Google AI Transforms Local SEO for Multi-Location Businesses

Image credit: Search Engine Journal

Global businesses with multiple physical locations must now adjust their local search engine optimization (SEO) strategies due to advanced Google algorithms and the impact of artificial intelligence.

Since 2020, Google’s local algorithms have evolved into a sophisticated entity matching engine, evaluating individual storefronts within a broader brand ecosystem, according to industry analysis.

The three foundational factors for local SEO—relevance, distance, and prominence—have undergone substantial changes beyond their initial parameters. Google’s AI Overviews are increasingly influencing local queries and Google Maps results.

Relevance now demands conceptual matching and entity clustering, requiring precise alignment of primary and secondary categories across Google Business Profiles and localized content on dedicated landing pages.

Distance remains an unoptimizable factor, with Google prioritizing a user’s real-time location or specific search query modifiers rather than business efforts.

Prominence is now assessed at a local level for each individual storefront, determined by the velocity, freshness, and sentiment of reviews, local backlinks, consistent name, address, and phone (NAP) information, and offline reputation.

Google Business Profiles (GBPs) function as critical entity anchors for Google’s understanding of multi-location businesses, necessitating a corporate setup for their maintenance and verification.

This corporate management often involves bulk verification processes, the organization of business groups, and the implementation of tiered access levels for various team members.

Tiered access levels for GBPs typically include Owners, who are often centralized corporate teams; Managers, who may represent regional teams; and Site Managers, who are local staff, ensuring control while enabling localized contributions.

Source: Search Engine Journal