How Proximity Affects Rankings in Local Search Results
In a recent study, we looked at the impact of proximity on rankings in local search results. In other words, given a search query for a certain location, which businesses will appear in the immediate vicinity vs. further away from that location? We found different kinds of patterns in our data set. For major metros like New York City and Los Angeles, there were obvious differences in rankings between highly ranked locations close by vs. more outlying areas. However, for mid-sized cities like Raleigh or San Francisco the results weren’t so straightforward. So what explains these different patterns? Here are results by Web design Toronto and web design NYC:
The first main pattern we found was that results were generally clustered around highly ranked places close to the center of a given metro area, if any exist at all. You can read more about this in our original blog post on the subject. This pattern makes sense because Google’s algorithm for local search is known to value high-quality places that people often use as references.
The second major pattern we found was that it made no difference whether you were searching at the city level or wider geography (e.g., “San Francisco, CA”). Businesses close by will rank well regardless of the size of the city. This makes sense as well because using a blanket search at a large scale will give you more raw results to work with, allowing Google’s algorithm to identify additional high-quality places that wouldn’t show up if you were searching for a specific town.
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1) Beach towns
There were several beach locations, like Miami and South Padre Island, where we found that the closer you get to the water (i.e., the narrower your search gets), the further down in rankings you go. This may be because of data quality issues; it’s also possible that people searching for beach towns often know exactly where they want to go and so will narrow down their search terms accordingly.
2) College towns
We found that college towns like Oxford, Mississippi and State College, Pennsylvania rank highly throughout the metro area and not just around the campuses. While we can’t be sure why this is, we suspect it may have to do with a combination of two factors: students’ tendency to research heavily before moving somewhere new, and school websites being a consistent source of high-quality local content.
3) Bigger cities
In some cases, we found that the size of the city itself made no difference in rankings. In Oklahoma City or Columbus, Ohio for example, there was little difference in rankings between highly ranked locations close by vs. more outlying areas. One possible explanation for this is large businesses dominating the SERPs regardless of where you are in these cities.
4) West coast
We found that on the West Coast, Google tends to be biased towards showing results from major metros and frequently-searched places regardless of proximity. For example, if you’re looking for pizza in Seattle, Google will show you results from all over the metro area instead of just highly ranked places close by that are more likely to have what you want. Why is this? It’s possible that West Coast culture favors having lots of options available no matter where you go.
CONCLUSIONS:
The first thing that this study shows is that there are exceptions to the rule. Given the wealth of potential places to choose from, Google’s local algorithm will likely not begin showing results until it has compiled enough of them; even if you’re searching for something fairly specific like “pizza San Francisco”.
This does raise an interesting question about how Google ensures high-quality results even when it’s forced to cast a wide net. According to Our SEO Toronto believes that one possibility is that the algorithm favors local businesses with far-reaching networks; another is simply having personable, helpful employees who are good at directing potential customers towards something they’ll enjoy.