Google Maps Uses 72 Ranking Signals for Local Search

Google Maps utilizes a sophisticated system comprising 72 distinct ranking signals to determine the order and visibility of local business listings. This system is underpinned by Geostore, Google's internal representation of geographic entities, which constructs a canonical view of places by integrating data from numerous sources. A "listing" as seen by a user is the final output of this extensive architecture, not the core geographic entity itself. Geostore represents geographic objects as "Features," which can encompass businesses, buildings, roads, cities, stations, areas, transit elements, or even 3D objects. For a business, a Feature can include identity, geometric data, source information, associated websites, relationships to business chains, references to the Knowledge Graph, conceptual information, and ranking data. The familiar Maps listing is assembled after this canonical representation is formed, meaning that information a business owner updates in Google Business Profile may not be the exact data Google internally maintains for the entity. Google's system builds a robust representation that can incorporate data from diverse origins, maintain its integrity through geometric changes, and link to other Google identifiers, such as the Knowledge Graph. The recovered material, obtained through access to a non-public scope of Geostore, Maps protocols, network traffic, the web index, mobile services, style tables, on-device components, and a 2024 Google leak, revealed specific metrics about this system. These include 793 data source providers, 446 local search intent types, 50,998 Mapcore styles, 12,936 label styles, and 10,936 searchable Geostore declarations. While the 72 ranking signals are notable, the underlying architecture provides a more profound insight into how Google comprehends places and the future direction of local search engine optimization (SEO). As Google Maps evolves into a conversational product, the intricate web of data and ranking mechanisms will become increasingly critical for businesses seeking local visibility. This comprehensive approach suggests that local SEO will likely shift towards optimizing for a deeper understanding of place entities rather than just surface-level listing management. The system's ability to connect Features to the Knowledge Graph and the broader web further indicates a move towards more contextually aware and semantically rich local search results. The distinction between a user-facing listing and the internal "Feature" entity highlights the complexity of Google's local search infrastructure and the continuous efforts to refine how it understands and presents geographic information to users worldwide.