Search Atlas Study Reveals Proximity, Reviews, and Relevance as Key Google Business Profile Ranking Factors

A recent study by Search Atlas has provided definitive insights into the factors that determine Google Business Profile rankings, revealing that proximity accounts for nearly half of all ranking influence while reviews and relevance serve as critical differentiators. The research, which analyzed 3,269 local businesses across food, health, law, and beauty sectors, employed machine learning techniques to quantify the precise weight of each ranking factor, offering businesses evidence-based strategies to improve their local search visibility.

The global analysis shows that proximity dominates local search rankings with approximately 48% influence, meaning businesses closer to the searcher's location have a significant advantage. Industry type follows at 21%, indicating that different sectors have varying ranking dynamics. Review keywords contribute 11% to rankings, demonstrating that customer feedback containing relevant search terms directly impacts visibility. The number of reviews accounts for 8%, while business name matching with searched keywords provides a 7% advantage. Profile and website optimization factors collectively contribute only 2-3%, with ratings and other elements playing minimal roles below 1%.

Sector-specific findings reveal important variations in ranking dynamics. In the food sector, proximity remains crucial at 46%, but review keyword relevance (19%) and equal contributions from ratings and review count (15% each) become significant differentiators. For top 1-5 rankings, review count increases to 23% importance while proximity slightly decreases to 41%. The health sector shows similar proximity dominance (46%) but places greater emphasis on category relevance (18%) and review content (13%), reflecting the trust-based nature of medical service selection. Legal services demonstrate the strongest proximity influence at 68%, with reviews playing secondary roles through review count (10%) and relevance (8%).

The beauty and personal care sector presents the most dramatic shift from proximity-based ranking, with reviews driving nearly half (48%) of ranking influence while proximity drops to 21%. For top positions, review count becomes the dominant factor at 35%, followed by business name-keyword matching at 30%, while proximity further declines to 13%. This pattern indicates that reputation and branding outweigh location considerations in beauty-related searches.

The study utilized XGBoost regression, a machine learning algorithm known for its scalable processing and robust feature weighting capabilities. The methodology combined keyword-based SERP grid visibility, business profile metadata, and website content with review data, using average position across grid queries as the target variable. The resulting model explained 75% of the variance in GBP rankings, providing strong predictive accuracy for local search performance.

Practical implications for businesses include treating proximity as a baseline factor that cannot be adjusted but must be acknowledged, developing review strategies that encourage service-specific keywords, aligning business names with keyword intent, and implementing sector-specific optimization approaches. The research confirms that Google likely applies natural language processing to extract meaning from customer reviews, website metadata, and backlink anchors, moving beyond traditional citation and NAP consistency factors.

While the study provides unprecedented quantification of local ranking factors, limitations include potential over-weighting of proximity due to fixed grid data collection, the correlational rather than causal nature of the findings, sector data imbalances, and the snapshot nature of the data collection that doesn't account for seasonal or algorithm changes. Nevertheless, the research offers the most comprehensive analysis to date of how businesses can systematically improve their local search visibility through evidence-based strategies.