Title: Analyzing Local Pack Rankings: A Deep Dive into Predictive Signals
In an effort to gain insight into what influences local pack rankings on Google Maps, I embarked on a comprehensive analysis. This investigation led to the creation of a robust dataset, comprised of over 116,000 observations across 108 metropolitan areas in the United States, covering 14 different industries. This dataset was further enriched with nearly 142,000 pulled reviews, backlinks from 3,375 domains, and 2,397 complete Google Business Profile (GBP) profiles.
To understand the impact of various signals on rank positions, I employed Spearman correlation analysis to test six key indicators against local ranking positions. The findings of this analysis reveal intriguing patterns regarding the factors influencing local rankings. Below are the key signals examined along with their correlation (ρ) to rank positions:
| Signal | Correlation (ρ) | Notes |
|———————|——————|—————————————————————————————–|
| Domain Authority | +0.361 | Inverted — higher Domain Authority corresponds to lower local rank. |
| Review Count | −0.092 | Represents approximately 1% of variance in rankings. |
| Review Velocity | −0.063 | Identified as noise in the data. |
| Review Recency | +0.041 | Also categorized as noise. |
| GBP Completeness | −0.038 | Considered noise. |
| Review Quality | −0.036 | Found to be inconsequential to ranking outcomes. |
From the analysis, it became clear that proximity emerged as the most significant predictor of local pack rankings. My testing revealed that when searched from a distance of 5 kilometers across 90 queries, 96% of top-three results changed. Notably, industries such as dental care, auto repair, and real estate exhibited zero overlap in top rankings within that distance. For instance, the top-ranking dentist in a downtown area may not be available just 5 kilometers away.
The data suggests that much of the optimization efforts for local rankings may only be addressing a mere 4% of the overall outcome. Interestingly, the seeming inverse correlation associated with domain authority does not indicate that backlinks detrimentally affect local rankings. Instead, it suggests that businesses with higher domain authority, such as major directories (e.g., Yelp, State Farm, Zillow), often occupy lower positions in the local pack due to their physical distance from the searcher.
Additionally, an analysis of review quality yielded negligible correlations. Out of the 141,900 reviews scored for service-related keywords and location mentions, the outcomes remained consistent regardless of whether reviews were detailed or brief, marking a correlation of ρ = −0.005 on the first review analysis and ρ = −0.036 on the second.
Variability was observed in median top-three positions across different industries, as shown below:
| Industry | Median Top-3 Rank | Minimum to Compete |
|———————|——————–|———————|
| Restaurant | 830 | 337 |
| Veterinary | 330 | 170 |
| Dental | 277 | 71 |
| Legal (Personal Injury) | 159 | 62 |
| Plumbing | 112 | 33 |
| Auto Repair | 103 | 36 |
| Roofing | 46 | 14 |
| Insurance | 28 | 8 |
| Electrical | 22 | 5 |
| Accounting | 10 | 2 |
It is noteworthy that while national medians show general trends, significant city-level variance exists. For example, the median top-three ranking for plumbing in Akron is an impressive 2,163, whereas dental services in Albany show a median of only 12.
These insights are observational, and I refer to “signals” rather than “factors” throughout the analysis. Stability checks prior to signal analysis revealed a remarkable 93.1% overlap in top-three results on an hourly basis and 83.3% across multi-hour intervals. This study was conducted utilizing non-parametric methods (Spearman correlation) and incorporated data from Google Maps API alongside comprehensive business and backlinks data.
For those interested in delving deeper into the dataset or checking out specific sectors and cities, I encourage you to visit the research page here and use the lookup tool to gain insights tailored to your needs.
In conclusion, while our understanding of local ranking dynamics is evolving, the central role of proximity remains strikingly clear: optimizing for physical location far outweighs the weight of review quality or domain authority in influencing local pack rankings.
One Comment
This study offers compelling evidence that proximity overwhelmingly dominates local pack rankings, which aligns with the fundamental nature of local search intent. While signals like review quantity, recency, or domain authority have traditionally been emphasized in local SEO strategies, this data suggests that their impact might be marginal compared to the simple fact of being physically close to the searcher.
It’s particularly interesting to see that industry and city-specific variability can drastically influence rankings, such as the median top-three rank for plumbing in Akron versus dental in Albany. This highlights how local market density, competition, and geographic dispersion can significantly affect visibility.
From a practical standpoint, this emphasizes the importance for businesses to focus on ensuring a strong physical presence within their target service areas—address accuracy, local citations, and proximity should be foundational. Meanwhile, efforts to optimize reviews or backlink profiles, while still valuable for overall brand authority and conversions, may not produce substantial shifts in local pack rankings unless they are coupled with a physical location advantage.
Ultimately, this reinforces the idea that local SEO is inherently spatial; succeeding often depends more on being where your customers are rather than solely optimizing digital signals. It also challenges the industry’s tendency to overemphasize backlinks and review signals as ranking factors, encouraging a more location-centric approach.