SOCi 发布本地 AI 可见度研究报告：品牌在主流 AI 工具中的推荐率达 36.1%

Consumer use of AI for local search has changed dramatically in the past 12 months. SOCi’s Local Discovery Index found that 52% of U.S. consumers use AI-powered tools for local recommendations at least once a month, up from just 9% a year ago.

This nearly 6x increase reflects growing adoption of AI tools like ChatGPT and Gemini for a wider range of tasks, along with AI features built into established platforms, most notably Google AI Overviews. Google also launched its Gemini-powered Ask Maps feature earlier this year, prompting more consumers to use AI when searching for local restaurants, retailers, and service providers.

As AI becomes more widespread and consumer awareness grows, AI visibility is increasingly essential for brands competing in local markets. AI tools often rely on established sources like Google Maps, which built their authority long before AI. What’s changed is which factors drive AI visibility and how they work.

Unlike traditional algorithms, which can theoretically be reverse-engineered, AI makes millions of probabilistic decisions inside a black box. But you can identify correlations between AI outputs and factors believed to influence local visibility. Analyzing those relationships across a large dataset can help reveal what drives results.

Measuring local visibility on AI platforms

At SOCi, we’ve spent nearly a decade developing a methodology to benchmark the local visibility of multi-location brands across digital channels. Our annual Local Visibility Index reports the findings. Ahead of the 2027 edition, we’re releasing part of our analysis focused on AI benchmarks, including recommendation rates, reputation signals, and sources cited in local AI answers.

Our method for measuring recommendation rates needs some explanation. We want to measure how often brands appear in AI queries where they’re a relevant choice. That’s complicated by the length and nuance of typical AI queries, along with the range of options introduced by query fan-out. Our solution is a simple, repeatable prompt: “Please recommend 10 businesses of type X in market Y.”

This format gets to the heart of many, if not all, local queries related to potential transactions. If a brand doesn’t appear on such a “best of” list, its chances of appearing in more nuanced queries may be slim. By running this query for every location in a brand’s sample, we can measure how often the brand appears rather than where it ranks in any single result. We consider this a more reliable measure of AI visibility.

We deliberately request 10 recommendations, more than AI answers typically provide. This lets us classify each location as “likely to be recommended” (ranks 1-5), “at risk of not being recommended” (ranks 6-10), or invisible.

We analyze ChatGPT, Gemini, and Claude, the three largest AI platforms by user base. Gemini also powers Google’s AI Overviews, AI Mode, and Ask Maps. Although brand appearances may vary across these surfaces, we use Gemini as a proxy for them.

Our Local Visibility Index dataset covers approximately 3,000 U.S. multi-location brands across 44 industries. For the AI analysis, we examined 3 million local business queries and captured 1.4 million unique source URLs.

AI tools have reached parity with local search

Perhaps our most significant finding is that AI tools now recommend our target brands about as often as Google Search does. Our 2026 Local Visibility Index, published in January, found that brands appeared in Google’s local 3-pack 35.9% of the time and on Page 1 of Yelp results 37.1% of the time. By comparison, average brand visibility across AI platforms was just 7%.

Our updated analysis shows that average AI visibility has climbed to 36.1%, roughly matching traditional local search platforms.

Results varied significantly by industry. Across our five major industry groups, AI visibility was highest for Retail and Local Services brands and lowest for Property Management and Food & Beverage. Financial Services fell in the middle. These patterns generally align with traditional search results.

Ratings and review counts drive visibility

Our results show that star ratings and review counts both strongly influence AI visibility.

Higher star ratings correlate with placement in our “likely to be recommended” tier (ranks 1-5). Recommended brands average 4.32 stars on Gemini, 4.24 on ChatGPT, and 4.22 on Claude. Ratings drop sharply for brands in the “at risk” tier (ranks 6-10), averaging around 4.0 stars across platforms. In fact, star ratings decline in a strikingly linear pattern as rank position falls across the dataset.

However, higher review volume can offset lower ratings, though the amount varies by platform. A brand with a star rating 0.2 points below its nearest ranked competitor can offset that deficit with just 4% more reviews on ChatGPT, compared with 14% on Claude and 64% on Gemini. 

These findings suggest AI tools weigh the reliability of star ratings, using review volume as a factor in that assessment.

The sources AI cites for local recommendations

AI tools rely on grounding and web search to provide answers that require up-to-date information. All three leading AI platforms now use local grounding techniques, supplemented by web search, to deliver reliable answers to local queries.

Gemini uses Google Maps, as does Claude, which appears to use the Google Places API. Reports suggest ChatGPT also accesses Google data through a third party, but its most prominent publicly identified partner for local results is now Yelp. The other platforms also frequently cite Yelp as a web source.

Our analysis of citation sources confirms that Google Maps and Yelp are by far the leading third-party sources for local AI answers. Business websites rank third, particularly the local landing pages that multi-location brands use to highlight the features and offerings of individual stores and offices.

To assess the importance of citation sources, we use two metrics that measure how widely sources appear across brands and their locations:

• Breadth measures the percentage of brands for which a source is cited for at least one location. 
• Depth measures the median percentage of a brand’s locations for which a source is cited, among brands where that source appears. 

Our three leading sources — Google Maps, business websites, and Yelp — appear for nearly every brand in the study and across almost all their locations. Other sources are more industry-specific. Apartments.com is frequently cited for property management companies, while Bankbranchlocator.com serves a similar role for banks.

Sites like Yellowpages.com, Facebook, and BBB.org offer citation opportunities across multiple industries. AI tools frequently check these sources but often can’t find profiles for every brand location. 

Our previous analysis found that roughly half of U.S. brands lack a Facebook store page for at least one location. That makes complete Facebook location coverage a potential competitive advantage for AI visibility.

How brands can win the AI recommendation

Our analysis points to four priorities for improving AI visibility in local markets:

1. Prioritize Google Maps and Yelp. Audit, fix, and optimize your listings on both platforms. Google Maps is already a well-established driver of local traffic, but some industries have overlooked Yelp, assuming it’s only relevant to restaurants. In fact, Yelp powers local AI answers across many industries and shouldn’t be ignored.
2. Make your website and local landing pages central to your strategy. Use them to establish your brand as an entity and distinguish individual locations. As a first-party property, your website offers more opportunities to establish semantic relevance than any third-party platform.
3. Improve star ratings and review volume. Encourage customers to leave reviews where feasible to maintain a steady flow of fresh feedback. Analyze review trends to identify and address service issues.
4. Optimize based on your brand’s citation profile. AI tools cite three to six sources per local answer. Once you’ve optimized your most important listings, examine your citation profile to find opportunities to claim and improve listings your competitors have neglected.

AI visibility will continue to change as LLMs evolve, bringing new optimization tactics and priorities. For now, these recommendations can help your brand stand out in local AI answers.

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