Every percentage carries a Wilson 95% CI and a row count; every quote is row-cited on the engagement deliverable.
The read under the rating.
Most tools count what guests mention. We read what they mean — every review, in any language, to the bottom — and hand you the asset-side decision a rating average hides. On any property, without operator cooperation.
A category dashboard counts what guests mention.
We read what they mean.
A rating average and a category-sentiment tool tell you guests talked about breakfast, and tag it amber. They can't tell you which kind of thing it was — and that is the part that moves the asset.
Those three distinctions are the lens. Each review is read end to end — over a hundred structured fields, in its original language — then read again as a set. Everything below is that read, anchored to the sentence behind it.
What a tool tags amber, read in full.
One real public review. A category tool returns three negative tags and moves on. Hover any highlighted phrase — the structured field it produces lights up. The same words, read to the bottom, are a decision.
What a category tool returns
A low score and three tags. They tell you it went wrong — not what it costs, or what to fix.
What the read returns
Eight Porto hotels. One rating, five operator grades — and the rate doesn't track the grade.
All eight cluster at roughly nine out of ten on guest rating — a search filter lumps them together. The read grades operator quality A–F against the cohort, beside the rate each one charges. Anonymised A–H; every grade traces to the guests' own words.
| Hotel | ADR | NLS | Operator culture | Service recovery | Repeat guests | Overall |
|---|---|---|---|---|---|---|
| Hotel B | €679 | +50 | A | A | B | A |
| Hotel A | €520 | +45 | A | A | A | A |
| Hotel G | €249 | +33 | B | C | C | B |
| Hotel C | €268 | +37 | C | C | C | C |
| Hotel D | €248 | +25 | C | D | D | D |
| Hotel F | €207 | +35 | D | C | C | D |
| Hotel E | €592 | +41 | C | D | D ↓ | D |
| Hotel H | €708 | +18 | F | F | F | F |
8 Porto premium hotels (upper-upscale to luxury) · 18,550 reviews · all clustered at ~9/10 guest rating · grades are cohort-relative. ADR = average offered nightly rate. NLS = net loyalty score (−100 to +100), a strict-language read of repeat intent. D ↓ marks a property declining since an ownership change. The set's two highest rates sit at opposite ends of the grade.
Three reads a rating average can't produce.
Each finding traces to the verbatim behind it, carries its window and sample size, and closes in a decision the right seat can act on.
Repeat guests identified by comparator language; window and n labelled on every shift claim.
Association, not a causal claim — verified across all eight hotels, each above the sample floor (Newcombe 95% CIs).
Every finding closes in a number you can defend.
We don't hand you a sentiment score. We hand you a decision — and the figure behind it, translated on your ADR with the anchor named, so your analyst can check the math.
Finding6.8% of repeat-guest reviews say "worse than last time"; 302 guests state outright they won't return — yet many still rate 8/10, so the operator's average reads fine.
BridgeLost repeat room-nights × your ADR × repeat-guest lifetime value. Repeat revenue carries near-zero acquisition cost — the most expensive kind to lose.
≈ €100–180K / yr · illustrative on a 100-key asset at €250 ADR · anchor: room-night lossDecisionSize the retention envelope and act before the rating moves.
FindingReviews naming a staff member are 44–60pp more likely to carry explicit advocacy. The goodwill concentrates around a handful of people.
BridgeWhen a named advocacy-driver leaves, a measurable share of return intent is exposed — a cost no rating average can see.
DecisionProtect and price the people guests come back for.
The read shows the decision and the real rate. Your asset-specific € is computed on the paid deliverable, on your actual ADR, occupancy and repeat-rate. We under-claim on purpose — the number you can put in a memo beats the number that looks good on a slide.
They count concepts. We read meaning.
STR covers the market; the operator's reputation tool covers the rating. We sit on the other side of the table — the why under the rating, for the seat deciding on the asset.
The reputation & ops layer.
Buyer: GM, front office, brand reputation manager. Job: respond to reviews, raise the score, train the team.
- SAASPer-property monthly subscription; the hotel is the customer.
- DATAAggregate scores and category sentiment; single-number indices.
- FORMAn always-on dashboard the team logs into.
- NEEDSYour own property, as a paying client.
What lands in the underwriting file.
Buyer: asset manager, transaction advisor, operator-selection advisor, owner. Job: decide on the asset — buy, hold, swap, intervene.
- MODELProject-priced engagement, designed to drop into an investment memo.
- DATAMeaning read from the prose — which cohort leaves in silence, which staff carry advocacy, what moves before the rating.
- FORMOne designed PDF per engagement — the product is the read, not access.
- NEEDSNothing from the hotel. Any property, from public reviews — yours, a rival's, a target.
A PDF your committee will actually read.
The cohort above, as the read ships it: the grade, the verbatim behind it, the decision behind each finding, and a labelled confidence band on every claim. Asset-side or operator-side, calibrated to your seat.
What it is — and what it isn't.
Where the read works
No operator cooperation. No PMS feed, no NDA, no site visit — public reviews only. That's what lets us read a rival or a target you have no relationship with.
Periodic, by design. Continuous monitoring of your own book is the operator platform's job. We are the periodic deep read it can't run — on targets, on rivals, on operator-transition deltas.
What reviews don't capture — and what we won't claim
We name the lever and the decision; we don't cost the build. That's your cost engineer. And whether your team executes is your call.
A thin finding stays directional. Anything resting on a handful of reviews is flagged, never sold as fact — every shift claim carries its window and its n.
From brief to deck in 10–14 days.
Four steps. Each one named, scoped, and dated. No sales calls required.
The asset, the question, the peer set.
You send the property (or portfolio), the question the deck should answer, and a peer set if you have one. We propose one if you don't.
Engagement note in two business days.
One page: scope, peer-set confirmation, deliverable shape, fixed price, dates. You confirm or adjust.
One designed PDF deck.
Anchored on the agreed peer set, calibrated to the question. No raw extracts — the deck is the deliverable.
Recurring cadence (optional).
For portfolio mandates: the same deck refreshed quarterly, or anchored on diligence windows. Same engine, same definitions.
Two founders. One read.
Rui Andrade
MSc in Informatics Engineering, University of Porto. Built the extraction pipeline and the read end to end — he has personally read more hotel reviews into structured data than anyone should admit to.
José Andrade
PhD candidate in hotel intelligence, University of Vigo (Spain); 26 years in industry before hospitality research. Calibrates the read against how hospitality markets actually behave, and owns the case methodology behind each engagement.
Name an asset. Get the read.
The next step is a scoped read on a property of yours — or a rival, or a target — anchored on its actual peer set.
Tell us who you are, the asset, and the question the deck would answer. We reply within two business days with a scoped engagement note and timing.
No demos · no free property analyses · two assets, €4,000, you're the judge