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Posted: 24 Jun 2015 04:35 PM PDT
Are click through rates on search results a ranking signal? The idea is that if the third result on a page is clicked more often than the first that it will, over time, rise to the second or first result.
I remember this question being asked numerous times when I was just starting out in the industry. Google representatives employed a potent combination of tap dancing and hand waving when asked directly. They were so good at doing this that we stopped hounding them and over the last few years I rarely hear people talking about, let alone asking, this question.
Perhaps it’s because more and more people aren’t focused on the algorithm itself and are instead focused on developing sites, content and experiences that will be rewarded by the algorithm. That’s actually the right strategy. Yet I still believe it’s important to understand the algorithm and how it might impact your search efforts.
Following is an exploration of why I believe click-through rate is a ranking signal.
Though the original principle wasn’t as clear cut, today’s interpretation of Occam’s Razor is that the simplest answer is usually the correct one. So what’s more plausible? That Google uses click-through rate as a signal or that the most data driven company in the world would ignore direct measurement from their own product?
It just seems like common sense, doesn’t it? Of course, we humans are often wired to make poor assumptions. And don’t get me started on jumping to conclusions based on correlations.
The argument against is that even Google would have a devil of a time using click-through rate as a signal across the millions of results for a wide variety of queries. Their resources are finite and perhaps it’s just too hard to harness this valuable but noisy data.
The Horse’s Mouth
It gets more difficult to make the case against Google using click-through rate as a signal when you get confirmation right from the horse’s mouth.
That seems pretty close to a smoking gun doesn’t it?
Now, perhaps Google wants to play a game of semantics. Click-through rate isn’t a ranking signal. It’s a feedback signal. It just happens to be a feedback signal that influences rank!
Call it what you want, at the end of the day it sure sounds like click-through rate can impact rank.
The Old Days
There are other indications that Google has the ability to monitor click activity on a query by query basis, and that they’ve had that capability for dog years.
Here’s an excerpt from a 2007 interview with Marissa Mayer, then VP of Search Products, on the implementation of the OneBox.
So way back in 2007 (eight years ago folks!) Google was able to create a scalable solution to using click-through rate per query to determine the display of a OneBox.
That seems to poke holes in the idea that Google doesn’t have the horsepower to use click-through rate as a signal.
The Bing Argument
Others might argue that if Bing is using click-through rate as a signal that Google surely must be as well. Here’s what Duane Forrester, Senior Product Manager for Bing Webmaster Outreach (or something like that) said to Eric Enge in 2011.
This and other conversations I’ve had make me confident that click-through rate is used as a ranking signal by Bing. The argument against is that Google is so far ahead of Bing that they may have tested and discarded click-through rate as a signal.
Yet as other evidence piles up, perhaps Google didn’t discard click-through rate but simply uses it more effectively.
Pogosticking and Long Clicks
Duane’s remarks also tease out a little bit more about how click-through rate would be used and applied. It’s not a metric used in isolation but measured in terms of time spent on that clicked result, whether they returned to the SERP and if they then refined their search or clicked on another result.
When you really think about it, if pogosticking and long clicks are real measures then click-through rate must be part of the equation. You can’t calculate the former metrics without having the click-through rate data.
And when you dig deeper Google does talk about ‘click data’ and ‘click signals’ quite a bit. So once again perhaps it’s all a game of semantics and the equivalent to Bill Clinton clarifying the meaning of ‘is’.
Seeing Is Believing
A handful of prominent SEOs have tested whether click-through rate influences rank. Rand Fishkin has been leading that charge for a number of years.
Back in May of 2014 he performed a test with some interesting results. But it was a long-tail term and other factors might have explained the behavior. But just the other day he ran another version of the same test.
However, critics will point out that the result in question is once again at #4, indicating that click-through rate isn’t a ranking signal.
But clearly the burst of searches and clicks had some sort of effect, even if it was temporary, right? So might Google have developed mechanisms to combat this type of ‘bombing’ of click-through rate? Or perhaps the system identifies bursts in query and clicks and reacts to meet a real time or ‘fresh’ need?
Either way it shows that the click-through behavior is monitored. Combined with the admission from Udi Manber it seems like the click-through rate distribution has to be consistently off of the baseline for a material amount of time to impact rank.
In other words, all the testing in the world by a small band of SEOs is a drop in the ocean of the total click stream. So even if we can move the needle for a small time, the data self-corrects.
