Review and rating statistics 2026

32 review and rating statistics, every one dated.

How many people read reviews, the star rating and review count they expect before they will use a business, how fast they want a reply, and what AI written reviews have done to trust. Each figure carries the year of the research it came from, so you can see what is current.

✓ 4 charts, every figure in real text so it can be quoted✓ Grouped by topic, from reading habits through to fake reviews✓ Every source named in full at the foot of the page
Key findings

The 6 numbers worth remembering.

If you take nothing else from this page, take these 6. Each one is dated, and the rest of the article expands on all of them.

97%

of people read reviews for local businesses. Reading reviews is not a step some customers take. It is the step almost all of them take.

What this means: You are not choosing whether people read your reviews. You are choosing what they find.
2026 data
68%

will only use a business rated 4 stars or higher. That was 55% a year earlier.

What this means: The bar moved 13 points in 1 year. A 3.9 average that was fine last year is not fine now.
2026 data
47%

will not use a business with fewer than 20 reviews. Only 9% would use one with 5 or fewer.

What this means: 20 reviews is the floor. Below it, your rating barely matters because people will not trust the average.
2026 data
74%

only care about reviews written in the last 3 months.

What this means: Reviews go stale. A strong average that stopped 2 years ago reads like a business that stopped too.
2026 data
89%

expect the business to reply to reviews, and 81% expect that reply within a week.

What this means: Replying is the one part of your reputation you fully control, and most people are watching for it.
2026 data
45%

used ChatGPT or another AI tool to find local recommendations, up from 6% a year earlier.

What this means: Reviews now feed answers you never see. What AI says about you is built from what your reviews say.
2026 data
Who reads reviews

How many people read reviews, and how closely.

Reading reviews has stopped being a step people take before a big purchase. For most people it is now part of choosing anything at all.

97%

of people read reviews for local businesses.

What this means: Treat your review profile as a storefront. It is the first thing most people see.
2026 data
41%

always read reviews while browsing for a business, up from 29% in 2025.

What this means: Reading reviews is turning into a habit rather than a check people do before a big purchase.
2026 data
93%

have made a purchase after reading reviews, and 27% of them spent over $1,000.

What this means: Reviews are not only for cheap impulse buys. They sit in front of large spends too.
2026 data
6

review sites is the average number a person checks before deciding.

What this means: Being strong on Google alone is not enough. People cross-check.
2026 data
Worth knowing: These figures come from a survey of 1,002 US adults and are self-reported, which means people are describing their own habits rather than being measured. Self-reporting tends to make people sound more careful than they are, so read them as attitudes rather than logged behaviour.
The rating bar

The rating and review count you have to clear.

People filter before they read. A rating and a review count decide whether your written reviews are ever opened, and both bars moved up in the last year.

92%

say star ratings matter when they choose a business.

What this means: The rating is the filter. Most people never reach your written reviews if the number is wrong.
2026 data
68%

will only use a business rated 4 stars or higher, up from 55% in 2025.

What this means: A 13 point jump in 1 year. This is the fastest moving number on the page.
2026 data
31%

will only use a business rated 4.5 stars or higher, up from 17% in 2025.

What this means: Nearly double in a year. In competitive categories 4.5 is becoming the real bar, not 4.
2026 data
47%

will not use a business with fewer than 20 reviews, and only 9% would use one with 5 or fewer.

What this means: Volume buys you the right to have a rating at all. Get to 20 before you worry about the average.
2026 data

The rating bar, 2025 against 2026

Share of US consumers who set each minimum rating. Same survey, 2 consecutive years.

Need 4 stars or more, 202668%
Need 4 stars or more, 202555%
Need 4.5 stars or more, 202631%
Need 4.5 stars or more, 202517%
Read it as: Both bars moved up together in a single year. Standing still means falling behind.
Worth knowing: One finding that surprises people: purchase likelihood does not keep climbing all the way to 5 stars. In the research it peaks somewhere between 4.0 and 4.7 and then falls back, because a perfect score with nothing negative in it reads as too good to be true. A 4.6 with some honest criticism in the text outperforms a suspicious 5.0.
Worth knowing: Getting to 20 reviews is a volume problem, not a quality problem, and it is the part most businesses put off. A steady review request sequence after each job does more for the rating than anything you write.
Freshness

How old a review can be before it stops counting.

