120K Mentions: 5 AI Models & 4 Signals for Multi-Location SEO
▼ Summary
– Uberall analyzed over 120,000 AI mentions across nearly 3,800 locations to determine how different models recommend local businesses.
– Each AI model exhibits a unique personality, with Claude favoring community businesses and Gemini providing diverse results via Google Maps integration.
– ChatGPT tends to generate consensus-driven shortlists but suffers from higher hallucination rates compared to other models.
– Grok focuses heavily on culinary details like chef qualifications and historical context, resembling long-form food journalism in its responses.
– Perplexity searches live sources and generates the most mentions, making it easier for businesses with strong current web presence to appear.
Understanding the Personality of AI Local Search
Getting your business recommended by generative AI tools like ChatGPT, Gemini, or Perplexity requires a fundamentally different approach than traditional local SEO. While these models share some foundational behaviors, each exhibits a distinct recommendation “personality” that influences how it selects and presents local businesses. Contrary to popular belief, market share is not a primary driver for AI visibility. Instead, specific data signals determine whether a location appears in search results.
Recent analysis of over 120,000 AI mentions across 3,793 locations reveals five distinct models with unique tendencies. Claude acts conservatively, favoring community-focused entities while avoiding specific healthcare providers to mitigate liability risks. Gemini offers the most diverse results by cross-referencing live Google Maps data, surfacing eight times more unique restaurants than ChatGPT in comparative studies. ChatGPT tends toward consensus-driven, shortlists but suffers from higher hallucination rates. Grok mimics food journalism, heavily referencing chef credentials and Instagram content. Finally, Perplexity searches live sources and generates the highest volume of mentions, making it the easiest model to win on for businesses with strong current web presence.
These insights stem from internal experiments at Uberall, where GEO analyst Katya Shishchenko identified consistent patterns across industries ranging from dentistry to banking. The findings suggest that multi-location brands must treat AI optimization as a systematic effort rather than relying on brand size alone.
The Four Pillars of AI Visibility
To simplify the complex landscape of AI ranking factors, we can categorize them into four core areas: Business Data, Authority, Review Volume, and Social Presence. These factors do not just influence frequency; they determine whether a business is included in the conversation at all.
Business Data Completeness
The foundation of AI visibility lies in the completeness and richness of your Google Business Profile (GBP). While business age is immutable, other profile elements are fully controllable and have significant impact. For grocery stores, a detailed GBP description can triple mention rates. In the hotel sector, increasing attributes from 6–10 to 31–50 boosts mention probability from 22% to 94%.
Photo count emerges as the single strongest predictor for restaurant mention frequency, with top-performing establishments averaging three times more photos than their peers. This signal also uniquely predicts both inclusion and frequency in the dental vertical and ranks among the top three predictors for banks. Additionally, having multiple locations increases mention rates, reaching 100% coverage at scale in grocery and dental categories. These data points act as the gatekeepers, getting your foot in the door before other signals take effect.
Authority Signals vs. Brand Size
Traditional marketing assumes that larger brands dominate search results due to scale. However, this study found that brand size,measured by store count or deposit share,is a poor predictor of AI recommendation frequency. While chains benefit from training data signals in grocery, hotels, and banking, independent brands in restaurants and dentistry frequently outperform national chains.
True authority comes from earned media and editorial recognition. Brands with 30+ news mentions saw a 15-fold increase in banking mentions and achieved a 100% mention rate in grocery. Editorial placements on platforms like Bankrate, Forbes Travel Guide, and Michelin function almost like direct ingestion into LLM training data. Banks listed on three or more editorial platforms experienced a 13-fold mention increase, while Michelin-recognized restaurants appeared in 94.5% of Perplexity’s responses. Wikipedia presence also serves as a reliable positive factor for hotels, groceries, and banks, though not for dentists. Notably, delivery platform presence on services like Instacart or DoorDash does not influence AI visibility for grocery stores.
Review Volume Trumps Star Ratings
Perhaps the most counterintuitive finding in this analysis is the relationship between reviews and AI visibility. Across four of the five analyzed verticals, review volume is the dominant signal, while star ratings play a secondary or negligible role.
For dentists, no star rating reached statistical significance as a predictor. In grocery stores, brands with high review volumes but lower ratings were mentioned 94.3% of the time, compared to only 60.6% for high-rated, low-volume brands. In banking, higher aggregate ratings on Yelp and TrustPilot actually correlated negatively with mention frequency. This is not because negative reviews help, but because national giants serving millions of customers naturally accumulate more complaints at scale.
Hotels stand as the notable exception, where GBP star ratings correlated more strongly with AI mention probability than review count. When optimizing for review volume, platform selection matters. Restaurants see a 93.3% mention rate with 1,000+ Yelp reviews, while grocery stores hit 100% probability with 500+ Yelp reviews. Dentists benefit significantly from Google Business Profile and Zocdoc reviews, with 1,000+ reviews correlating to a 92.9% mention rate.
“While star ratings aren’t always a reliable AI mention factor, they still influence the humans deciding which business to visit,” the report notes. “My advice is to optimize review volume for the models, and keep the rating for the people.”
Social Signals and Immediate Actions
Social media profiles serve distinct functions in AI visibility. Facebook followers enhance the probability of being mentioned at all, acting as a trust signal. For banks, follower count is the strongest social factor, and mentioned dentist practices have nearly five times the followers of those that are not. Conversely, Instagram presence amplifies mention frequency once a business is already in the conversation. Restaurants with strong Instagram and Yelp presences are mentioned almost seven times more often. For boutique hotels, Instagram is the single strongest predictor of AI mentions, outperforming even GBP and editorial signals. This aligns with Grok’s behavior, which references Instagram content more than any other model.
To capitalize on these insights, multi-location brands should focus on three immediate actions:
- Complete Your GBP Profiles: Filling out descriptions, attributes, and categories at scale can increase AI mention rates by 40 to 60 percentage points in certain verticals. Without complete data, there is no business for AI to amplify. Tools like Uberall’s UB-I can automate profile maintenance and highlight errors based on search impact.By systematically addressing BARS,Business data, Authority, Reviews, and Social presence,brands can ensure consistent visibility across all major AI models. This approach moves beyond reliance on market share, focusing instead on the tangible signals that drive local AI recommendations.
