Local search visibility has become a cornerstone of business growth, yet the true drivers behind Google Business Profile (GBP, formerly GMB) rankings are still debated. That’s the gap we set out to close.
We analyzed 3,269 local businesses across multiple sectors (food, health, and law), pairing GBP data, reviews, website content, and SERP grid performance to measure ranking outcomes.
The study defines how GBP rankings shift based on proximity, reviews, and relevance. GBP ranking is the process by which Google orders businesses in the local map pack. GBP ranking matters because it drives customer discovery.
The study investigates proximity, reviews, and content relevance, which provides measurable percentages, sector-specific breakdowns, and practical recommendations. The study matters because local search influences user decisions directly.
What is the Final Takeaway?
The study done by Search Atlas in May 2025 and published in August 2025 proves proximity is the strongest universal driver, while reviews and relevance define competitive advantage. The study identifies proximity as baseline, reviews as differentiators, and relevance as sector-specific.
Businesses seeking local visibility must invest in reviews, keyword branding, and GBP completeness. Businesses must accept proximity as fixed. Businesses that adopt evidence-based, sector-specific strategies achieve higher ranking predictability.
Methodology
The study done by SA applies machine learning to analyze local ranking features. Machine learning (ML) refers to automated model training from data. Machine learning produces feature importance scores. Machine learning matters because it reveals hidden ranking correlations.
The researchers from SA use the Machine Learning technique called XGBoost regression. XGBoost is an ML algorithm and delivers scalable processing, robust feature weighting, and tabular data handling. XGBoost matters because it generates predictive accuracy for structured signals.
The dataset combines 3 key sources:
- Keyword-based SERP grid visibility.
- Business profile metadata.
- Website content and reviews.
The target variable is average position, the mean rank across grid queries that represents visibility distribution, positional accuracy, and rank prediction. The average position matters because it indicates exposure in local search.
What does the Global Analysis Show?
According to the global analysis conducted by Search Atlas, the model explains 75% of the variance in GBP rankings. The global model refers to the all-sectors dataset, which weights features across industries.
There are key factors that influence how high a business ranks in Google’s local search results. The key factors that influence businesses ranks in local search results are listed below.
- Proximity (≈ 48%): Location is the single biggest driver. The closer a business is to the searcher’s center point, the higher it tends to rank.
- Industry Type (≈ 21%): Different sectors rank differently. For example, what matters most for doctors may not weigh the same for home services.
- Review Keywords (≈ 11%): Reviews that include relevant keywords help rankings. Google rewards customer feedback that matches search terms.
- Number of Reviews (≈ 8%): Having more reviews improves rankings, but not as much as the actual content of those reviews.
- Business Name Match (≈ 7%): Businesses with names that include the searched keyword gain an advantage.
- Profile and Website Optimization (≈ 2–3%): A complete GBP and keyword-optimized website add some value but are not primary factors.
- Other Factors (<1%): Ratings, business category relevance, and website authority play very small roles overall and may only matter in certain industries.
Proximity, industry type, and review signals are the strongest drivers of Google local rankings, while profile details and ratings play only minor roles.
What are the Sector-Based Findings?
I, Manick Bhan, along with David Conde and Euthymios Kasvikis, ran experiments across major sectors to show which factors matter most for rankings in each industry. The breakdown to show which factors matter most for rankings in each industry is listed below.
Food Sector
The Food sector shows review-driven differentiation beyond proximity. Food sector ranking refers to restaurant and dining-related businesses. Food sector ranking reflects competitive density. Food sector ranking matters because consumer decisions depend heavily on reviews.
The rankings for food businesses and the main factors influencing them are shown below.
- Positions 1–21: Distance is the top factor (46%), followed by review keyword relevance (19%). Both rating and review count contribute equally (15%).
- Top 1–5 Rankings: Proximity (41%) still matters most, but review count (23%) and rating (17%) grow in importance. Review keyword alignment (12%) helps differentiate leaders.
Food businesses win visibility by combining strong local presence with high-quality and plentiful reviews.
Health Sector
The Health sector combines proximity with review and category alignment. Health sector ranking refers to clinics, doctors, and medical services. Health sector ranking depends on trust and accuracy. Health sector ranking matters because patient selection relies on credibility.
The rankings for health businesses and the main factors influencing them are shown below.
