Ask ChatGPT for the best HVAC company in your city. It gives you one name, maybe two. Ask Gemini. Same thing. Now ask yourself: why that business and not yours?
The answer is math. And the math is brutal.
Data from SOCi's 2026 Local Visibility Index, which analyzed nearly 350,000 locations across 2,751 multi-location brands, shows that only 1.2% of local businesses get recommended by ChatGPT. Gemini is a bit more generous at 11%. Perplexity sits around 7.4%. For comparison, Google's local 3-pack gives visibility to roughly 35.9% of businesses in a given category.
That's a 30x visibility gap between Google and ChatGPT.
Important
In Google search, 35.9% of businesses get some visibility. In ChatGPT, it's 1.2%. AI doesn't give you a list. It picks a winner.
The winner-take-all math
Google shows you options. Here are three plumbers. Pick one. The user makes the decision.
AI works differently. It synthesizes everything it knows about every plumber in your market, makes a judgment call, and presents one answer. The AI already decided. The user just accepts the recommendation.
This changes the competitive math entirely. In the 3-pack era, three businesses shared visibility. Being #2 still got you clicks. In AI search, being #2 often means not being mentioned at all.
And AI search is growing fast. ChatGPT hit 800 million weekly active users by October 2025, up from around 400 million in early 2025. AI's share of general search queries has been growing rapidly.
This isn't a niche trend. It's the next era of search.
Why AI is so selective
Google's algorithm ranks pages. It can show ten results, twenty results, a hundred. Showing more doesn't cost anything.
AI recommendations are different. When ChatGPT recommends a plumber, it's putting its reputation on the line. A bad recommendation erodes user trust. So AI systems set a high bar for confidence before recommending anyone.
That bar includes:
Authority and trust signals. AI systems prioritize sources that demonstrate real expertise and experience. Google's own AI Overviews pull almost exclusively from authoritative, well-established sources. If your business can't demonstrate credibility through reviews, credentials, and consistent presence, the AI picks someone who can.
Cross-platform consistency. The AI checks your information across Google, Yelp, BBB, industry directories, and more. Conflicting data lowers confidence. Consistent data raises it.
Review depth. Not just star ratings. AI systems analyze review text, looking for specific mentions of services, staff, and experiences. A business with 2,000 detailed reviews is more statistically credible than one with 50 generic ones.
Recency signals. Old reviews and stale content suggest a business might not be operating at the same level. AI favors fresh signals.
What the 1% have in common
We've analyzed the patterns across businesses that consistently get recommended by AI. Five things stand out.
Overwhelming review evidence. The top 1% don't have "good" reviews. They have undeniable review profiles. Thousands of recent reviews with specific, detailed language about their services. This gives AI systems statistical confidence that the recommendation will be accurate.
Entity clarity. AI can clearly identify what these businesses are, where they operate, and what they do. Their structured data is clean. Their citations are consistent across every platform. There's no ambiguity.
Citation consistency. Same name, same address, same phone number, same categories, everywhere. Not "mostly" the same. Exactly the same.
Earned media presence. AI cross-references independent sources like Wikipedia, Reddit, YouTube, and Forbes when deciding what to recommend. The 1% get talked about by others, not just by themselves.
Multimodal content. Businesses that combine text, video, and schema markup send richer signals than text-only competitors. YouTube citations account for 16% of all LLM answer sources, per Adweek. Video content is a massively underutilized signal.
Pro Tip
Pages cited in AI Overviews tend to earn more organic clicks too, reinforcing the value of AI visibility.
The earned media factor
This is the part most businesses miss completely.
You can optimize your website, clean up your citations, and get your schema markup perfect. That's table stakes. But AI systems weight third-party validation heavily.
AI engines cross-reference independent sources like Wikipedia, Reddit, YouTube, news outlets, industry publications, and review platforms. Even though 86% of citations come from brand-managed sources, the model still looks for independent confirmation that your business is what it claims to be.
YouTube is particularly interesting. Citations from YouTube have spiked to 16% of all LLM answer sources. If you have video content showing your team, your work, or customer testimonials on YouTube, that's a signal most competitors aren't sending.
Reddit mentions matter too. AI systems treat Reddit as authentic consumer voice. If people on Reddit recommend your business in relevant threads, that's a strong signal.
"You can't buy earned media signals. You have to earn them. That's what makes them valuable to AI."
The multimodal edge
Businesses that rely on text-only web presence are leaving visibility on the table.
Think about how AI builds confidence. A text-only website with no images, no video, and no schema is a thin signal. A business with detailed service pages, YouTube videos of completed work, photo galleries, and clean structured data sends a much richer signal. AI systems have more evidence to work with, and more evidence means more confidence in the recommendation.
The businesses that produce video content have a particularly large advantage right now. Most local businesses don't create video at all, which means the barrier to standing out is low.
The math for multi-location businesses
If only 1.2% of businesses get recommended by ChatGPT, and you run 100 locations, you might think the odds are in your favor. They're not.
AI evaluates each location independently. Your Dallas location and your Houston location are separate entities with separate review profiles, separate citations, and separate signals. A strong brand name helps, but it doesn't override weak local signals.
If 40 of your 100 locations have thin review profiles, inconsistent citations, or missing schema markup, those 40 locations are invisible to AI. Your brand might get recommended in some markets and be completely absent in others.
This is where centralized GEO management becomes a competitive advantage. Hello Sugar used this approach to go from 50 to 700 reviews/month across their franchise network. The franchises and multi-location brands that treat every location as its own GEO project are the ones that show up consistently.
Your 90-day plan to break into the 1%
Days 1-30: Foundation.
- Audit every location's review profile. Count reviews, check recency, read the actual content.
- Run a citation consistency check across Google, Yelp, BBB, and your top industry directories. Document every inconsistency.
- Implement or fix LocalBusiness schema markup on every location page. Use specific subtypes (Plumber, BeautySalon, etc.).
- Set up AI monitoring: ask ChatGPT and Gemini about your business category in every market you serve. Document what they say.
- Use the Cheers AI Visibility Grader to get a baseline score across all four AI platforms in 60 seconds.
Days 31-60: Velocity.
- Launch a systematic review collection program targeting 20+ new reviews per location per month. Focus on detailed reviews, not just stars.
- Fix every citation inconsistency you found. Same name, same address format, same phone number, everywhere.
- Create video content for at least your top-performing locations. Post to YouTube with proper titles and descriptions.
- Update your website content. Fresh, specific pages for every service in every market.
Days 61-90: Amplification.
- Build earned media signals. Get covered in local news, industry publications, or relevant online communities.
- Expand schema markup beyond basics: add Service schema, FAQ schema, and Review schema.
- Re-test AI recommendations. Track changes from your Day 1 baseline.
- Double down on what's working. If review velocity moved the needle in some markets, increase it everywhere.
Pro Tip
Track your AI visibility monthly. Ask the same questions across ChatGPT, Gemini, and Perplexity for every market you serve. This becomes your GEO scorecard.
Further Reading
- SOCi 2026 AI Local Visibility Report. The source for the 1.2% ChatGPT recommendation rate, based on 350,000 locations
- Local Falcon ChatGPT Local Search Data. Deep dive into ChatGPT's local recommendation patterns
- Yext 15 AI Search Stats for 2026. Cross-platform AI visibility benchmarks
- Adweek: YouTube and Reddit AI Citation Analysis. How YouTube and Reddit are driving LLM citations
- SparkToro Zero-Click Search Study. The original research behind the 58.5% zero-click stat
Dylan Allen-Arnegård is the CEO of Cheers, the GEO platform for local service businesses.