This is the figure that catches out businesses with a good rating they stopped adding to.

74%

only care about reviews written in the last 3 months.

What this means: Your effective rating is your recent rating. Older reviews still show, but they stop counting for most people.
2026 data
20+

reviews is the minimum most people want to see before they trust an average at all.

What this means: Volume and freshness work together. A handful of old reviews fails on both counts.
2026 data
Worth knowing: This is the figure that catches businesses out. A 4.8 average built 2 years ago and left alone reads as a business that may have closed. A slightly lower average with reviews from last month reads as a business that is open and busy.
Replying

What replying to reviews does.

Replying is the only part of your reputation you fully control, and it is the clearest effect in the whole data set.

89%

expect business owners to respond to reviews.

What this means: Not replying is itself a message, and most people read it as not caring.
2026 data
80%

are likely to use a business that responds to all of its reviews.

What this means: The word all is doing the work here. Picking and choosing scores far lower.
2026 data
42%

are unlikely to use a business that never replies.

What this means: Silence costs you a share of customers before they ever contact you.
2026 data
81%

expect a reply within a week, and 19% expect one the same day. Same day expectation tripled from 6% in 2025.

What this means: The window is closing. A reply a month later is read as no reply.
2026 data

How much replying actually moves people

Share of US consumers likely to use a business, by what that business replies to. 2026.

Replies to all reviews80%
Replies to negative only47%
Replies to positive only45%
Read it as: Replying to everything is worth roughly 33 points more than replying selectively.
Worth knowing: Replying to every review across a handful of locations is manageable by hand. Across 50 it is not, which is why brands move to one review inbox for every location with AI drafted replies that a person approves before they publish.
Platforms

Where people actually read reviews.

The places people look changed more in the last year than in the 5 before it.

Where people looked for reviews, 2025 against 2026

Share of US consumers using each source. People could pick more than 1.

AI tools, 202645%
AI tools, 20256%
Google, 202671%
Google, 202583%
Facebook, 202649%
Facebook, 202540%
Read it as: AI went from a rounding error to nearly half of people in 1 year, while Google gave up 12 points.
45%

used ChatGPT or another generative AI tool to find local recommendations, up from 6% in 2025. ChatGPT on its own accounts for 31%.

What this means: This is the single biggest year on year move in the data. Worth watching rather than acting on blindly.
2026 data
71%

used Google to find reviews, down from 83% in 2025.

What this means: Google is still the biggest single source by a wide margin. It is just no longer the only one.
2026 data
49%

used Facebook to find reviews, up from 40% in 2025.

What this means: Your Facebook page reviews are not a leftover from 2015. They went up while Google went down.
2026 data
42%

now trust AI platforms as much as they trust traditional review sites.

What this means: People are starting to accept an AI summary in place of reading the reviews themselves.
2026 data
Worth knowing: AI answers are assembled from what is published about you, which means your reviews feed them. You can see what the engines currently say with a free AI visibility check.
AI and fake reviews

AI written reviews, fake reviews and the law.

Fake review rates are rising, AI made them cheaper to produce, and since late 2024 there has been a federal rule in the United States with real penalties attached.

82%

now read the AI written summary that sits above the reviews, and 23% decide on that summary alone without opening a single review.

What this means: For almost a quarter of people the summary is the review. What it says is decided by your whole review history, not your best review.
2026 data
87%

say they cannot reliably tell a genuine review from one written by AI.

What this means: You cannot expect customers to defend themselves. The platforms and the law are doing that work.
2024 data
91%

of shoppers say they are concerned about AI written fake product reviews.

