Posted by randfish
A recent patent from Google suggests a new kind of influence in the rankings that has immense implications for marketers. In today's Whiteboard Friday, Rand discusses what it says, what that means, and adds a twist of his own to get us thinking about where Google might be heading.
For reference, here's a still of this week's whiteboard. Click on it to open a high resolution image in a new tab!
Video Transcription
Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week let's chat about some things that Google is learning about web searchers and web surfers that may be impacting the rankings.
I was pretty psyched to see a patent a few weeks ago that had been granted actually to Google, so filed a while before that. That patent came from Navneet Panda who, as many in the SEO space may remember, is also the engineer for whom Panda, the Panda Update from Google, is named after. Bill Slawski did a great analysis of the patent on his website, and you can check that out, along with some of the other patent diagrams themselves. Patents can be a little confusing and weird, especially the language, but this one had some surprising clarity to it and some potentially obvious applications for web marketers too.
Deciphering searcher intent
So, in this case, Googlebot here -- I've anthropomorphized him, my Googlebot there, nicely -- is thinking about the queries that are being performed in Google search engine and basically saying, "Huh, if I see lots of people searching for things like 'find email address,' 'email address tool,' 'email finder,' and then I also see a lot of search queries similar to those but with an additional branded element, like 'VoilaNorbert email tool' or 'Norbert email finder' or 'how to find email Norbert,' or even things like 'email site:voilanorbert.com,'" Googlebot might actually say, "Hmm, lots of searchers who look for these kinds of queries seem to be also looking for this particular brand."
You can imagine this in tons and tons of ways. Lots of people searching for restaurants also search for Yelp. Lots of people searching for hotels also add in queries like "Trip Advisor." Lots of people searching for homes to buy also add in Zillow. These brands that essentially get known and combined and perform very well in these non-branded searches, one of the ways that Google might be thinking about that is because they see a lot of branded search that includes the unbranded words around that site.
Google's site quality patent
In Panda's site quality patent -- and Navneet Panda wasn't the only author on this patent, but one of the ones we recognize -- what's described is essentially that this algorithm, well not algorithm, very simplistic equation. I'm sure much more than simplistic than what Google's actually using if they are actually using this. Remember, when it comes to patents, they usually way oversimplify that type of stuff because they don't want to get exactly what they're doing out there in the public. But they have this equation that looks like this: Number of unique searchers for the brand or keyword X -- so essentially, this is kind of a searches, searchers. They're trying to identify only unique quantities of people doing it, looking at things like IP address and device and location and all of that to try and identify just the unique people who are performing this -- divided by the number of unique searches for the non-branded version.
So branded divided by non-branded equals some sort of site quality score for keyword X. If a lot more people are performing a search for "Trip Advisor + California vacations" than are performing searches for just "California vacations," then the site quality score for Trip Advisor when it comes to the keyword "California vacations" might be quite high.
You can imagine that if we take another brand -- let's say a brand that folks are less familiar with, WhereToGoInTheWorld.com -- and there's very, very few searches for that brand plus "California vacations," and there's lots of searches for the unbranded version, the site quality score for WhereToGoInTheWorld.com is going to be much lower. I don't even think that's a real website, but regardless.
Rand's theory
Now, I want to add one more wrinkle on to this. I think one of the things that struck me as being almost obvious but not literally mentioned in this specific patent was my theory that this also applies to clickstream data. You can see this happening obviously already in personalization, personalized search, but I think it might be happening in non-personalized search as well, and that is essentially through Android and through Chrome, which I've drawn these lovely logos just for you. Google knows basically where everyone goes on the web and what everyone does on the web. They see this performance.
So they can look and see the clickstream for a lot of people's process is a searcher goes and searches for "find email address tool," and then they find this resource from Distilled and Distilled mentions Rob Ousbey's account -- I think it was from Rob Ousbey that that original resource came out -- and they follow him and then they follow me and they see that I tweeted about VoilaNorbert. Voila, they make it to VoilaNorbert.com's website, where their search ends. They're no longer looking for this information. They've now found a source that sort of answers their desire, their intent. Google might go, "Huh, you know, why not just rank this? Why rank this one when we could just put this there? Because this seems to be the thing that is answering the searcher's problem. It's taking care of their issue."
So what does this mean for us?
