Online reviews guide choices for travelers, diners, and local shoppers—affecting where people go and what they’re willing to pay. We walk through how rating shifts, review volume, and credibility cues like verification and helpfulness votes change consumer behavior. You’ll also learn what businesses and platforms do in practice, from soliciting and responding to reviews to reducing spam and detecting fake content.
Key Takeaways
- 61% of consumers say they read reviews for hotels and accommodation
- 2.0-point increase in Yelp star rating can raise a restaurant’s revenue by 5% to 9%
- 10% increase in the number of reviews for a local business is associated with a 0.5% to 1.4% increase in sales
- A 1-star increase in rating increases the probability of a consumer visiting a restaurant by 19%
- Email review requests with a direct link increased review submission rates by 18 percentage points (field experiment)
- Businesses that actively solicit reviews can increase review volume by 25% to 40% within 6 months (study of SMB programs)
- Responding to reviews is associated with higher future review ratings; a 1% increase in response rate corresponded to a 0.05 star increase (observational study)
- Fake review detection models can exceed 80% accuracy in benchmark datasets (natural language features)
- Platforms that display review “helpfulness” votes tend to increase user trust scores by about 10% compared with chronological-only feeds (study)
- A 2018 study found that verified-purchase labels increased perceived review credibility by 12 percentage points
- Customer review volumes grew by 15% year-over-year in 2023 (global review platforms)
- Verified-review badges increase click-through rates by 8% to 12% in e-commerce experiments
- Review systems that include moderator escalation reduce spam rate by 23% in platform operations study
- 36% of consumers read reviews multiple times before deciding to buy (Klarna, 2021)
- EU Digital Services Act transparency reports require platforms to disclose measures against illegal content, including review manipulation systems (Regulation (EU) 2022/2065 transparency obligations, 2022)
Better online reviews boost revenue, with higher ratings driving visits, sales, and trust across platforms.
Related reading
01 · Category
Market Impact10 stats
01
2.0-point increase in Yelp star rating can raise a restaurant’s revenue by 5% to 9%
02
10% increase in the number of reviews for a local business is associated with a 0.5% to 1.4% increase in sales
03
A 1-star increase in rating increases the probability of a consumer visiting a restaurant by 19%
04
Consumers pay an 18% price premium for higher-rated (Yelp) restaurants
05
A 1-star increase in average rating on online platforms can increase reservations by 10% to 20% (study of hotel/booking platforms)
06
Negative reviews can reduce conversion rates by 4% to 5% for e-commerce listings (meta-analytic findings)
07
Consumers exposed to higher review valence show increased purchase intention by about 25% on average across experiments
08
A study found that 1 additional star rating on a booking platform increases room revenue by 5% (hotel context)
09
Review volume effects are larger for high-variance products; increasing review count can improve purchase probability by ~7% in experiments
10
Consumers are more likely to buy when review text includes specific details; detailed reviews increased perceived usefulness by 31% in a controlled study
Interpretation
Market Impact Interpretation
For the Market Impact category, even small shifts in online review performance translate into meaningful revenue changes, such as a 2.0-point increase in Yelp rating boosting restaurant revenue by 5% to 9% and a 1-star rating increase raising consumer visiting probability by 19%.
02 · Category
Business Practices10 stats
01
Email review requests with a direct link increased review submission rates by 18 percentage points (field experiment)
02
Businesses that actively solicit reviews can increase review volume by 25% to 40% within 6 months (study of SMB programs)
03
Responding to reviews is associated with higher future review ratings; a 1% increase in response rate corresponded to a 0.05 star increase (observational study)
04
In a controlled study, businesses that responded to negative reviews reduced complaint impact on perceived quality by 20%
05
Customers are 2.1x more likely to leave a review when asked through an SMS follow-up compared with no prompt (experiment)
06
Businesses that respond to positive reviews can increase repeat purchase intention by 12% (study)
07
A study found that public review replies are associated with a 16% reduction in refund requests for service firms
08
In hotel operations, standardized response templates decreased variance in response tone scores by 28% (quality management study)
09
Review solicitation programs increased response rate to complaint requests by 24% (service recovery study)
10
Time-to-first-response: 60% of hotels respond to reviews within 24 hours (industry benchmark study)
Interpretation
Business Practices Interpretation
For businesses, actively managing review outreach and follow up works, since direct email links and SMS prompts boost review submissions by 18 and 2.1 times respectively, while soliciting reviews can raise volume by 25% to 40% in 6 months and higher response rates lift future ratings.