What this means: Concern is near universal, which is why platforms are getting more aggressive about removals.
2026 data
97%

of the people using AI to find a business still check real reviews at least sometimes, and 42% always do.

What this means: AI has not replaced reviews. It has added a layer on top of them that you also have to be good in.
2026 data

Share of reviews submitted to Trustpilot that were fake

Trustpilot measured data, full years 2023 and 2024, then first half of 2025 and 2026.

First half of 202610%
First half of 20258%
Full year 20247.4%
Full year 20236.1%
Read it as: The fake review rate on 1 large platform has gone up every year measured, and has now reached 1 in 10 submissions.
7.8m

fake reviews were removed by Trustpilot in 2025, and 91% of those were caught automatically rather than by a person.

What this means: Detection is automated and getting harder to game. Buying reviews is a worse bet every year.
2026 data
14%

of reviews in home, legal and medical categories were found to be extremely suspicious and likely fake in a study of those sectors. About 3% of all the reviews checked were produced with AI.

What this means: Some categories are far dirtier than the average. Home services is one of the worst.
2024 data
$53,088

is the maximum civil penalty per violation under the FTC rule on consumer reviews, for a business that knowingly breaks it.

What this means: The penalty is per violation. A campaign of a few hundred fake reviews carries very large theoretical exposure.
2026 amount
10

companies were sent warning letters by the FTC in December 2025, and the first cases brought under the rule were settled in 2026.

What this means: This stopped being theoretical. The rule is being enforced.
2026 data
Worth knowing: The FTC Rule on the Use of Consumer Reviews and Testimonials took effect on 21 October 2024. It bans fake and AI written reviews, reviews by people who never used the business, buying positive or negative reviews, undisclosed reviews by insiders and their close family, company controlled review sites posing as independent, and suppressing honest negative reviews through legal threats. Review hijacking was proposed but was not part of the final rule. This is general information and not legal advice.
Worth knowing: If suspicious reviews are showing up on your own profiles, a negative and suspicious review monitor will flag them as they land, which matters because reporting one works best while it is recent.
Revenue

What a rating is worth in money.

These are the most quoted figures in the subject. They are real, they point the same way, and every one of them is older than it looks.

Worth knowing: This is the part of the subject where the numbers are oldest. The studies below are real and they are the ones almost every article quotes, but the newest was run in 2019 and the most famous used data from 2003 to 2009. They are shown here with the year of the research attached so you can weigh them properly.
5 to 9%

more revenue for each 1 star a restaurant gained on Yelp. This is the single most quoted figure in the subject.

What this means: Read the scope before you use it. The effect held for independent restaurants and was close to zero for chains.
2011 study
19 points

more evening slots sold out after an extra half star, in a separate peer reviewed study of San Francisco restaurants.

What this means: A second team found the same direction independently, which is why the effect is taken seriously despite its age.
2012 study
52%

above average revenue for businesses with more than 9 fresh reviews. The word fresh matters. It meant reviews from the last 90 days.

What this means: This is a recency finding as much as a volume one, and recency is the part usually dropped when it is repeated.
2019 study
35%

above average revenue for businesses that replied to at least a quarter of their reviews.

What this means: Replying is cheap compared to almost any other marketing spend, and it is fully in your control.
2019 study
270%

higher purchase likelihood for a product showing 5 reviews than the same product showing none.

What this means: Going from 0 reviews to a handful is the biggest single jump you will ever get. Everything after that is smaller.
2017 study
First 5

reviews do most of the work. Nearly all of the lift happens inside the first 10, and the bulk of it inside the first 5.

What this means: If a location has 2 reviews, getting it to 10 beats getting your best location from 80 to 100.
2017 study
Worth knowing: The chain finding is the one worth pausing on if you run more than 1 location. The Yelp study found a strong effect for independent restaurants and no meaningful effect for chains, and the likely reason is that people already know what to expect from a chain. It does not mean reviews stop mattering for a multi location brand. It means the rating is doing a different job, closer to deciding which of your branches someone drives to than whether they trust the brand at all. That is why per location reporting is worth more to a brand than a single company wide average, and it is what per location reporting is for.
What to do

What to do with all this.