But Rand isn’t the only one testing this stuff. Darren Shaw has also experimented with this within the local SEO landscape.
Darren’s results aren’t fool proof either. You could argue that Google representatives within local might not be the most knowledgable about these things. But it certainly adds to a drumbeat of evidence that clicks matter.
But wait, there’s more. Much more.
Show Me The Patents
For quite a while I was conflicted about this topic because of one major stumbling block. You wouldn’t be able to develop a click-through rate model based on all the various types of displays on a result.
The result that had a review rich snippet gets a higher click-through rate because the eye gravitates to it. Google wouldn’t want to reward that result from a click-through rate perspective just because of the display.
Or what happens when the result has an image result or a answer box or video result or any number of different elements? There seemed to be too many variations to create a workable model.
But then I got hold of two Google patents titled Modifying search result ranking based on implicit user feedback and Modifying search result ranking based on implicit user feedback and a model of presentation bias.
The second patent seems to build from the first with the inventor in common being Hyung-Jin Kim.
Both of these are rather dense patents and it reminds me that we should all thank Bill Slawski for his tireless work in reading and rendering patents more accessible to the community.
I’ll be quoting from both patents (there’s a tremendous amount of overlap) but here’s the initial bit that encouraged me to put the headphones on and focus on decoding the patent syntax.
Very soon after this the patent goes on to detail the number of different types of presentation bias. So this essentially means that Google saw the same problem but figured out how to deal with presentation bias so that it could rely on ‘click evidence’.
Then there’s this rather nicely summarized 10,000 foot view of the issue.
Again, no one is saying that click-through rate can be used in isolation. But it clearly seems to be one way that Google has thought about re-ranking results.
But it gets better as you go further into these patents.
Here we see clear references to how to measure long clicks and later on they even begin to use the ‘long clicks’ terminology. (In fact, there’s mention of long, medium and short clicks.)
But does it take into account different classes of queries? Sure does.
This shows that Google may adjust what they view as a good click based on the type of query.
But what about types of users. That’s when it all goes to hell in a hand basket right? Nope. Google figured that out.
Users are not created equal and Google may weight the click data it receives accordingly.
But they’re missing the boat on topical expertise, right? Not so fast!
Google may identify topical experts based on queries and weight their click data more heavily.
Frankly, it’s pretty amazing to read this stuff and see just how far Google has teased this out. In fact, they built in safeguards for the type of tests the industry conducts.
As I mentioned, I’m guessing the short-lived results of our tests are indicative of Google identifying and then ‘disregarding’ that click data. Not only that, they might decide that the cohort of users who engage in this behavior won’t be used (or their impact will be weighted less) in the future.
What this all leads up to is a rank modifier engine that uses implicit feedback (click data) to change search results.
Here’s a fairly clear description from the patent.
It tracks and logs … everything and uses that to build a rank modifier engine that is then fed back into the ranking engine proper.
But, But, But
Of course this type of system would get tougher as more of the results were personalized. Yet, the way the data is collected seems to indicate that they could overcome this problem.
Google seems to know the inherent quality and relevance of a document, in fact of all documents returned on a SERP. As such they can apply and mitigate the individual user and presentation bias inherent in personalization.
And it’s personalization where Google admits click data is used. But they still deny that it’s used as a ranking signal.
Perhaps it’s a semantics game and if we asked if some combination of ‘click data’ was used to modify results they’d say yes. Or maybe the patent work never made it into production. That’s a possibility.
But looking at it all together and applying Occam’s Razor I tend to think the click-through rate is used as a ranking signal. I don’t think it’s a strong signal but it’s a signal none the less.
Why Does It Matter?
You might be asking, so freaking what? Even if you believe click-through rate is a ranking signal, I’ve demonstrated that manipulating it may be a fool’s errand.
The reason click-through rate matters is that you can influence it with changes to your title tag and meta description. Maybe it’s not enough to tip the scales but trying is better than not isn’t it?
Those ‘old school’ SEO fundamentals are still important.
Or you could go the opposite direction and build your brand equity through other channels to the point where users would seek out your brand in search results irrespective of position.
Over time, that type of behavior could lead to better search rankings.
The evidence suggests that Google does use click-through rate as a ranking signal. Or, more specifically, Google uses click data as an implicit form of feedback to re-rank and improve search results.
Despite their denials, common sense, Google testimony and interviews, industry testing and patents all lend credence to this conclusion.
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