Dimension Labs / Causal Intelligence
NYSE: PLNT
An independent causal analysis

Churn erased $1.6 billion in a day. The cause was the front desk and billing.

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.

Subject
Planet Fitness
Chicago fleet
Basis
Member reviews +
public filings
Sample
9,186 reviews
33 clubs
Span
Jan 2025
to Jul 2026
Scroll
What this analysis is

A causal read of why Planet Fitness members quit, built entirely from public data.

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 corpus
9,186
Google reviews across 33 Chicago clubs, every one read in full
The instrument
28
structured dimensions read from each review by a language model
The fusion
Public
Q1 2026 filings, the May 7 crash and the franchise economics, all cited
The method
Causal
effects, not correlations, each one stress-tested three ways
The one thing to know

Planet Fitness is spending on broken equipment.
Members are quitting over the front desk and billing.

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.

$1.6B
market value lost in a single day when net joins missed
the price of not seeing churn
50%
of members who hit a billing failure say they will leave or warn others
36x the fleet average
+36pp
the front desk is the largest cause of an unhappy member
broken equipment: +5pp
71.9%
franchise margin on a royalty levied on members, not workouts
every quit is an annuity lost
The causal spine

From a member's own words to the dollars at stake

HOW A MEMBER'S OWN WORDS BECOME MARKET VALUEone causal chain, from 9,186 reviews to the number that broke the stock01 THE VOICE9,186member reviews, read in full33 Chicago clubs · 18 months02 THE CAUSEwhat makes a member unhappy (pp)Front desk+36Safety+26Billing+25Cleanliness+25Crowding+7Equipment+503 THE CHURN50%of billing complaints saythe member will quit04 THE MONEY$1.6Brepriced in a single day71.9% margin per memberTHE MEMBER SIGNALMARKET VALUE AT RISKheld against club, period, reviewer type and length · estimated twice · cleared by three stress-tests
Purple is the member signal, the nameable cause. Yellow is the money it puts at risk.
The argument, in six moves
01
The stake

The number that broke the stock is churn, and it sits on no dashboard

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.

Exhibit 01The market loved the story, then priced the churn

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.

$0$30$60$90$120May 7, 2026: -31%beat revenue & EPS, missed net joinsJan '25 ~$90Jul 2025 peak $114May 6 $63.96May 7 $44.01Jun 30 $52.17Jan '25Jul '25Dec '25Jan '26May 6May 7Jun 30PLNT daily close, indexed to public market prices
PLNT daily close. Labeled points are actual closes: Jul 23 2025 peak $114.47; May 6 2026 $63.96; May 7 2026 $44.01 (-31%, about $1.6B); Jun 30 2026 $52.17. Source: public market data.
02
Dissatisfaction

The front desk is the biggest cause of an unhappy member. Broken equipment is the smallest.

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

Exhibit 02What makes a member unhappy

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.

+0+20+40+60Causal effect on the chance a review is one or two star (percentage points)Staff conduct107 reviews name it+36ppBilling or cancellation107 reviews name it+25ppCleanliness107 reviews name it+25ppSafety, theft, harassment41 reviews name it+26ppBroken equipment122 reviews name it+5ppCrowding33 reviews name it+7pp
Two independent estimates, each with a 95% range, cross-checked against a club fixed-effects model. The full method is named in the closing section.
03
The misallocation

Planet Fitness is spending on the loudest problem, not the costly one

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.

Exhibit 03The priority list, rearranged by cause

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.

RANKED BY VOLUMEhow often members raise itRANKED BY CAUSAL EFFECTtrue effect on a 1-2 star reviewBroken equipment122 reviews · #1 loudestFront desk & staff107 reviews · #2 loudestBilling & cancellation107 reviews · #3 loudestCleanliness107 reviews · #4 loudestSafety & theft41 reviews · #5 loudestCrowding33 reviews · #6 loudestFront desk & staff+36pp · #1 by effectSafety & theft+26pp · #2 by effectBilling & cancellation+25pp · #3 by effectCleanliness+25pp · #4 by effectCrowding+7pp · #5 by effectBroken equipment+5pp · #6 by effectEquipment is raised most, and matters least.The front desk is raised less, and matters most.
Left rank is the number of reviews naming each issue. Right rank is the g-computation causal effect. The rankings are close to mirror images.
04
Defection

Billing is the single biggest reason members say they will quit

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.

Exhibit 04A billing failure is a leaving signal

Share of members raising each issue who say they will leave or warn others. Billing towers over everything else.