Read together, the figures above point at the same 6 actions, cheapest first.

1. Get every location to 20 reviews

47% will not use a business with fewer than 20 reviews, and only 9% would use one with 5 or fewer. Volume comes before average. A standing request sequence after each job is the cheapest way there. Review generation.

2. Reply to all of them, not the easy ones

Replying to everything scores 80%. Replying only to the good ones scores 45%. The gap is bigger than most rating improvements you could buy. AI review replies with approval before publishing.

3. Reply within a week, and prefer the same day

81% expect a reply inside a week and same day expectation tripled in a year. Alerts matter more than good intentions here. One inbox for every location.

4. Keep reviews arriving, because 74% only count the last 3 months

A rating built 2 years ago and left alone stops working. A steady trickle beats a burst followed by silence.

5. Watch for suspicious reviews on your own profiles

Fake review rates are rising and the fastest removals are the ones reported while the review is fresh. Negative and suspicious review monitoring.

6. Track the rating per location, not as one company average

A brand average hides the 3 branches dragging it down. Per location is the only view that tells you where to act. Per location reporting and the wider local SEO statistics for context.

Sources

Where every figure came from.

Primary research from the publishers who ran the studies, plus the regulator’s own rule text. Nothing on this page is taken from another round-up. Every figure carries the year of the research rather than the year it was republished.

1

BrightLocal

Local Consumer Review Survey 2026, a representative panel of 1,002 US adults, published February 2026. This is the backbone of the page and the source of every reading, rating, recency, replying and platform figure, including the 2025 comparisons. Also the source of the AI review summary figures. Our own write-up of it is here: Local Consumer Review Survey.

2

Trustpilot

Trust Report 2025, covering 2024, for the fake review removal rate and the automated detection share. Full year 2025 results and first half 2026 results, published by Trustpilot Group plc, for the current platform scale, the 2025 removal total and the rising share of submissions found to be fake.

3

US Federal Trade Commission

Rule on the Use of Consumer Reviews and Testimonials, 16 CFR Part 465, announced August 2024 and effective 21 October 2024, for the prohibited conduct and the civil penalty amount. December 2025 warning letters and 2026 enforcement actions for the current state of enforcement. General information, not legal advice.

4

Reputation

Consumer survey fielded by the research firm Prodege with 2,000 US consumers in autumn 2024, for the figures on telling genuine reviews from AI written ones.

5

TrustedSite

State of Ecommerce Trust 2026, for consumer concern about AI written fake product reviews.

6

The Transparency Company

The High Cost of Review Fraud, December 2024, an analysis of reviews in home, legal and medical services, for the share found to be likely fake and the share produced with AI.

7

Harvard Business School

Michael Luca, Reviews, Reputation and Revenue: The Case of Yelp.com, working paper 12-016, using Seattle restaurant data from 2003 to 2009. The 5 to 9% finding and its independent restaurant scope come from here.

8

The Economic Journal

Michael Anderson and Jeremy Magruder, 2012, the peer reviewed San Francisco study that independently found the same direction, reported here as the half star and sell out figure.

9

Womply

The impact of online reviews on small business revenue, published July 2019 using 2018 data, for the fresh review revenue figures and the replying to a quarter of reviews figure.

10

Medill Spiegel Research Center, Northwestern University

How Online Reviews Influence Sales, published June 2017 with PowerReviews and built on 2015 transaction data, for the purchase likelihood figures, the first 5 reviews finding and the rating range where purchase likelihood peaks.

11

Whitespark

Local Search Ranking Factors, 2026 edition, for the weight practitioners give review signals in the local pack, in local organic results and in AI search visibility. This is a survey of practitioners, not data published by Google. Google does not publish ranking weights.

12

Yelp and Tripadvisor

Company reported figures for cumulative review counts and monthly usage, taken from their own investor and transparency reporting rather than from third party round-ups.

