Google is realizing that time is money
Earlier this year (Jan 31, 2017) Google received approval for their patent US9558210 B1 (filed March 15, 2013), the so called “Determining the quality of locations based on travel time investment” patent.
The patent’s abstract reads as follows:
“Methods and apparatus related to associating a quality measure with a given location. For example, an anticipated distance value for a given location may be identified that is indicative of anticipated time and/or distance to reach the given location. At least one actual distance may be identified that is indicative of actual time for the one or more members to reach the given location. In some implementations, the anticipated/actual distance values may include one or more distributions. A quality measure is then determined based on a comparison of the anticipated distance value and the identified actual distance value. The quality measure is associated with the given location. The quality measure may be further based on additional factors.”
This might sound like a whole lot of gobbledygook to most people but for those of us sad enough to be interested in google patents (like Search Labs Pty Ltd) it gives us a super interesting insight in to another small but important local ranking factor inside Googles algorithm.
But what is it all about I hear you ask and why would it be of interest to SEOs?
Essentially Google is trying to take a search users willingness to travel to a destination in to account when ranking properties in their searches. Google is looking to use this willingness to travel to parameter as part of it’s search result quality scoring.
Google mentions in the patent that.
“The present disclosure is directed to methods and apparatus for determining the quality measure of a given location. In some implementations, the quality measure of a given location may be determined based on the time investment a user is willing to make to visit the given location.”
They then go on further to say.
“The method may further include: identifying, for the or more members, a rank indicative of the number of competing locations bypassed to reach the given location, wherein the determining the quality measure for the given location is further based on the identified rank.”
Let’s have a look at a quick example.
Say you’re kicking back at home with some mates and want to go out to a restaurant for some pizza. You grab your android phone and say “OK Google, best pizza near me”. Google returns a multitude of results, including a map pack showing the 3 local shops with the best SEO.
Papa Gorgio’s is close (only 2 kilometers away) but their pizza is only “alright”. Whilst looking at the local listings you spot Don Carlo’s, a well known local eatery with amazing slices. Don Carlo’s is 6 kilometers away (an extra 10 minutes travel time) but you and your mates decide it’s worth the extra travel time just so you can get a sweet, sweet slice of that famous Don Carlo’s pepperoni pizza.
It’s this willingness to go the extra mile that Google is looking to capture. For all users, Google is realizing that time has a price and the users willingness to invest their own time in to getting to a location when there are other potential valid search results closer to you is an amazing indicator of that local search results quality. Learn more about SEO companies in Melbourne.
An example of the local “map pack” listings for “best pizza New York”.
Another reason why this patent piqued our interest was the way in which Google included a range of optional extras that may or may not be used to augment the algorithm. We were particularly interested in the passages around user and region characteristics and through them any ways in which a business could optimize their local search rank.
Google included a multitude of options in the patent. The ones below were of particular interest. Sometimes a clean up of content is required.
It seems as though Google is looking to use search participants queries to determine one or more “region characteristic” for a given geographic location.
For instance, it seems that Google may recommend a product based on its distance, rather than the entire relevance of that result – distance becomes a relevance factor.
These region characteristics will be used to determine a quality measure for a certain area in an effort to provide more relevant search results. Google goes on to include that the region characteristic could include factors such as population density.
On top of the inclusion of region characteristics Google also wanted to add a user component in to the location quality metrics. The patent explicitly stated:
“The method may further include identifying one or more user characteristics and selecting the members of the population based on whether they share the one or more user characteristics. The one or more user characteristics may include at least one of a semantically meaningful characteristic and a latent characteristic.”
Through the passage above, Google makes clear their intentions to aggregate user data when it comes to local search and recommend certain results based on similarities. We haven’t yet figured out how this could be put to use from a SEO point of view but believe me we’re working on it.
But what if you are feeling nostalgic and decide to drive 130km back to your home town to get a slice of pizza from the favorite pizzeria of your childhood? Google has that covered too. They list an exception in the patent for excluding out-liers and thus enable them to remove the influence of people that are willing to invest a significant amount of travel time in to a certain task for less than normal reasons.
Googles patent US9558210 B1 dovetails in quite nicely with one of their other patents, US9563641B1 “Suggestion refinement“ which also relates to data aggregation. The suggestion refinement patent specifically looks at adjusting the search ranking of results based on whether or not a user or users visit a given location after receiving it as a search result. We will look to go in to more detail on this patent one of our upcoming blog posts.
Thanks for taking the time to read!