0%10%20%30%40%50%fleet average 1%Billing or cancellation50% (36x)Staff conduct13% (9x)Safety, theft, harassment12% (9x)Cleanliness12% (9x)Broken equipment11% (8x)Crowding9% (6x)Share of members raising each issue who say they will leave or warn others
About one in six billing complaints specifically describes being charged after cancelling or a surprise fee, the trust break, not the ease of exit.
05
The map

A handful of urban clubs carry most of the failures

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

Exhibit 05One brand, two operations

Trailing star rating by club type, with the one-star share beneath each bar and the per-driver failure rates at right.

0123453.46Urban34% one-star share4.21Inner Suburban17% one-star share4.84Outer Suburban3% one-star shareONE-STAR SHARE BY DRIVERBroken equipment8.51.70.4Billing / cancel4.32.40.6Cleanliness5.02.50.5Staff conduct3.63.00.6Safety / theft2.51.10.1
9,186 reviews across three club types, same metro and price.
06
The blind spot

A review campaign inflated the rating and hid the churn

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.

Exhibit 06Nine months of applause, then silence

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.

FEB TO OCT 2025 · THE SURGE WINDOW022044066088025-0125-0325-0525-0725-0925-1126-0126-0326-0526-075 STARTOTAL1-2 STARJanuary is the peak joining month, yet the floor
Correlation of total volume with five-star count 0.999; with one-and-two-star count 0.48.
Exhibit 07The door versus the last 18 months, all 33 clubs

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.

3.03.54.04.55.0starsDISPLAYED (ALL-TIME) vs LAST 18 MONTHSAvondale+1.3Little Village+1.2Schaumburg Barrington+0.9N. Broadway+0.9Oak Lawn+0.9Cicero+0.8Back of Yards+0.8Logan Square+0.8Pullman Park+0.8Elmwood Park+0.8Washington Square+0.8ChathamMelrose ParkForest ParkCrystal LakeW FosterTinley ParkSouth LoopJoliet JeffersonDeerfieldLockportNew LenoxAurora Lake StBolingbrookPlainfieldWest DundeeMundeleinMontgomeryNapervilleElginWaukegan LewisAddisonJoliet Voyagerpurple = displayed rating · red = actual last-18-month rating
33 clubs, ranked by the gap between displayed and recent rating. Coverage ranges 6% to 71% of each club's lifetime reviews.
Read together
Churn cannot be fixed while it stays invisible. The members can see it, they named the cause, and it costs less than a treadmill.
07
The receipts

The reasons, in their words

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

08
The verdict

Four moves, ranked by what they return

Not the order of complaint volume. The order of proven, dollar-relevant impact.

1

Instrument the cause of churn, not the rating

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.

Highest payoff
Office of the CEO / analytics
2

Fund the front desk before the equipment floor

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.

Largest cause, lowest cost
Operations / franchise support
3

Repair billing trust, do not re-add cancellation friction

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.

Strongest churn signal
Billing systems / corporate
4

Work the named clubs, not the fleet average

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.

Concentrated, addressable
Regional operations
09
The proof

Every cause here is proven, not asserted

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:

9,186
reviews analyzed, none sampled out
257,208
structured readings, 28 per review
6
operational causes tested
5
independent estimators per effect
3
refutation tests each must pass
300
bootstrap resamples per interval
Cause of an unhappy memberReviewsEffect (pp)95% rangeFixed-effects oddsRefutationsConfidence
Staff conduct107+3626 to 5235xpassed x3Strong
Billing or cancellation107+2517 to 3615xpassed x3Strong
Cleanliness107+2516 to 3613xpassed x3Strong
Safety, theft, harassment41+2612 to 5811xpassed x3Moderate
Broken equipment122+52 to 92xpassed x3Strong
Crowding33+7-0 to 183xpassed x3Moderate

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.

Exhibit 08One failure and the odds jump; two and they compound

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.

0%25%50%75%100%4%No failure named8,742 reviews66%1 failure378 reviews81%2+ failures57 reviews
9,186 reviews grouped by count of distinct failures named. These rates are unconditional; the causal estimates in the table above hold the confounders constant.
Exhibit 09The front desk raises the odds of a bad review thirty-five fold

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.

