"One million percent." That was the answer from Eric Edelson, CEO of Fireclay Tile, when asked whether customer reviews affect AI recommendations. He's not alone. Brands across ecommerce categories have found that their review presence is one of the most direct levers they have over whether AI mentions them.
But the mechanism is not what most people think. It's not about star ratings. It's not about volume on a single platform. And it works differently for ChatGPT than it does for Perplexity or Google AI Overviews.
Here's what's actually going on.
How AI uses reviews (it's not what you think)
AI models don't just check your average star rating. They read reviews: the actual language people use to describe their experience with your brand.
This matters because two brands can both have 4.5 stars and tell completely different stories. "Great product, fast shipping" is positive but generic. "I've tried six [product type] brands and this is the only one that works for [specific use case]" is positive and specific. AI learns different things from each.
What AI extracts from reviews:
Sentiment and endorsement. Not just whether reviews are positive, but whether AI itself can synthesize a recommendation from them. There's a meaningful distinction between "customers seem happy" (neutral) and "customers consistently say this is the best option for X" (positive). AI Trust scoring rewards the latter.
Category association. Reviews that mention specific use cases ("perfect for sensitive skin", "best for trail running in wet conditions") help AI understand exactly what your brand is good for. This makes it more likely to mention you for specific queries, not just generic ones.
Sentiment consistency across platforms. AI draws from multiple sources. Consistent positive sentiment across Trustpilot, Google Reviews, Reddit, and niche forums creates a much stronger signal than a high volume of reviews on one platform.
Recency. Reviews from three years ago matter less than recent ones, particularly for AI Search tools like Perplexity and Google AI Overviews, which draw on live web data. Keeping a steady stream of recent reviews coming in keeps your signal fresh.
Where reviews matter most, and which platforms
Not all review platforms are equal in AI's eyes.
Google Reviews are indexed and surfaced heavily in Google AI Overviews. If you're targeting customers in Google, having recent, positive Google Reviews is probably the highest-leverage single action you can take.
Trustpilot is widely cited by Perplexity, which explicitly references it as a source in its answers for trust queries. Perplexity often includes a Trustpilot citation when someone asks "Is [brand] legit?" or "Should I buy from [brand]?"
Reddit is disproportionately influential. Analysis of AI citations across product categories consistently shows Reddit threads appearing far more often in AI-generated answers than you'd expect from their domain authority alone. Real customer experiences shared in communities like r/ecommerce, r/MakeupAddiction, r/running (and hundreds of niche subreddits) carry significant weight. Brands with organic Reddit discussion are better represented in AI responses.
Niche review sites specific to your category (whether that's Verified Reviews, Influenster, niche blogs, or category-specific communities) are often what separates well-covered brands from invisible ones. AI has been trained on the whole web, including specialist sources.
Your own site reviews matter less for AI Knowledge (training-data models), which weight third-party sources more heavily. They still count for AI Search tools, which do crawl your site, but the independent platforms carry more weight.
The Perplexity and Google AI Overviews difference
For AI Knowledge models (ChatGPT, Claude, Gemini), review impact takes months to materialize. These models were trained on a historical snapshot of the web. New reviews you collect today won't affect what ChatGPT says about you until the next time the model is retrained.
For AI Search tools (Perplexity, Google AI Overviews), the impact is much faster. Both crawl the live web, which means a wave of new positive Trustpilot reviews, a helpful Reddit thread, or a fresh review placement on a buying guide can show up in AI Search responses within days or weeks.
If you want faster results from your review strategy, focus efforts on platforms that AI Search tools cite: Google Reviews, Trustpilot, and active Reddit communities in your category.
What actually works (and what doesn't)
What works:
Getting customers to leave reviews on multiple platforms, not just one. Even modest review presence across three or four platforms creates a more consistent signal than a large volume on one.
Encouraging specific, detailed reviews over generic ones. "Great product" is less useful to AI than "I switched from [competitor] after six months and the quality is noticeably better". You can prompt specificity in follow-up emails without violating any platform's guidelines.
Responding to negative reviews. AI reads your responses. A thoughtful response to a 2-star review can neutralize its negative sentiment in ways that AI recognizes.
What doesn't work:
Gaming review volume with incentivised or fake reviews. This violates platform terms and, more practically, AI models are increasingly able to detect inauthentic review patterns.
Focusing only on your own site reviews. These matter for your conversion rate, but they're not what drives AI recommendations.
Treating reviews as a one-time task. Recency matters. A brand with 200 reviews from 2022 looks less active than one with 80 reviews from the past six months.
A practical review audit
To understand where you stand, run these searches and note what you find:
- "[your brand] reviews" in Google. What platforms appear on page one?
- Ask Perplexity "Is [your brand] worth it?" Does it respond with review data? What does it say?
- Search "[your brand]" on Reddit. Is there organic community discussion? What's the sentiment?
- Ask ChatGPT "What do customers say about [your brand]?" Can it answer confidently, or does it say it doesn't have enough information?
The gaps in those answers are your review strategy priorities.
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Written by Stu Miller, Founder of SeenByAI and CEO & Co-founder of Smart Insights. Stu has spent 16 years helping ecommerce businesses grow their digital marketing, and built SeenByAI after experiencing the AI visibility problem first-hand running his own Shopify store.
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