The evolution in Social Media Signals: What’s in it for everyone?
Only a few weeks ago we were discussing the evolution of social signals and implications from a SEO perspective on our blog.
During the past weeks, the discussion around the influence of Social signals on search has been extremely hot, and some of the top search experts have shared their knowledge and experience on the topic.
The SEO Ranking Factors that were recently released by SEOMoz confirmed the strong correlation between social media and search engine rankings.
According the research, the most significant factors are (ordered by importance) Facebook shares, comments, likes, followed by tweets.
In this post I will be investigating the implications of the “social signal evolution” from the perspective of all parties involved:
- Web marketers
Let’s start with Google, who only few hours after the official launch of the +1 button has made another strategic move in the direction of social, announcing the acquisition of PostRank, an analytics company whose mission it is to track how users engage with content on social networking platforms such as Facebook, Twitter as well as many others.
These 2 events (+1 and PostRank) are strategic for Google, whose objective is to obtain the largest number of “social signals” possible, to assist their mission of constantly improving the quality of search engine results.
If you are interested, the following video provides an intro to PostRank:
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Social signals can also be used as a useful metric for web marketers and publishers.
It is likely that many webmasters have yet to realize the importance of social factors for top search engine results. Writing content that is optimised not only for SEO but also for the social audience will have a flow on effect. This ties nicely in with Panda, as copy that is unique, engaging and sticky ranks better overall.
Taking the traditional SEO factors (on-page and off-page) out of the picture, content which users interact with through the use of likes, retweets and / or +1s, is surely a stronger indication of what Google has traditionally referred to as “high quality content” than a piece of content that receives “only” links.
Google has many times mentioned that content is one of the key factors in assessing the overall quality of a website. However, it can be quite a challenge to objectively assess the quality of the content, right? But if a strong correlation exists between pages that get shared on Facebook and pages that rank well on Google (in spite of Matt Cutts later saying that this is not actually a ranking signal), then, as Danny Sullivan points out in his excellent article “it could be that shares work as a very good proxy for figuring out what Google considers quality”.
And finally, what are the implications of social signals for the end user?
We all know that when performing a query it can be hard to identify the best result. Seeing that a friend (or family member/colleague/person you follow on Twitter) has recommended (shared) a particular resource can make this task a bit easier and safer.
See this example for the query ‘Hotel in Sydney”:
Instead of spending hours reading reviews on TripAdvisor, I might just decide to trust my friend Hannah (who is known as a seasoned traveler) and book my stay at the Cambridge Hotel.
Too easy. Thanks Google.
But there’s also a (big?) downside: what about the serendipity made possible by search? What about the exposure to new ideas, content, products or even travel experiences that are outside the scope of our networks?
What if the answer we want is a point of difference?
The risk of each of us becoming a slave of a “too-perfect”, highly customised, tiny, predictable web now exists; this means that, paradoxically speaking, we could be going back to an “old-school” word-of-mouth model where our closest nodes are the only people providing answers to us.
What are your thoughts about the topic? How do you see the evolution of social signals?