Since last December’s admission from Google + Bing’s search teams regarding the direct impact of Twitter + Facebook on search rankings, marketers have been asking two questions:
- What signals are Google + Bing counting?
- How much influence do these social signals have on the results?
Over the last few weeks, we’ve been collecting data and running calculations in an attempt to provide more insight into these answers. Today, I’d like to share some results of that process. But, before we begin, there’s some important caveats.
The data we’re sharing below examines the top 30 ranking results for 10,217 searches performed on Google in late March (after the Panda/Farmer update, using top suggested keywords in each category from Google’s AdWords data). It compares the features that higher ranking results have, which lower ranking results do not. Since the standard error numbers are very, very tiny, we can be fairly confident that these correlation values would apply to Google results as a whole (i.e. if we were to run these correlations on 100K, 1 million or 1 billion results, we’d get the same correlations).
However, this does not mean we can be confident that what we’re measuring are actually ranking factors having a direct influence. Let’s use an analogy about dolphins to help illustrate:
Thus, our first caveat is – correlation is NOT causation – the features we show below may indeed be directly influencing Google’s ranking algorithm, but they also may just be artifacts or features that high ranking pages tend to have (though, we do know from their public statements that at least some data from these sources is influencing the results).
It’s also true that our analyses will not be nearly as sophisticated as whatever Google + Bing are doing with the data, so while we look at raw numbers from APIs, the search engines may have arrangements enabling them to look far deeper into the signals that make a tweet or share authentic – in particular the “author authority” metric they mention in the linked interview above. Thus, the second caveat is that results presented here are likely overly simplistic. A big takeaway for marketers should, thus, be – even if you’re sure that a social metric is highly influential, spamming the heck out of it is probably a dumb way to try manipulating the rankings.
With those out of the way, let’s look at some data!
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