This is tough for marketers. I think both of these, the query formatting and the potential clickstream uses, suggest a world in which building up your brand association and building up the stream of traffic to your website that's solving a problem not just for searchers, but for potential searchers and people with that issue, whether they search or not, is part of SEO. I think that's going to mean that things like branding and things like attracting traffic from other sources, from social, from email, from content, from direct, from offline, and word-of-mouth, that all of those things are going to become part of the SEO equation. If we don't do those things well, in the long term, we might do great SEO, kind of classic, old-school keywords and links and crawl and rankings SEO and miss out on this important piece that's on the rise.
I'm looking forward to some great comments and your theories as well. We'll see you again next week for another edition of Whiteboard Friday. Take care.
Video transcription by Speechpad.com
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Posted by amandaecking
This post was originally in YouMoz, and was promoted to the main blog because it provides great value and interest to our community. The author's views are entirely his or her own and may not reflect the views of Moz, Inc.
I've been in and out of Google Analytics (GA) for the past five or so years agency-side. I've seen three different code libraries, dozens of new different features and reports roll out, IP addresses stop being reported, and keywords not-so-subtly phased out of the free platform.
Analytics has been a focus of mine for the past year or so—mainly, making sure clients get their data right. Right now, our new focus is closed loop tracking, but that's a topic for another day. If you're using Google Analytics, and only Google Analytics for the majority of your website stats, or it's your primary vehicle for analysis, you need to make sure it's accurate.
Not having data pulling in or reporting properly is like building a house on a shaky foundation: It doesn't end well. Usually there are tears.
For some reason, a lot of people, including many of my clients, assume everything is tracking properly in Google Analytics... because Google. But it's not Google who sets up your analytics. People do that. And people are prone to make mistakes.
I'm going to go through six scenarios where issues are commonly encountered with Google Analytics.
I'll outline the remedy for each issue, and in the process, show you how to move forward with a diagnosis or resolution.
1. Self-referrals
This is probably one of the areas we're all familiar with. If you're seeing a lot of traffic from your own domain, there's likely a problem somewhere—or you need to extend the default session length in Google Analytics. (For example, if you have a lot of long videos or music clips and don't use event tracking; a website like TEDx or SoundCloud would be a good equivalent.)
Typically one of the first things I'll do to help diagnose the problem is include an advanced filter to show the full referrer string. You do this by creating a filter, as shown below:
Filter Type: Custom filter > Advanced Field A: Hostname Extract A: (.*) Field B: Request URI Extract B: (.*) Output To: Request URI Constructor: $A1$B1
You'll then start seeing the subdomains pulling in. Experience has shown me that if you have a separate subdomain hosted in another location (say, if you work with a separate company and they host and run your mobile site or your shopping cart), it gets treated by Google Analytics as a separate domain. Thus, you 'll need to implement cross domain tracking. This way, you can narrow down whether or not it's one particular subdomain that's creating the self-referrals.
In this example below, we can see all the revenue is being reported to the booking engine (which ended up being cross domain issues) and their own site is the fourth largest traffic source:
I'll also a good idea to check the browser and device reports to start narrowing down whether the issue is specific to a particular element. If it's not, keep digging. Look at pages pulling the self-referrals and go through the code with a fine-tooth comb, drilling down as much as you can.
2. Unusually low bounce rate
If you have a crazy-low bounce rate, it could be too good to be true. Unfortunately. An unusually low bounce rate could (and probably does) mean that at least on some pages of your website have the same Google Analytics tracking code installed twice.
Take a look at your source code, or use Google Tag Assistant (though it does have known bugs) to see if you've got GA tracking code installed twice.
While I tell clients having Google Analytics installed on the same page can lead to double the pageviews, I've not actually encountered that—I usually just say it to scare them into removing the duplicate implementation more quickly. Don't tell on me.
3. Iframes anywhere
I've heard directly from Google engineers and Google Analytics evangelists that Google Analytics does not play well with iframes, and that it will never will play nice with this dinosaur technology.
If you track the iframe, you inflate your pageviews, plus you still aren't tracking everything with 100% clarity.
If you don't track across iframes, you lose the source/medium attribution and everything becomes a self-referral.
Damned if you do; damned if you don't.
My advice: Stop using iframes. They're Netscape-era technology anyway, with rainbow marquees and Comic Sans on top. Interestingly, and unfortunately, a number of booking engines (for hotels) and third-party carts (for ecommerce) still use iframes.
