Search Results containing a local pack often get the majority of clicks. Knowing which local rankings factors to optimize for the biggest bang is crucial for SEOs and business owners alike.
Previously, Google My Business studies and opinion surveys from localseoguide.com, moz.com and brigtlocal.com have sought to reveal and rank the most important local ranking factors. The goal of our research was to confirm these findings using state-of-the-art machine learning and call attention to any key differences.
This study intends to fill the gap and shed some insights in the personal injury niche on which local ranking factors are the most relevant ones.
It builds and extents on our previous data-based study to evaluate 112,000 personal injury law SERPs (search engine results page). You can see a full breakdown of the previous data analysis right here.
We defined for 4 unique keyword combinations in 426 US cities (> 100.000 inihabitants). The format of the search queries was the following:
To gather the base data for the study, we created a script to collect data points from the Google My Business Maps Listing. The relevant entries were scraped from the Google Search page (https://www.google.com/) by entering the above keyword combinations. A basic data overview can be found below.
It is important to note that we did not collect at data on proximity factors, given the inherent and practical difficulties in obtaning such data.
|Total # of searches||1674|
|#Unique place IDs||12931|
|Searches with less than 10 results||0.25%|
|Searches with 10 to 15 results||1.31%|
|Searches with 16 to 19 results||5.44%|
|Searches with a full first-page||93%|
As a next step, we enriched the listed website domains with SEO data frm the third-data provider Ahrefs. To do so, we cut down the URL website domains to their root and uploaded them onto the Ahrefs bulk analysis tool. All data sets were then merged into one.
We applied a state of the art machine learning model (first published in 2017) to determine the importance of GMB factors on rankings. More information on the model can be found in the Technical Annex. Then, we provided a deep dive into single variables that the model identified as particulary important to impact GMB positions.
The plot below indicates what factors are particulry important in impacting GMB rankings (a more technical explanation can be found below). We can conclude that having the same GMB city listed as in the search query has the largest effect on the ranking position, followed by the “type category is personal injury lawyer”, # of reviews and the # of photos. Adding the string “lawyer or”attorney" to the title can also positively impact positions, according to our analysis.
GMB details such as adding street address, website domain, and phone number do not seem to be relevant. The same is true for social signals.
The shap feature importance plot (see above) indicates the importance of each variable. That is each factor’s average contribution to the model’s predictions. The higher a variable is listed on the plot, the higher the factor´s contribution is to the GMB rankings.
On the other hand, the plot below shows the direction of the impact given each factor’s value.
For instance, if we look at the first row and the feature named “Has same city listed as in search query”, we can see a polarized distribution of SHAP values around zero. Yellow points correspond to low feature values (in this case, “No”). That means that their impact to all predictions in the data set is negative. The purple points correspond to high feature values (“Yes”) and have a positive impact on the predicted positions.
To take another example, the “Type category is personal injury” variable behaves similarly to the “Has same city listed as in search query” in that sense that they have higher feature values i.e. they will impact positively the predicted positions.