How Booking (and Other OTAs) Calculate Your Average Score — and How You Can Influence It

In short

Booking, Expedia and Google start from different scales and calculate the average differently: Booking has weighted recent reviews more heavily than older ones since January 2025, Expedia converts a 1-5 scale to /10 with a simple average, and Google does a simple average of stars with no date weighting — and separately runs its own hotel class rating system, which has nothing to do with guest reviews.

Why scores differ between platforms

A hotel can score 8.7 on Booking, 4.3 on Google and 8.9 on Expedia in the same month without any of the three being "wrong". It's not that different guests feel differently — each platform defines its own scale, its own calculation method, and its own rules about who can leave a review and for how long it counts. Comparing scores across platforms without accounting for those rules is comparing apples to oranges.

Booking: recency-weighted average since 2025

Booking uses a 1-10 scale and calculates your score as the average of every score received over the past 36 months. So far, simple. The relevant change arrived in January 2025: since then, more recent reviews carry more weight in that calculation than older ones — a review from the last 3 months influences the score considerably more than one from 30 months ago. Booking also displays category scores (cleanliness, comfort, value for money, facilities, location, staff) visibly, but those sub-scores are informational: they don't feed directly into the headline score you see in search.

Expedia: a 1-5 scale converted to /10, verified guests only

Expedia asks guests for an overall score from 1 to 5, which it converts to a 1-10 scale to display next to your name. The calculation it discloses is a simple average of those converted scores, with no disclosed weighting by age. The real difference versus Booking isn't the formula — it's the entry filter: only guests who booked and stayed through Expedia can leave a review, which lowers volume but also noise. Reviews are removed after 3 years, unless the property has too few reviews and removing them would leave it without enough history.

Google: a simple star average — and a completely separate hotel class system

The score you see next to your name on Google Search and Maps is, by Google's own documentation, a simple average of every published 1-to-5-star rating, with no weighting by date or review length. The difference that actually matters here is who gets to rate: on Google, anyone with an account can rate a hotel without having proven they stayed there — something neither Booking nor Expedia allow. And there's a nuance that causes constant confusion: the "hotel class" stars that sometimes appear next to your name on Google are not a guest-review average — they're a separate system Google calculates from third-party data, its own research, and models evaluating price, location or room size. Two numbers that look alike and aren't.

Comparison table: how each platform calculates your score

Summarizing the factors above side by side:

The problem: without aggregated visibility, you don't know what drives your score

If your Booking score drops two tenths in a quarter, the question that matters isn't "what does the algorithm say" — it's "what happened to my guests". And that answer is almost never on a single platform: sometimes a dip on Booking lines up with recurring complaints on Google that nobody had cross-referenced, or with a one-off issue (a renovation, a staff change) that only shows up when you look at review dates across all three sites at once. Looking at each platform separately, each with its own calculation logic, makes it nearly impossible to tell noise from real signal.

Frequently asked questions

How Mosana turns that aggregation into revenue

With each platform weighting, filtering and time-limiting differently, the "average score" isn't a plain comparable number. Mosana aggregates reviews from every source, cross-references them with the rest of the guest signal you already have (PMS, direct conversation), and applies AI to prioritize what to fix first based on its estimated revenue impact.

That prioritization adjusts to what matters for your business: you can weight by channel (more weight to Booking if it's your main booking source, or to Google if it's where guests discover you) or by signal type (a review from a guest who stayed 5 nights can carry different weight than one from a single night, and a complaint about your top room category can be prioritized above one about the standard category). The goal isn't to show you every score at once — it's to tell you which of those signals, properly weighted, will actually move your revenue if you fix it first.