AI, Google Algorithm Changes Alter Multi-Location Business SEO

Image credit: Search Engine Journal

Global businesses with multiple physical locations are seeing a significant evolution in local search engine optimization (SEO) strategies driven by artificial intelligence and changes to Google‘s algorithms.

This shift moves beyond basic name, address, and phone number (NAP) consistency, now emphasizing sophisticated entity matching engines, a development prominent since Google’s Possum update in 2016.

Google’s local algorithms assess multiple business locations by evaluating relevance, distance, and prominence, according to industry analysis. Relevance involves conceptual matching, entity clustering, and precise category alignment, while distance considers the user’s real-time or query-modified location.

Prominence is determined by factors such as customer reviews, local backlinks, consistent NAP data, and a business’s offline reputation.

Google Business Profiles (GBP) now serve as critical entity anchors for multi-location businesses. Maintaining these profiles requires proper corporate setup, including bulk verification, the use of business groups, and tiered access levels for management.

Relevance for businesses operating at multiple sites is established by analyzing data across their entire geographical footprint. This necessitates exact alignment of primary and secondary categories across all GBPs and localized content on dedicated landing pages for each location.

Prominence, a key ranking factor, is assessed individually for each storefront. This evaluation considers the velocity, freshness, and sentiment of customer reviews, the strength of local backlinks, the consistency of NAP data, and real-world foot traffic patterns.

Source: Search Engine Journal