New Study Quantifies Google Business Profile Ranking Factors: Proximity Dominates at 48%

A comprehensive analysis of 3,269 local businesses by Search Atlas has quantified the precise weighting of Google Business Profile ranking factors, revealing that proximity to the searcher accounts for approximately 48% of ranking influence in local search results. The study, which applied machine learning techniques including XGBoost regression to analyze local ranking features, demonstrates that while proximity serves as the foundational baseline factor, review quality and relevance become increasingly critical for achieving top positions in competitive sectors.

The global analysis shows that industry type represents approximately 21% of ranking influence, while review keywords contribute about 11% and the number of reviews accounts for 8%. Business name matching with search queries provides a 7% advantage, while profile and website optimization collectively contribute only 2-3% of ranking influence. The research methodology combined keyword-based SERP grid visibility, business profile metadata, and website content with reviews to measure average position across local search queries.

Sector-specific findings reveal significant variations in ranking factor importance. In the food sector, proximity remains dominant at 46% for positions 1-21, but review count and rating grow to 23% and 17% respectively for top 1-5 rankings. The health sector shows particular emphasis on category relevance at 18%, while the law sector demonstrates overwhelming proximity dominance at 68% for general rankings. Most strikingly, the beauty and personal care sector shows reviews driving almost half of ranking influence at 48%, with proximity mattering less at 21%.

The study's implications for local businesses are substantial. Companies must treat proximity as an unchangeable baseline factor while developing sophisticated review strategies that encourage service-specific keywords. Branding alignment with keyword intent emerges as critical, as businesses with names containing relevant search terms gain measurable advantages. Sector-specific optimization approaches prove essential, with beauty businesses benefiting most from reviews and brand alignment, while law firms depend primarily on proximity and review content.

This research provides empirical validation of industry observations while adding precise quantification of factor weights. The findings extend beyond traditional citation and NAP consistency factors, incorporating review text relevance and suggesting Google applies natural language processing to extract meaning from customer feedback. The study's machine learning approach, detailed in their whitepaper, explains 75% of variance in GBP rankings, offering local businesses evidence-based strategies for improving search visibility and customer discovery.