- Positions 1–21: Distance dominates (46%), while category relevance (18%) is particularly influential in health searches. Review content (13%) and review volume (10%) add credibility.
- Top 1–5 Rankings: Proximity drops slightly (24%), while review volume (23%) and review relevance (22%) become equally important. Business name relevance and ratings (13% each) matter more at the very top.
Health businesses need a balance of location, category accuracy, and keyword-rich reviews to secure top spots.
Law Sector
The Law sector highlights proximity as overwhelming. Law sector ranking refers to attorneys and legal services. Law sector ranking relies on trust, location, and visibility. Law sector ranking matters because clients seek nearby counsel.
The rankings for legal businesses and the main factors influencing them are shown below.
- Positions 1–21: Proximity dominates with nearly 68% influence. Reviews matter but less—review count (10%) and review relevance (8%) are secondary. Ratings ( 5%) add modest support.
- Top 1–5 Rankings: Distance is still strongest (43%), but reviews grow in weight. Review relevance (22%) and review count (17%) are key, supported by ratings (10%).
Law firms succeed locally by being nearby and backed by credible, keyword-relevant reviews. Trust and locality outweigh technical SEO factors.
Beauty and Personal Care Sector
The Beauty sector relies most on reviews and branding. Beauty sector ranking refers to salons, spas, and personal care providers. Beauty sector ranking reflects aesthetics, branding, and customer trust. Beauty sector ranking matters because reputation drives customer acquisition.
The rankings for beauty and personal care businesses and the main factors influencing them are shown below.
- Positions 1–21: Reviews drive almost half of ranking influence (48%). Proximity matters less (21%), while review text relevance (7%) and ratings (6–7%) support visibility.
- Top 1–5 Rankings: Review count (35%) and business name–keyword match (30%) lead the way. Proximity drops to 13%, while review text and star rating contribute 9% each.
In beauty, reputation and branding outweigh location. High review volume and a keyword-matching business name are decisive for top positions.
What Patterns Appear Across Sectors?
Proximity is the top driver of local visibility. Proximity means the distance between the searcher and the business centroid. Proximity accounted for 48% of predictive power in the global model covering positions 1 to 21. That figure doubled the next factor, business sector, and surpassed reviews, content, and digital authority.
Proximity does not always dominate in elite positions. In the top 1 to 5 rankings for beauty, food, and law, reviews, business name relevance, and review text relevance gained more weight. In beauty, reviews and business name relevance explained over 65% of predictive power, with proximity ranking third.
What Should Local Businesses Do?
The implications for businesses are listed below.
1. Treat Proximity as a Baseline
Proximity influences inclusion in results. Businesses located closer to the grid centroid gain an advantage. Proximity cannot be adjusted. Treat proximity as a baseline factor, not a competitive differentiator.
2. Build a Review Strategy
Encourage reviews that include service-specific keywords. Analyze review text for semantic alignment with target keywords. Focus on quality, quantity, and context. Reviews that mention phrases like “best dentist for whitening” outrank reviews that only say “great service.”
3. Align Branding With Keyword Intent
Add keywords naturally to GBP names. A listing called Downtown Dental Clinic ranks higher for “dentist near me” compared to one called Smile Center. Branding with keyword intent creates alignment between business identity, search query, and Google’s NLP-driven interpretation.
4. Optimize by Sector
Apply strategies that reflect sector weighting. Beauty benefits from reviews and brand alignment. Law depends on proximity and review content. Health balances proximity, reviews, and category alignment. Sector-specific strategies outperform generic rules.
How Does This Compare to Industry Wisdom?
The study supports existing observations from the SEO community that proximity, reviews, and relevance shape rankings. The difference is quantification. The models assign measurable percentages to each factor.
The study extends beyond citations and NAP (Name, Address, Phone) consistency. It incorporates review text relevance, backlink anchor alignment, and URL content matching. These signals suggest that Google applies natural language processing (NLP) to extract meaning from customer reviews, website metadata, and backlink anchors.
What Could be the Limitations of the Study?
Every model has its boundaries. The boundaries of the study are listed below.
- Proximity may be overweighted due to fixed grid data collection. It’s possible that different radii or shapes shift results.
- Correlation is not causation. We measured associations, not direct algorithm rules.
- Sector imbalance tilt global weights, since some industries had more data points.
Temporal factors matter. Our snapshot captured rankings at one point in time, not across seasonal or algorithm shifts.