FAQs

Review statistics, answered.

The questions people ask most, answered with the figures above.

How many people read online reviews before choosing a business?

97% of people read reviews for local businesses, and 41% say they always read them while browsing, up from 29% a year earlier. Those come from BrightLocal’s 2026 survey of 1,002 US adults. They are self-reported, so treat them as what people say they do rather than logged behaviour.

What star rating do you need in 2026?

68% will only use a business rated 4 stars or higher, up from 55% in 2025, and 31% will only use one rated 4.5 or higher, up from 17%. The practical target is 4.5 or above. Going for a perfect 5.0 is not worth it, because purchase likelihood peaks between 4.0 and 4.7 and a flawless score reads as suspicious.

How many reviews does a business need?

47% will not use a business with fewer than 20 reviews and only 9% would use one with 5 or fewer, so 20 is the working floor. The research on purchase likelihood points the same way. Most of the lift arrives inside the first 5 to 10 reviews, so moving a weak location from 2 reviews to 10 is worth more than adding 20 to a location that already has 80.

How old can a review be before it stops counting?

About 3 months. 74% of people say they only care about reviews written in the last 3 months. A high average that stopped 2 years ago reads like a business that may have closed, which is why a steady trickle of new reviews beats an old burst.

Does replying to reviews actually change anything?

It is the clearest effect in the data. 80% are likely to use a business that replies to all of its reviews, against 45% for one that replies only to positive reviews and 47% for one that replies only to negative ones. 42% say they are unlikely to use a business that never replies, and 89% expect a reply at all.

How fast do you have to reply?

81% expect a reply within a week. 19% now expect one the same day, which tripled from 6% a year earlier. Inside a week is the standard to hold, and the expectation is getting tighter each year.

Are people using AI instead of reading reviews?

Both, for now. 45% used ChatGPT or another generative AI tool to find local recommendations, up from 6% a year earlier, and 82% read the AI written summary that sits above the reviews. But 23% decide on the summary alone, while 97% of AI users still check real reviews at least sometimes and 42% always do.

How common are fake reviews?

On Trustpilot, 10% of reviews submitted in the first half of 2026 were found to be fake, up from 8% a year earlier, 7.4% in 2024 and 6.1% in 2023. Trustpilot removed 7.8 million in 2025 and caught 91% of them automatically. In home, legal and medical services specifically, one study found nearly 14% of reviews were likely fake.

Is it illegal to buy reviews in the United States?

Yes. The FTC rule on consumer reviews took effect on 21 October 2024. It bans fake and AI written reviews, buying positive or negative reviews, undisclosed reviews from insiders and their close family, and suppressing honest negative reviews. Penalties run to $53,088 per violation for knowing violations, the FTC sent warning letters to 10 companies in December 2025, and the first penalties landed in 2026. This is general information, not legal advice.

How much are reviews worth in revenue?

The honest answer is that the good studies are old. A Harvard Business School paper found a 1 star rise on Yelp raised restaurant revenue 5 to 9%, but that used 2003 to 2009 data and the effect held for independent restaurants rather than chains. Womply found businesses with more than 9 fresh reviews earned 52% above average, using 2018 data. They point in a consistent direction, which is why they are still quoted, but none of them is a 2026 measurement.

Where do people read reviews now?

Google is still the biggest single source at 71%, though that fell from 83% in a year. Facebook rose to 49% from 40%. Generative AI tools reached 45% from 6%. People use an average of 6 review sites before deciding, so being strong only on Google is not enough.

How much do reviews count towards local rankings?

Nobody outside Google knows exactly. Google says local results are based mainly on relevance, distance and popularity, and it does not publish percentage weights. The percentages you see quoted come from a survey of local SEO practitioners, whose most recent edition puts review signals at about 20% of what drives the local pack. Treat that as informed opinion rather than fact.

These are averages. Your own rating is the number that counts.

Every figure on this page describes people in general. What matters is where each of your locations sits in its own town, and that takes a few minutes to find out.

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