | +26 | 12 to 58 | 11x | passed x3 | Moderate |
| Broken equipment | 122 | +5 | 2 to 9 | 2x | passed x3 | Strong |
| Crowding | 33 | +7 | -0 to 18 | 3x | passed x3 | Moderate |
Effect is the g-computation average treatment effect on the chance of a one-or-two-star review. Fixed-effects odds is the odds ratio from a within-club logistic model. Safety and crowding rest on smaller samples, so they carry wider ranges and are marked Moderate.
The share of reviews that are one or two star, by how many distinct failures a review names. The rise is steep and monotonic, the signature of a real effect rather than noise.
Odds ratios from a club fixed-effects logistic model, on a log scale, with 95% ranges. Every permanent difference between one club and another is already removed, so these are within-club effects.
Every figure in this analysis came from public Google reviews that Planet Fitness generates and does not systematically read. Dimension Labs turns that raw text into structured, comparable, monitorable intelligence in four steps, and the fourth is the one no review dashboard performs.
All 9,186 reviews across 33 clubs are read in full, including the ones with no star rating that a ratings report ignores entirely.
A language model reads each review the way a trained analyst would and records what it is about, what went wrong, who was involved, and whether the member will return. Free text becomes a queryable table.
Each driver runs through a model that holds the club, the period, the review length and the reviewer constant, so an effect is not the location, the vintage, or the writer. Every estimate carries a 95% range.
Shuffle the cause and the effect should vanish. Inject a fake variable and the estimate should hold. Re-run on random slices of the data and it should hold. A finding that fails any test does not appear.
Every number here was computed from public Google reviews. No survey, no member export, no system access, nothing the company had to hand over. That is the proof of the method, and also its constraint: reviews are the loudest slice of the member voice, written by the people who chose to speak up.
The same causal engine becomes an order of magnitude more actionable on the data the company already owns. Layer in the member-management and billing system, the cancellation and freeze logs, join and attrition records, key-card check-in frequency, the app and Black Card usage, and post-visit surveys, and the question upgrades from what causes a bad review to what causes a lapsed member.
Whether a billing failure cuts check-in frequency before it shows up as a cancel. What a rude front desk costs in retained months. Which fix wins a member back. Same standard of proof, aimed at net member joins, the number the market actually prices.
And it runs as a monitor, not a snapshot: the same read every week across the fleet, showing which clubs are slipping and whether the front-desk and billing fixes are landing, before the next print.
This is the early-warning system the last quarter proved was missing, and the instrument for the one number the stock trades on.
The figure that broke the quarter was churn, and its causes were public the whole time: the front desk, the billing, and a set of clubs that can be named. This reads one metro from one weekend of compute. The same analysis, run across the full fleet every month, is the early-warning system Q1 proved was missing.
The member signal reads 9,186 Google reviews across 33 Chicago Planet Fitness clubs, January 2025 to July 2026, joined on the unique review identifier with no deduplication. The outcome is a one-or-two-star review; leaving intent is a stated intention to cancel, not return, or warn others. Operational failures are extracted from the review text with precision-audited patterns and treated as causes; they are text proxies, not a labeled dataset, and small categories are reported at lower confidence. Each causal effect uses DoWhy propensity-score weighting and a logistic standardization with a 300-sample bootstrap interval, is checked against placebo, random-common-cause and subset refutations, and is confirmed by a club fixed-effects logit. Heterogeneity uses an EconML causal forest.
The financial figures are public and cited: the Q1 2026 results and the 31% single-day decline of May 7, 2026 (the ~$1.6B market-value figure is computed from the May 6 and May 7 closes and shares outstanding); the 71.9% franchise-segment margin and the royalty-on-dues model; average monthly dues of $19.51 across roughly 21.5 million members; the ~22% of clubs in low-income areas; and management's attribution of higher attrition to "cancel anytime" messaging and online cancellation. Planet Fitness does not disclose a churn rate; none is invented here. Review-count figures and per-club rates are queried from the data; leaving intent is measured, not assumed.
| Cause of an unhappy member | Effect (pp) | 95% range | Leaving intent | Confidence |
|---|---|---|---|---|
| Staff conduct | +36 | 26 to 52 | 13% | Strong |
| Billing or cancellation | +25 | 17 to 36 | 50% | Strong |
| Cleanliness | +25 | 16 to 36 | 12% | Strong |
| Safety, theft, harassment | +26 | 12 to 58 | 12% | Moderate |
| Broken equipment | +5 | 2 to 9 | 11% | Strong |
| Crowding | +7 | -0 to 18 | 9% | Moderate |
Dimension Labs Causal Intelligence. Dataset figures are queried and rendered from a fixed results file; public figures are cited to the external record. No figure is estimated or recalled. The argument runs one direction: the miss was churn, the causes are nameable and ranked, and investment is aimed at the noise instead of the damage.