Short answer: Yes — the words matter more than the stars. When someone asks for “a reliable plumber who handles emergency leaks,” the AI recommends the business whose reviews keep saying “quick response” and “emergency repair” — not the one with fifty generic five-stars. Review text is the evidence the bot quotes. A wall of “great service” gives it nothing to quote.
How the bot actually uses your reviews
Think about what the AI is doing when it answers “who should I call.” It’s matching the words in the question to evidence it can find. If the question asks for emergency leak help, it looks for businesses with evidence of emergency leak help. And the richest source of that evidence isn’t your website copy — it’s your customers describing what you did, in their own words.
One analysis of business profiles in AI search put it directly: “If a user asks for a ‘reliable plumber who handles emergency leaks,’ the AI will likely recommend a business whose reviews frequently mention ‘quick response’ and ’emergency repair’ over a business that simply has those terms in its meta description.” Businesses with specific, keyword-rich testimonials get cited more often than businesses with generic five-star ratings and no text.
A review-industry breakdown this month named vague reviews as one of the three mistakes sending customers to competitors: “great service” instead of “same-day AC repair in Phoenix.” And review audits keep finding the same thing — AI answers quote review text verbatim. Your customers’ words become the bot’s answer.
The star-rating trap
Most owners chase stars. Stars are visible, countable, and feel like the scoreboard. But a five-star rating with no text tells the AI almost nothing — it’s a number without a story. Forty detailed reviews at 4.8 stars will beat a hundred empty five-stars, because the detailed reviews give the bot something to match against the question and something to quote in the answer.
This is also why review platforms matter less than review content here. The question isn’t just where your reviews live — it’s whether the words in them describe the work you actually want to be recommended for.
The review-request script (free, repeatable)
You can’t write your own reviews. But you can ask for better ones — and most owners never do. After a job, when the customer is happy, ask them to mention two things: what you did and where. That’s it.
“If you have a minute to leave a review, it really helps when folks mention the job — like the water heater swap — and the town. Just a sentence is perfect.”
That’s the whole script. You’re not scripting fake reviews or stuffing keywords — you’re asking real customers to include the specifics they’d naturally mention anyway. “Mike replaced our water heater the same day it died, here in Mesa” is a quotable piece of evidence. “Great service!!” is not.
What to actually do about it
- Read your last twenty reviews. Count how many mention a specific job or a specific town. If the answer is “almost none,” you have the generic-review problem.
- Start the two-thing ask this week. What you did, and where. Every finished job, every happy customer. It compounds.
- Don’t neglect the unhappy ones. A detailed critical review is still evidence the bot reads — respond to it well, and the response becomes part of the record too.
- Keep it honest. Never write reviews yourself, never pay for them, never hand customers pre-written text. The specifics have to be real — fabricated detail is both against every platform’s rules and, increasingly, detectable.
What’s actually known — and what’s not
Known: AI answers quote review text verbatim, and businesses whose reviews describe specific jobs in specific places get cited more often than businesses with generic ratings. The review-request script — ask for the job and the town — is a free, ownable fix no competitor can block.
Not known: nobody has published a controlled test measuring naming rate against review-text specificity for trades businesses — the evidence is observational and mechanism-level, not a measured curve. What’s missing is someone running the test: same market, similar star counts, specific-text versus generic-text reviews, measured naming rates. That’s a test worth running.
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