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Online reviews still matter in 2026, but shoppers are using them differently. Recent surveys show people checking reviews at several points, from comparing options to the final click. They also show people using AI to narrow choices, while mostly keeping the final decision for themselves. Trust is shifting toward detail, visual proof and signs of real people.
The evidence has limits. Most of it is U.S. survey data, plus one global retail study and UK government work on fake reviews. These are self-reported answers, one experiment and one modeled estimate. They don’t add up to a single worldwide trend line, so each figure below carries its source, date and sample.
How do online reviews affect buying decisions in 2026?
Reviews work as a checkpoint that shoppers return to, not a one-time lookup. They can also stop a purchase as easily as start one.
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Reviews still carry weight
ConsumerAffairs surveyed 2,000 Americans through Pollfish in December 2025 (poststratified by age and gender). About 77% said online reviews were important, and one in three said they would not buy without reading them first. About 80% said reviews had persuaded them to avoid a product or service. That last figure is the more telling one: reviews act as a brake as well as a nudge.
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Shoppers check at more than one stage
Clutch’s survey of 400 U.S. consumers, released February 4, 2026, describes review checking as a repeated habit:
- 96% checked reviews before a first-time purchase.
- 47% always checked before buying from an unfamiliar brand or product.
- 72% read reviews while comparing options.
- 69% checked again before finalizing the purchase.
- Only 15% trusted star ratings alone.
Clutch analyst Jeanette Godreau put it this way: “Customer reviews remain a key trust signal, but consumers no longer take them at face value. Shoppers want authenticity, detail, and real experiences before deciding.”
These two surveys use different samples and questions, so don’t read them as the same measurement or as proof that every shopper always checks reviews. Clutch’s sample is small and U.S.-only, and it isn’t broken out by product category.
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The surveys point to AI as a research assistant, not a replacement buyer.
AI helps narrow choices more than it makes them
In a January 2026 Gartner survey of 322 U.S. consumers, 31% were willing to let AI narrow choices for household supplies and 28% for personal electronics. Willingness to let AI actually make the purchase decision reached only 11% across lower-stakes categories. Gartner VP Analyst Kate Muhl said: “Consumers are not looking to outsource shopping decisions to AI. They want AI to help them find better information, compare prices, identify deals and narrow choices, while keeping final decision-making control for themselves.”
Reviews are now part of what people ask AI for
A National Retail Federation and IBM study published January 7, 2026 draws on IBM research with 18,000 consumers worldwide. It found 41% used AI assistants to research products, 33% to look for reviews and 31% to search for deals. This is global, not U.S.-only, and it isn’t directly comparable with Gartner’s U.S. figures.
AI answers still need checking
In a separate Gartner survey of 846 U.S. consumers (November–December 2025), people who had used AI for a recent purchase reported friction. 54% said they had to double-check all the information the tools gave them, and 62% said the information ended up wasting their time. Don’t merge this sample with the 322-person survey; they answer different questions.
The practical consequence for review reading: an AI summary of 5,000 reviews is a convenient map, and it can still miss or flatten details. ConsumerAffairs’ guidance says: “AI-generated summaries can be a helpful introduction to a group of reviews, but they’re no replacement for human analysis.”
What do current findings say about fake reviews and trust?
How common are fake reviews?
The most rigorous public figures come from the UK Department for Business and Trade (2023). It defines a fake review as one that doesn’t reflect a genuine experience and is designed to mislead. For three categories on e-commerce platforms widely used by UK consumers (consumer electronics, home and kitchen, and sports and outdoors), it estimated 11%–15% of reviews were fake. That is a UK estimate from 2023 for those categories, not a current rate for every retailer or country.
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Do fake reviews change what people buy?
In the report’s experimental shopping task, the effect depended on how convincing the fakes were:
| Type of fake review | Effect on purchase likelihood |
|---|---|
| Subtle, plausible fake reviews | 3.1% more likely to buy |
| Poorly written fake reviews | 5.3% less likely to buy |
The effects varied by price and category, and the report itself calls the picture nuanced. The takeaway is that polished deception is the bigger risk, because clumsy fakes can backfire on the people posting them.
Can you spot fakes by how they read?
Not reliably. In the data the government team examined, reviewer overlap and network features predicted fake reviews better than wording alone. Individual shoppers rarely see those network signals, so a review that reads well isn’t evidence of being real.
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What does fake-review harm cost?
The same report modeled annual consumer harm from fake product-review text at £50 million to £312 million. This is a modeled estimate for the UK, and it leaves out several kinds of harm, so treat it as a range rather than a measured total.
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Gartner surveyed 1,006 U.S. consumers in May–June 2026 and published results on September 22, 2026. 35% said they relied on influencers less for shopping recommendations because of AI-generated content, and 43% said they relied more on real people. Gartner Sr Principal Analyst Emily Weiss said: “The answer is not to avoid AI. The answer is to use it thoughtfully.”
Clutch’s release points the same way: respondents valued detail, visual proof and authenticity, and suspected AI involvement lowered trust for many of them. These are individual survey results. They don’t show that most consumers reject AI content or influencers, only that a sizable share is adjusting.
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ConsumerAffairs senior research editor Jon Bortin said: “Good consumer research isn’t about finding a product or service fastest — it’s about understanding the full context around a purchase, including benefits, risks, trade-offs and other users’ experiences.” The checks below follow that idea, using the guidance in ConsumerAffairs’ report plus the fake-review findings above.
| Check | What to do |
|---|---|
| Source | Compare independent review platforms, retailer pages, brand-owned pages and user communities. Each reaches a different audience. |
| Depth | Favor written explanations, photos and concrete use conditions over a bare star score. |
| Pattern and recency | Look for themes repeated across many reviews, not one glowing or furious account. Check dates and which model or version was reviewed. |
| Incentives and authenticity | Note disclosed incentives, verified-purchase labels, and copied or clustered posting. None of these alone guarantees a review is genuine. |
| Decision fit | Ask whether reviewers share your use, constraints and risks. Read the negatives for omissions and trade-offs, not only satisfaction. |
| Fine print | Check warranty, return and subscription terms that reviews often skip. |
| AI summaries | Use them to find themes, then confirm important details in original reviews and the product page. |
A simple routine: skim the AI or platform summary for themes, read a handful of the most detailed three-star and negative reviews, compare against a second source, then confirm the specifics you care about against the manufacturer’s information.
What these findings can’t tell you
- There is no single global, year-over-year trend line for review influence. Surveys differ in sample, wording and method.
- Survey figures are what people say they do, not observed behavior. The UK experiment is the only behavioral result here, and it was a simulated shopping task.
- The fake-review prevalence figures date from 2023 and cover three categories, so they may not reflect today’s platforms.
- The Clutch figures come from a press release summarizing 400 U.S. consumers, and the NRF and IBM figures come from a worldwide sample.
The Bottom Line
Treat reviews as evidence to cross-check, not a verdict. Use AI to find themes and narrow options, then read some original reviews and keep the final call yourself. Polished praise deserves as much scrutiny as an obvious rant.
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