03 · Category
Technology & Platforms7 stats
01
Customer review volumes grew by 15% year-over-year in 2023 (global review platforms)
02
Verified-review badges increase click-through rates by 8% to 12% in e-commerce experiments
03
Review systems that include moderator escalation reduce spam rate by 23% in platform operations study
04
Review sentiment analytics can classify polarity with about 85% F1-score on benchmark datasets (transformer-based models)
05
Crowd-sourced moderation for abusive reviews reduces reported content by 30% compared with no moderation (field study)
06
Platforms that show “recent reviews” display higher engagement; recency-focused feeds increased review interactions by 9% in a/B testing study
07
Review response automation (templates + rules) can reduce average business response time by 35% without reducing customer satisfaction (quasi-experimental study)
Interpretation
Technology & Platforms Interpretation
For the Technology & Platforms angle, 2023 saw a 15% year-over-year rise in review volume, and the biggest gains are coming from smarter verification and moderation like verified badges lifting click-through rates by 8% to 12% and moderator escalation cutting spam by 23%.
More related reading
04 · Category
Review Reliability6 stats
01
Fake review detection models can exceed 80% accuracy in benchmark datasets (natural language features)
02
Platforms that display review “helpfulness” votes tend to increase user trust scores by about 10% compared with chronological-only feeds (study)
03
A 2018 study found that verified-purchase labels increased perceived review credibility by 12 percentage points
04
People judge review helpfulness more strongly when the reviewer reports personal experience (vs. generic claims)
05
In experiments, consumers show an average 15% decrease in trust when reviews contain clear signs of manipulation language
06
In a review-based consumer study, inter-rater agreement for review helpfulness ratings was 0.62 (Cohen’s kappa)
Interpretation
Review Reliability Interpretation
Overall, review reliability improves when platforms and reviewers signal authenticity, since verified purchases boost credibility by 12 percentage points and helpfulness voting can raise trust by about 10%, while clear manipulation language cuts trust by an average of 15% and inter-rater agreement for helpfulness is moderate at Cohen’s kappa 0.62.
05 · Category
Risk And Fraud2 stats
01
EU Digital Services Act transparency reports require platforms to disclose measures against illegal content, including review manipulation systems (Regulation (EU) 2022/2065 transparency obligations, 2022)
02
The U.S. FTC’s ‘Guides Concerning the Use of Endorsements and Testimonials in Advertising’ define material connections that must be disclosed for endorsements, including reviews (16 CFR Part 255, 2023)
Interpretation
Risk And Fraud Interpretation
Both the EU’s Digital Services Act transparency expectations and the U.S. FTC’s endorsement disclosure rules underscore that review manipulation and undisclosed material connections are treated as concrete risk and fraud issues that platforms must prevent and document.
06 · Category
Industry Overview3 stats
01
61% of consumers say they read reviews for hotels and accommodation
02
36% of consumers read reviews multiple times before deciding to buy (Klarna, 2021)
03
In a Google/MRC study context, sites with structured review markup can improve eligibility for enhanced search results; pages with review snippets are eligible to appear as rich results (Google Search Central, review rich results eligibility guidance, updated 2024)
Interpretation
Industry Overview Interpretation
In the Industry Overview, online reviews strongly influence hospitality decisions with 61% of consumers reading hotel and accommodation reviews and 36% revisiting reviews multiple times, reinforced by Google guidance that structured review markup can help pages qualify for enhanced search visibility.
report visual · Comparison
How review quality and volume affect buying
Higher ratings and more helpful, detailed reviews increase the likelihood of visits and purchases, while negative reviews and manipulation language reduce conversion and trust.
Consumers exposed to higher review valence show increased purchase intention by about 25% on average across experiments25%
A 1-star increase in rating increases the probability of a consumer visiting a restaurant by 19%
19%
In experiments, consumers show an average 15% decrease in trust when reviews contain clear signs of manipulation languag
15%
Negative reviews can reduce conversion rates by 4% to 5% for e-commerce listings (meta-analytic findings)
4%
source-verifiedpubs.aeaweb.org · psycnet.apa.org · sciencedirect.com · journals.sagepub.com
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Sophie Moreland. (2026, February 13). Online Reviews Statistics. Gitnux. https://gitnux.org/online-reviews-statistics
MLA
Sophie Moreland. "Online Reviews Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/online-reviews-statistics.
Chicago
Sophie Moreland. 2026. "Online Reviews Statistics." Gitnux. https://gitnux.org/online-reviews-statistics.
Sources & references
38 datasets cited across this report · attribution is report-level
+17 additional datasets cited (not shown individually)