By Tech & Marketing Desk
The landscape of local discovery has shifted beneath the feet of enterprise marketers. Gone are the days when dominating Google’s local pack guaranteed steady foot traffic. Today, consumers are increasingly turning to generative AI engines like ChatGPT, Gemini, Perplexity, and Grok to find everything from neighborhood dental practices to late-night dining options.
For years, multi-location brands treated AI-driven search as a speculative, beta-stage novelty. However, a comprehensive new study by location-marketing platform Uberall fundamentally changes that narrative. Analyzing more than 120,000 AI mentions across 3,793 business locations spanning major U.S. markets—including New York and Chicago—researchers have decoded how large language models (LLMs) choose which local businesses to recommend.
The findings dismantle several long-held assumptions about digital marketing, proving that market share and brand size no longer guarantee visibility in the age of conversational search. Instead, AI models rely on a distinct, measurable set of algorithmic triggers.
Main Facts: The Evolution of Local AI Search
The Uberall study, spearheaded by internal GEO (Generative Engine Optimization) analyst Katya Shishchenko, evaluated five distinct AI models across five vital industries: restaurants, grocery stores, dental practices, hotels, and banks.
The core takeaway is clear: while individual AI models possess distinct recommendation "personalities," they all evaluate local businesses through a shared lens. Brand size and corporate footprint are surprisingly poor predictors of AI visibility, particularly in service-driven sectors. In fact, independent restaurants and local dental clinics routinely outperform massive enterprise chains in AI recommendation rates, provided they optimize for the right signals.
To help marketers conceptualize these overlapping metrics, the study introduced a straightforward framework known as BARS: Business Data, Authority Signals, Review Signals, and Social Signals. Understanding and optimizing these four pillars is now considered essential for any brand hoping to capture high-intent local search traffic.
Chronology: From Vague Beta Results to Predictive AI Personalities
To understand where local search stands today, it helps to examine how rapidly the technology has matured.
- Early 2025 and Prior: The early era of LLM-based local search was notoriously unreliable. Users querying AI models for local recommendations frequently encountered vague results, missing imagery, and geographic hallucinations—such as recommending a restaurant three states away for a neighborhood dinner.
- Late 2025: As retrieval-augmented generation (RAG) improved, AI models began pulling more systematically from structured databases, review ecosystems, and real-time maps data. However, marketers still viewed LLMs as wild cards, applying traditional SEO tactics without clear visibility into what drove recommendations.
- September 2026 (The Uberall Study): Uberall released its landmark data analysis covering 120,000+ AI mentions across 3,793 locations. For the first time, multi-location marketers gained empirical proof that models like ChatGPT and Perplexity favor specific operational and reputational behaviors, leading to the formalization of the BARS framework and location performance optimization strategies.
Supporting Data: Decoding the BARS Framework
The Uberall research provides granular data on what actually moves the needle across the four BARS categories.
1. Business Data (The Foot in the Door)
Data completeness—specifically the richness of a Google Business Profile (GBP)—determines whether a local brand makes it into an LLM’s consideration set at all. Complete descriptions, accurate categories, and updated attributes at scale can increase AI mention rates by 40 to 60 percentage points in select verticals. Without baseline data hygiene, a business remains invisible to the model, rendering all other marketing efforts moot.
2. Authority Signals (Beyond Enterprise Size)
Enterprise brands often assume their massive store counts or deposit shares will naturally translate into AI recommendations. However, the study revealed that corporate size is only relevant in heavily consolidated industries like grocery, banking, and hospitality, where training data heavily weights national footprints. For local service providers like dentists and restaurants, independent brands frequently dominate mention rates through hyper-local authority and earned media.
3. Review Signals (Volume Over Star Ratings)
Perhaps the most surprising revelation of the study is that review volume significantly outperforms star ratings as a predictor of AI mentions across all five analyzed verticals.
While humans heavily scrutinize star ratings, AI models look primarily at the sheer density of conversational data found within reviews. The study highlighted distinct platform dependencies across industries:
- Restaurants: Yelp dominates, with locations boasting 1,000+ reviews hitting a 93.3% mention rate.
- Grocery Stores: Yelp presence is critical; locations with 500+ reviews achieved a 100% mention probability, whereas those with under 10 reviews dropped to 13.3%.
- Dentists: Google Business Profile and Zocdoc drive visibility. Mentioned practices averaged 643 reviews compared to just 253 for unmentioned competitors.
- Hotels & Banks: GBP ratings remain vital for hotels, while Trustpilot and the Better Business Bureau (BBB) serve as primary predictors for banking institutions.
4. Social Signals (Facebook vs. Instagram)
Social media platforms play a dual role in AI visibility. Facebook follower count acts as an inclusion signal—helping a business get mentioned in the initial generation phase. Conversely, Instagram presence acts as an amplifier, increasing the frequency and prominence of mentions. For boutique hotels, Instagram engagement proved to be the single strongest predictor of AI recommendations, outperforming even traditional editorial coverage.
Official Responses and Industry Perspectives
Marketers and software developers are increasingly aligning their toolsets to meet the demands of generative engines. Industry experts note that managing these disparate signals manually across hundreds—or thousands—of brick-and-mortar locations is no longer feasible for human teams alone.
"Multi-location brands have spent years learning Google’s personality," industry analysts point out. "Now there are at least five more major AI models to figure out. Just like marketers need to understand their customers, they need to understand what matters to each AI model."
To bridge this operational gap, tech providers are leaning heavily into agentic AI. Platforms like Uberall have introduced automated control centers—such as their proprietary UB-I agent—designed to audit location profiles, correct errors based on search impact, and maintain continuous data hygiene across multi-unit enterprises without manual oversight.
Implications: Three Actionable Steps for Multi-Location Brands
For marketing teams looking to dominate local AI search over the coming quarter, the Uberall study suggests a concise, three-pronged operational focus:
- Complete and Optimize GBP Profiles at Scale: Ensure every single location profile is fully fleshed out with accurate categories, attributes, and descriptions. Leveraging automated agentic AI tools can prevent profile degradation and prioritize fixes based on direct search impact.
- Shift Focus to Review Volume: While maintaining high service standards is non-negotiable, marketing budgets dedicated solely to obsessing over a fractional star rating should be redirected toward systematic review acquisition programs across industry-appropriate platforms like Yelp, Google, and Trustpilot.
- Scale Visual and Social Assets: Upload high-quality, location-specific photos in steady, incremental waves rather than all at once. Aiming for high photo volume (100+ for standard locations, 2,000+ for premium hospitality and dining) signals to AI algorithms that a business is active, vibrant, and relevant to the local consumer.
As generative AI solidifies its role as the primary concierge for modern consumers, brands that master the BARS framework will successfully transition from traditional search optimizers to undisputed local role models across all major AI ecosystems.