1x2x5x10x20x50xOdds a review is 1-2 star when the issue is named, vs not (club fixed-effects logit, log scale)Front desk / staffp < 0.00135xBilling & cancellationp < 0.00115xCleanlinessp < 0.00113xSafety & theftp < 0.00111xCrowdingp = 0.0093xBroken equipmentp = 0.0032x
Club fixed-effects logit: the odds a review is one or two star when the issue is named versus not. All p-values below 0.01.
The robustness recordWhat was run, and what each step proves
IPW
Propensity-score weighting (DoWhy) balances reviews that name a failure against those that do not, on club, period, reviewer type and length, before comparing outcomes.
Regression
A second estimator on the same causal graph; effects agree in sign and rough magnitude, so the result is not an artifact of one method.
G-computation
Logistic standardization predicts each review's outcome under failure and no-failure; the gap is the effect, reported with a 300-sample bootstrap 95% range.
Fixed effects
A club fixed-effects logit removes every permanent difference between clubs, so an effect cannot be a bad location wearing a cause's clothes.
Placebo
Shuffle the cause at random and a true effect collapses toward zero. Every driver did, with the change near zero and not significant.
Common cause
Inject an unrelated variable and a true effect is unmoved. Every driver held.
Subset
Re-estimate on a random subset of the reviews and a true effect is stable. Every driver held.
Causal forest
An EconML causal forest shows the front-desk effect is not uniform: largest in the outer-suburban clubs at +43pp, and present everywhere.
Time series
An interrupted time series isolates the 2025 review surge: five-star volume +532 a month (p below 10⁻⁸); one-and-two-star volume +11 a month, not significant. The applause moved; the complaints did not.
No fabrication
Every dataset figure is queried from a fixed results file, and public figures are cited to filings. No number is recalled or estimated.
10
The method

How 9,186 public reviews became findings a board can act on

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.

01  Read

Every review, not a sample

All 9,186 reviews across 33 clubs are read in full, including the ones with no star rating that a ratings report ignores entirely.

02  Extract

28 dimensions from each one

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.

03  Model

Cause, not correlation

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.

04  Stress-test

Every finding, before it is stated

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.

The instrument28 dimensions read from every review
Sentiment & lifecycle
Overall sentiment
the true tone of the visit, read from the words, not the star
Member lifecycle stage
joining, established, departing, or already a former member
Review occasion
what prompted it: a sign-up, a routine workout, an incident, a billing event
Primary friction
the one thing that went wrong, by a fixed severity order
The front desk & people
Staff interaction
helpful, rude, absent, unhelpful, or discriminatory
Staff named
the actual employee a member praises or blames
Judgement-free promise
whether the no-judgement promise held or broke
Money & membership
Billing or cancellation
charged after cancelling, blocked cancellation, surprise or unauthorized fee
Amount disputed
the dollar figure a member says they were wrongly charged
Price and value
great value, poor value, or a premium tier not worth it
Recommendation
will return, will not return, recommends, warns others, or switching
Competitor named
the rival gym a member names as the alternative
The facility
Equipment condition
broken, aged, too few, or the wrong mix of machines
Equipment named
the specific machine that failed
Cleanliness
hazardous, persistently dirty, understocked, or spotless
Facility infrastructure
air conditioning, plumbing, showers, lighting, locker rooms
Black Card amenity
tanning, hydromassage and the premium amenities, working or broken
Amenity named
the specific premium amenity
Crowding
unusable when busy, waiting for machines, or comfortably quiet
Member conduct
phone-camping, weights left out, disruptive groups, unsupervised minors
Access and hours
the 24-hour claim, unexpected closures, lockouts at the door
Safety & resolution
Safety and security
assault, harassment, theft, or an unsafe environment
Issue resolution
whether a problem was fixed, ignored, or made worse
Issue severity
minor, moderate, or severe
Actionability owner
which team inside the company owns the fix
In the member's words
Three verbatim fields
the exact quote for the friction, the billing failure, and the overall sentiment
28 dimensions · 9,186 reviews · 257,208 structured readings. A topic-and-keyword tool captures perhaps three of these. The rest are the difference.
Where this goes

All of this came from data anyone can read. Imagine it on the data only Planet Fitness has.

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 rating said nothing.
The members said everything.

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.

Dimension LabsCausal Intelligence
Methodology & Sources

How this was built, and what it can and cannot claim

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 memberEffect (pp)95% rangeLeaving intentConfidence
Staff conduct+3626 to 5213%Strong
Billing or cancellation+2517 to 3650%Strong
Cleanliness+2516 to 3612%Strong
Safety, theft, harassment+2612 to 5812%Moderate
Broken equipment+52 to 911%Strong
Crowding+7-0 to 189%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.