Review Schema: Using Customer Reviews to Boost AI Visibility
Customer reviews are one of the most powerful trust signals AI engines use when deciding which businesses to recommend. But reviews that exist only on Yelp, Google, or Trustpilot are not automatically incorporated into your site's AI citation signals. Review schema, specifically AggregateRating JSON-LD markup, brings your verified review data into your own site's structured data where AI engines can read it alongside your other brand signals.
How AI engines use review signals
When an AI engine answers a recommendation query, it evaluates candidate businesses across multiple dimensions: location, category match, credential quality, and social proof. Review scores are the primary social proof signal available in structured form. A business with a 4.8 average from 350 reviews is substantially preferred over an equivalent business with a 3.9 average from 20 reviews, all else being equal. AI engines also consider review volume because a higher number of reviews indicates a more established business with broader customer experience. Both rating and volume should be reflected in your AggregateRating schema.
AggregateRating schema implementation
AggregateRating schema is added to your page's JSON-LD, nested within your Organization, LocalBusiness, or Product schema. The required properties are: ratingValue (the average rating, e.g. 4.8), ratingCount or reviewCount (the total number of ratings or reviews), bestRating (the maximum possible rating, usually 5), and worstRating (the minimum, usually 1). Optionally include itemReviewed to specify what is being rated. Use data from a verified review platform (Google Reviews, Yelp, Trustpilot, G2) and only include ratings where you have at least a minimum threshold of reviews. Google requires at least one review for AggregateRating rich results to display.
Which review platform to reference
Reference the review platform where you have the highest number of verified reviews and the most credible source in your industry. For local businesses, Google Reviews is the highest-authority source. For e-commerce brands, Trustpilot or Amazon. For SaaS, G2 or Capterra. For healthcare, Healthgrades or Zocdoc. Do not invent or inflate review numbers: AI engines and Google's schema validation detect discrepancies between your schema data and the actual data on the linked platform. Accurate schema that matches your genuine review profile is both the ethical and the most effective approach.
Individual Review schema for testimonials
In addition to AggregateRating, you can mark up individual customer testimonials using Review schema. Each Review entity includes: author (a Person schema with the reviewer's name), reviewRating (a Rating with ratingValue and bestRating), reviewBody (the text of the review), and datePublished. Only mark up reviews that are genuine and from real customers. Do not mark up invented testimonials or quotes that have been fabricated: AI engines and Google's quality systems penalize sites with fake Review schema. If you have permission from a customer to feature their testimonial, mark it up with Review schema and link the author's name to their verified review on the external platform where it was originally posted.
Managing your review presence for ongoing AEO
Review schema is only as strong as your underlying review profile. Make review generation a systematic part of your customer journey: send automated review request messages 24 to 48 hours after a purchase, service completion, or appointment. Make the review link direct (one click to the review form) rather than requiring navigation. Respond publicly to every review, positive and negative: AI engines interpret active review management as a trust signal. Monitor your aggregate rating monthly and run an AEO audit at /aeo/scores quarterly to confirm that your review schema is correctly implemented and that your review signal category is not flagging as a weakness.
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