Direct answer: Type it. After any AI engine recommends a business — yours or a competitor’s — ask the follow-up: “Why did you recommend them?” The engine will often tell you which signals it used: the reviews it read, the directory it trusted, the facts that tipped the decision. It’s free competitive intelligence, it takes five minutes, and almost nobody in the trades is doing it.
Where this comes from
CI Web Group, a digital agency, published a checklist called “The AI Interview: What Answer Engines Ask About You.” Buried in it as step 5 of a 20-minute self-check is the single most useful sentence in the AI-visibility literature this year:
> “Ask a follow-up: ‘Why did you recommend them?’ The engine will often tell you which signals it used. Free competitive intelligence.”
The full checklist is worth your twenty minutes — open ChatGPT, Perplexity, and Gemini in separate tabs; type the exact question a homeowner would ask for your top three services and your top three cities (“Best plumber in Katy for a slab leak” beats “plumber Katy”); screenshot every answer; note who gets named, who gets skipped, and which facts about your business are wrong; run the follow-up; trace every error to its source; repeat monthly. (CI Web Group)
But the follow-up question is the hinge. Everything else is observation. The follow-up is interrogation.
Why it works
When ChatGPT, Perplexity, or Gemini recommends a business, it has just done retrieval: it searched the web, pulled sources, and synthesized. When you ask why, you’re asking it to narrate that retrieval. The engine will typically name the kinds of signals that carried weight — review volume and recency on a specific platform, a directory profile with complete service data, mentions across multiple independent sources, specific review language matching the question.
Is the explanation perfectly faithful to the model’s internal process? No — and you should know that. The “why” is itself a generated answer: a plausible reconstruction, not a system log. Treat it the way you’d treat a rival estimator explaining his bid: informative, self-serving in places, and most valuable when you cross-check it against the evidence (the actual citations in the first answer, which you screenshotted).
Even with that caveat, it’s the cheapest competitive intelligence in marketing. An agency will happily sell you a “competitor gap analysis” that tells you less — and bill you for the privilege.
The 5-minute procedure (do it tonight)
Minute 1 — Ask the buyer question. Open ChatGPT (or Perplexity, or Gemini — run all three if you have ten minutes). Type exactly what a homeowner would type. Not keywords. A question. Include the service and the city: “Who’s the best plumber in Katy for a slab leak repair?” Screenshot the answer.
Minute 2 — Ask why. Type: “Why did you recommend [the named business]?” Use their exact name from the answer. Screenshot what comes back. You’re looking for signal names: review platforms, specific review counts, directory profiles, “mentioned across multiple sources,” website content it quotes.
Minute 3 — Ask about the sources. Follow up with: “Which specific reviews or pages influenced that recommendation?” and “What would make you recommend a different company for this job?” The second question is the money question — it tells you the gap between you and the winner in the engine’s own words.
Minute 4 — Run it for your business. Now ask about yourself by name: “What do you know about [Your Company] in [City]?” Then: “Why didn’t you recommend them for [the job]?” Screenshot everything. The engine will often list what’s missing — thin reviews, no directory presence, conflicting hours, an unclaimed profile.
Minute 5 — Write down the three gaps. Not ten. Three. The three missing signals the engine named most specifically. Those are your work orders for the month.
What you’ll typically learn
About the winner: which review platform carried the recommendation (often Yelp or Google reviews, sometimes a directory you ignore), whether the win came from review language matching the question rather than review count, and whether the business is even good or just legible. Scott Tischler’s July 2026 experiment found the named winner often isn’t the best-reputed shop in town — it’s “the one that was legible to a machine.” The interrogation tells you which legibility won.
About yourself: SOCi’s 2026 Local Visibility Index puts business profile accuracy on ChatGPT and Perplexity at about 68% — versus 100% on Gemini, which pulls straight from Google Maps — so expect the engine’s picture of your business to contain errors. Wrong hours, wrong services, a closed flag, an old phone number. Each error has a source, and the source is fixable. As CI Web Group puts it: “Wrong hours on ChatGPT usually means wrong hours on a directory the engine trusts. Fix the source, not the chatbot. You cannot argue with the machine. You can only feed it better facts.”
About the game: run the same questions across engines and you’ll see different winners with different reasons — which is exactly what the “which AI should I care about” analysis shows. The interrogation teaches you that there is no single ranking to climb. There are separate evidence pools, and the follow-up shows you which pool each engine drank from.
Three traps to avoid
Trap one: treating one answer as the truth. AI answers are non-deterministic — the same question tomorrow can name a different business. Run the interrogation two or three times across a week before you spend money on what it told you. One screenshot is an anecdote; three is a pattern.
Trap two: arguing with the engine. Telling ChatGPT “that’s wrong, I’m better” accomplishes nothing. The engine restates what the web says. Change the web — the reviews, the directory data, the pages — and the answer follows on the next crawl. The checklist’s last line is the whole philosophy: “Repeat monthly. This is a vital sign now, like checking your reviews. Operationalize it with a LLM visibility measurement stack.”
Trap three: interrogating once and filing it. The signals change. In August 2026, ChatGPT’s retrieval shifted and Reddit’s citation share fell off a cliff — the “why” answers from July would have named sources that stopped mattering in September. Monthly is the cadence. Put it on the calendar next to the review check.
What to do with the intel
Convert each of the three gaps into a source fix, not a chatbot fix:
- “Recommended them because of 200+ recent Google reviews mentioning slab leak work” → run a review campaign asking specifically for job-type language, not stars.
- “Their Yelp profile lists slab leak detection as a service with photos” → complete your Yelp categories and upload real job photos.
- “Mentioned on three local ‘best of’ lists” → pitch the list publishers, or earn the mentions with work worth listing.
- “Your hours conflict across two directories” → fix the source directories; the engine can’t resolve what you haven’t resolved.
Then re-run the interrogation next month and watch the “why” change. When the engine starts naming your signals unprompted, you’re winning.
The line to remember
Your competitor’s recommendation is a case file, and the engine will read it to you if you ask. Five minutes, three questions, zero dollars — “Why did you recommend them?” is the cheapest market research in the trades right now. Run it tonight, monthly after that, and fix sources instead of arguing with machines.
FAQ
Q: Will the engine actually answer honestly? A: It will answer plausibly. The explanation is generated, not a system log — treat it as a strong lead, not gospel. Cross-check against the citations in the original answer (which is why you screenshot first).
Q: Should I do this in ChatGPT, Perplexity, or Gemini? A: All three — they use different source pools and often name different winners. The procedure is identical; the intelligence differs. That’s the point.
Q: What if it recommends me and I ask why? A: Even better. You learn which of your assets is actually carrying the win, so you can protect it — and you learn the exact language to repeat in reviews, profiles, and pages.
Q: Can I automate this? A: You can script the prompts, but the judgment — which gap matters, which error to fix first — is still yours. Monthly, by hand, twenty minutes. Some things shouldn’t be delegated to the thing you’re auditing.
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