On local review platforms (such as大众点评, Yelp), which factors have the greatest impact on a merchant's GEO weight?

On local review platforms (such as大众点评, Yelp), a merchant's GEO weight is typically influenced by four core factors: the quality and quantity of reviews, information completeness, depth of user interaction, and content relevance. The quality and quantity of reviews are fundamental: Authentic, detailed reviews (including specific experience descriptions and scenario details) are more likely to be recognized by AI as high-quality signals than short positive reviews, and a continuously growing number of reviews can enhance the merchant's activity weight. Information completeness directly affects positioning accuracy: Accurate basic information such as address, business hours, and contact details helps AI quickly associate with users' geographical needs and improves local search matching. The depth of user interaction reflects merchant activity: Promptly replying to user reviews and participating in Q&A interactions can send a signal of "active merchant operation" to AI, indirectly increasing recommendation priority. Content relevance determines matching precision: The more the core services mentioned in review content (such as "high cost performance" and "clean environment") match the merchant's main business category, the more likely AI will determine it as meeting users' search intentions. Merchants can regularly check the accuracy of basic information, encourage users to share real experience details, and maintain the habit of replying to reviews to gradually improve their GEO weight and natural exposure opportunities on local review platforms.