New Google Ranking Signals

In Rand’s example he searched for the phrase “Seattle Waterfront Walk” and a fairly unknown personal blog pops up in the second spot. Why? Well this is what I like to call the Starbucks effect.

Websites like Yelp, TripAdvisor, and Google/Yahoo local are like the Starbucks of the internet. Often when people conduct local searches they do a good job of taking over all available landscape. This is ok when looking for a store on the fly but it can become stifling when simply searching for local information.

Blogs are almost like the “mom and pop” stores of the internet.  Google can recognize that “Seattle Waterfront Walk” is a search most likely looking for location specific information. So where’s the best place to find it? Google picked out a personal blog written by a photographer residing in Seattle instead of much more authoritative national “Starbucks-like” websites.

In the above picture you can see how the blog links directly to his Blogger profile. Since Blogger is owned by Google it's an easy way for them to get a general idea of a blog's location.

Let's look at a hypothetical scenerio of how viral content signals may look very different depending on the website size.

Pretend the average Mashable article receives 1,500 social shares and 50 backlinks (again, hypothetical statistics).

Now pretend there's a personal blog covering the same industry averaging 90 social shares and 4 backlinks per post (equal quality to Mashable’s).

One day they happen to publish an article simultaneously that covers identical topics. After two days the Mashable article receives 700 social shares and 40 links. The personal blog post receives 300 social shares and 15 links. The Mashable article has received over twice as many shares and links as the other post; but will it rank higher?

In this scenario, when you compare the article to Mashable’s entire website it becomes apparent it’s only half as popular as their average article. Conversely, the personal blog article was over three times more popular than the average article on their site.

In order to show the best possible results Google must continually determine which pages contains better content and then rank them accordingly. Google’s algorithm runs in a very logical manner. It would be very easy to make the argument that in certain circumstances a very popular article on a small site should outrank a subpar article on a larger well known site. This is also a very scalable metric to incorporate (always important to Google) and could be accomplished by including a few site wide standard deviation measurements into their algorithm.