What it is
Guests rate stays on each channel. Some channels use a 5-point scale and some a 10-point scale. To report one number, convert to a common scale and weight by review count.
Many channels also show category ratings such as cleanliness or communication. Those are often more useful than the overall average.
Why operators track it
- Demand. Ratings appear in search results and on listings.
- Operations feedback. Low cleanliness or accuracy scores point to fixable problems.
- Owner reporting. Owners see reviews and ask about them.
- Quality control for teams and vendors.
How it is calculated
Normalized review score = Σ(review count × rating rescaled to a common scale) ÷ Σ review count
- Rescale linearly (rating ÷ scale max × 5) unless you have a better mapping.
- Weight by count.
- Use reviews dated in the period, or a trailing 12 months.
- Keep raw channel scores alongside.
Worked example
Example data The same made-up portfolio is used on every hospitality page: a manager with 120 homes, four of them shown in detail.
The four example homes received 17 reviews in October: 9 on a 5-point channel averaging 4.82, 5 on another 5-point channel averaging 4.60, and 3 on a 10-point channel averaging 8.9 (4.45 rescaled). The weighted score is 4.69 out of 5.
How to read it
- Small samples swing. A home with three reviews should show the count.
- Read categories. A drop in cleanliness is actionable; an overall drop is not.
- Compare a home to itself over a trailing period.
- We do not publish a benchmark. Levels vary by market, season, and property type.
Definitional choices and common pitfalls
- Scale. Linear rescale or a mapping. State it.
- Period. Reviews posted in the month, or trailing 12 months.
- Weighting. By review count or by stay.
- Private feedback. Exclude from the public score; track separately.
How different roles use it
| Role | How they use it |
|---|---|
| Operations | Reads category scores by home and cleaning team. |
| Owner relations | Shares reviews with owners. |
| Revenue manager | Watches rating effects on demand. |
| Product / engineering lead | Ingests reviews from each channel with scale metadata. |
Questions it answers
- Which homes have falling scores?
- Is cleanliness the main complaint?
- Do scores differ by channel for the same home?
How to present it in a report
- Normalized score with raw channel scores beside it.
- Category scores by home.
- Review count shown on every score.
Guest reviewsOctober 2026
Score (5-point)4.6917 reviews
WatchLake houseone review with cleanliness 3.0
Channels3
| Channel | Reviews | Raw | Rescaled to 5 |
|---|---|---|---|
| Channel A (5-point scale) | 9 | 4.82 | 4.82 |
| Channel B (5-point scale) | 5 | 4.6 | 4.60 |
| Channel C (10-point scale) | 3 | 8.9 | 4.45 |
Data you need
Fields
| Field | What it means | Example value |
|---|---|---|
| Review | Channel, date, overall and category ratings | 4.8 overall |
| Scale max | 5 or 10 | 5 |
| Reservation | Link to the stay | R-88412 |
| Text | Review text | Clean and quiet. |
What the raw data looks like
Example data A few rows as they might come out of the source system.
| Review | Channel | Home | Date | Overall | Scale | Cleanliness |
|---|---|---|---|---|---|---|
| V-3011 | Channel A | Beach house | Oct 9 | 5.0 | 5 | 5.0 |
| V-3019 | Channel C | Condo | Oct 14 | 9.0 | 10 | 8.5 |
| V-3024 | Channel B | Lake house | Oct 22 | 4.0 | 5 | 3.0 |
Where this data usually lives
- OTAs and booking channels (Airbnb, Vrbo, Booking.com, and partner distribution sites) hold payout reports, host fees, reviews, and cancellations. Payouts are usually net of host fees.
- CRM and guest tools hold owner contracts and leads, guest messages, reviews, and upsells. Sales projections often live in spreadsheets.
- Property management system (PMS) holds units, the calendar with block types, reservations, rates, fees, and often owner statements and trust accounting. For example: Guesty, Hostaway, OwnerRez, Track, Streamline, and Escapia. Some larger managers run their own in-house PMS.
The same field can live in more than one system. Agree with the customer which one is the source of truth.
Common data gaps and traps
- Scale not stored. Ratings saved as numbers with no scale.
- Reviews not linked to reservations. Hard to tie to a cleaning team.
- Category names differ by channel.
Related metrics
FAQ
- How do you combine ratings on different scales?
- Rescale each to a common scale and weight by review count. State the method.
- Should guest surveys from direct bookings be included?
- Keep them separate unless they use the same question and scale.
- What is a good review score?
- We do not publish a benchmark. Levels vary by market, season, and property type.
For your data teamFormula variants, tables, SQL sketch, and dashboard spec
Formula details
score_5pt = rating / scale_max * 5
normalized = AVG(score_5pt) over review rows (weights by count)
Worked example detail
| Channel | Reviews | Raw average | Rescaled |
|---|---|---|---|
| Channel A (5-point scale) | 9 | 4.82 | 4.82 |
| Channel B (5-point scale) | 5 | 4.6 | 4.60 |
| Channel C (10-point scale) | 3 | 8.9 | 4.45 |
| Weighted | 17 | 4.69 |
Tables and fields
| Table | Fields | Notes |
|---|---|---|
reviews |
review_id, channel, unit_id, reservation_id, posted_at, overall, scale_max | |
review_categories |
review_id, category, rating |
SQL sketch
SELECT unit_id, AVG(overall / scale_max * 5) AS score_5pt, COUNT(*) AS reviews
FROM reviews WHERE posted_at >= '2026-10-01' AND posted_at < '2026-11-01'
GROUP BY unit_id;
Build spec
Copy this into a ticket. It lists the grain, inputs, logic, and edge cases.
metric: hospitality.review_score
grain: one row per review
inputs:
- overall rating
- scale max
- channel
- unit
logic:
- rescale to 5
- average weighted by count
edge_cases:
- missing scale: exclude and flag
- few reviews: show count prominently
- edited reviews: use latest
As a dashboard card
- KPI for normalized score with count; table of homes with category scores.
Common mistakes
- Averaging channel averages without weights.
- Mixing scales.
- Hiding review counts.