This is the question every local business owner should be asking. When someone asks ChatGPT, Gemini, or Siri for a recommendation in your category, are you the answer?
If not, the next step is to diagnose the result before choosing a fix.
Understand what AI is looking for
Local AI answers can combine training data, web retrieval, search indexes, local data, and cited pages. The exact mix depends on the product and query.
Important
AI answers often narrow the visible field, but they do not expose one universal ranking formula. Save the answer, sources, provider, prompt, market, and date before diagnosing it.
That record lets you inspect what was visible and cited without pretending to know the system's internal reasoning.
Reviews are customer evidence, not a ranking formula
Review volume gives customers a larger sample of experiences. Recent, detailed reviews can also make the public profile more useful. Public evidence does not establish a universal weighting for volume, recency, platform diversity, sentiment, or response rate in AI recommendations.
Important
Never ask customers for a rating, keyword, service phrase, or positive review. The review belongs to the customer, and review count alone cannot explain an AI result.
Use a consistent, compliant review process. Ask every eligible customer under the same rule, make clear that reviews are optional, and never request a rating or specific wording. Track review activity to diagnose the process, not as a claimed AI ranking factor. For the evidence and policy boundaries, see How Reviews Support AI Visibility for Local Businesses.
Track recent review activity separately
Older reviews and recent reviews answer different customer questions. Lifetime reviews show the depth of the profile; recent reviews show whether customers have current experiences to inspect.
Track recency as an operating metric for the review program. Do not present it as a known AI ranking factor or proof of current service quality.
Pro Tip
A consistent, neutral request process is easier to audit than occasional campaigns. Investigate unexpected spikes for policy and data-quality issues.
Review language helps explain the service experience
Star ratings compress the experience into one number. Written reviews can give customers more context about the service, communication, timing, and outcome.
Reviews that say "John was professional, on time, and explained everything clearly" give future customers more detail than "Great!" The customer must choose the words without coaching.
Pro Tip
Use neutral prompts that invite detail without steering the rating: "If you have a moment to leave us a review, we'd appreciate hearing what stood out about your experience."
Clean up your citations
Customers and crawlers can encounter business information across the web. Correct material conflicts such as an old address, disconnected phone number, wrong hours, or inaccurate service list. A punctuation difference in the business name is not the same problem.
Audit your presence across Google, Yelp, Facebook, BBB, Apple Maps, Bing Places, and industry directories. Standardize your business name, address format, and phone number everywhere.
Citation cleanup is routine maintenance. Correct records that can send a customer to the wrong branch, number, or service page before adding more listings.
Next step
Is AI recommending your business?
Find out how visible you are across ChatGPT, Gemini, Perplexity, and AI Overviews.
Add structured data to your website
AI systems that crawl websites look for machine-readable data. JSON-LD schema markup gives them a cleaner version of what your business is, where you operate, and what you're known for.
At minimum, implement LocalBusiness schema with your name, address, phone, hours, and service categories. Use Service schema to describe what you offer, FAQPage schema where you publish useful FAQs, and sameAs links to connect your verified profiles. Use Review or AggregateRating markup only when it fits current Google guidelines.
Your web developer can implement this in an afternoon. Test with Google's Rich Results Test to make sure it's working. For the full technical walkthrough, see What Is JSON-LD?.
Respond to reviews
Review responses are public and may be retrieved with the page. Respond to help customers and future readers, not to create an assumed AI signal.
Respond to everything. Vary your language. Reference specific details from each review. For negative reviews, acknowledge the problem and offer resolution.
Monitor and test
Periodically ask ChatGPT and Gemini for businesses in your category and location. See if you're mentioned. See who's ahead of you.
This isn't a one-time check. Models, retrieval sources, and competitors change. You need to track your AI visibility over time just like you'd track keyword rankings. The AI Visibility Grader can give you a focused one-profile baseline in about 1-3 minutes.
For Google-specific testing, the Gemini local recommendation guide separates Gemini Apps, Ask Maps, Google AI Search, and Maps-grounded applications before you compare markets.
Important
When a competitor appears instead, compare the cited pages and public business facts before assuming reviews, schema, or any single tactic caused the result.
Further Reading
- How to Rank in ChatGPT Local Search Results. Local Falcon's tactical guide to AI visibility
- ChatGPT Local Search Data Sources. Where ChatGPT pulls business information from
- Google Rich Results Test. Validate your schema markup implementation
- LocalBusiness Schema. Official documentation for structured data
Dylan Allen-Arnegård is the CEO of Cheers, the local search platform for service businesses.