If you have any clients in those verticals, or if you're in the vertical yourself, check with your provider to see if they use iframes. Or you can check for yourself, by right-clicking as close as you can to the actual booking element:
There is no neat and tidy way to address iframes with Google Analytics, and usually iframes are not the only complicated element of setup you'll encounter. I spent eight months dealing with a website on a subfolder, which used iframes and had a cross domain booking system, and the best visibility I was able to get was about 80% on a good day.
Typically, I'd approach diagnosing iframes (if, for some reason, I had absolutely no access to viewing a website or talking to the techs) similarly to diagnosing self-referrals, as self-referrals are one of the biggest symptoms of iframe use.
4. Massive traffic jumps
Massive jumps in traffic don't typically just happen. (Unless, maybe, you're Geraldine.) There's always an explanation—a new campaign launched, you just turned on paid ads for the first time, you're using content amplification platforms, you're getting a ton of referrals from that recent press in The New York Times. And if you think it just happened, it's probably a technical glitch.
I've seen everything from inflated pageviews result from including tracking on iframes and unnecessary implementation of virtual pageviews, to not realizing the tracking code was installed on other microsites for the same property. Oops.
Usually I've seen this happen when the tracking code was somewhere it shouldn't be, so if you're investigating a situation of this nature, first confirm the Google Analytics code is only in the places it needs to be.Tools like Google Tag Assistant and Screaming Frog can be your BFFs in helping you figure this out.
Also, I suggest bribing the IT department with sugar (or booze) to see if they've changed anything lately.
5. Cross-domain tracking
I wish cross-domain tracking with Google Analytics out of the box didn't require any additional setup. But it does.
If you don't have it set up properly, things break down quickly, and can be quite difficult to untangle.
The older the GA library you're using, the harder it is. The easiest setup, by far, is Google Tag Manager with Universal Analytics. Hard-coded universal analytics is a bit more difficult because you have to implement autoLink manually and decorate forms, if you're using them (and you probably are). Beyond that, rather than try and deal with it, I say update your Google Analytics code. Then we can talk.
Where I've seen the most murkiness with tracking is when parts of cross domain tracking are implemented, but not all. For some reason, if allowLinker isn't included, or you forget to decorate all the forms, the cookies aren't passed between domains.
The absolute first place I would start with this would be confirming the cookies are all passing properly at all the right points, forms, links, and smoke signals. I'll usually use a combination of the Real Time report in Google Analytics, Google Tag Assistant, and GA debug to start testing this. Any debug tool you use will mean you're playing in the console, so get friendly with it.
6. Internal use of UTM strings
I've saved the best for last. Internal use of campaign tagging. We may think, oh, I use Google to tag my campaigns externally, and we've got this new promotion on site which we're using a banner ad for. That's a campaign. Why don't I tag it with a UTM string?
Step away from the keyboard now. Please.
When you tag internal links with UTM strings, you override the original source/medium. So that visitor who came in through your paid ad and then who clicks on the campaign banner has now been manually tagged. You lose the ability to track that they came through on the ad the moment they click on the tagged internal link. Their source and medium is now your internal campaign, not that paid ad you're spending gobs of money on and have to justify to your manager. See the problem?
I've seen at least three pretty spectacular instances of this in the past year, and a number of smaller instances of it. Annie Cushing also talks about the evils of internal UTM tags and the odd prevalence of it. (Oh, and if you haven't explored her blog, and the amazing spreadsheets she shares, please do.)
One clothing company I worked with tagged all of their homepage offers with UTM strings, which resulted in the loss of visibility for one-third of their audience: One million visits over the course of a year, and $2.1 million in lost revenue.
Let me say that again. One million visits, and $2.1 million. That couldn't be attributed to an external source/campaign/spend.
Another client I audited included campaign tagging on nearly every navigational element on their website. It still gives me nightmares.
If you want to see if you have any internal UTM strings, head straight to the Campaigns report in Acquisition in Google Analytics, and look for anything like "home" or "navigation" or any language you may use internally to refer to your website structure.
And if you want to see how users are moving through your website, go to the Flow reports. Or if you really, really, really want to know how many people click on that sidebar link, use event tracking. But please, for the love of all things holy (and to keep us analytics lovers from throwing our computers across the room), stop using UTM tagging on your internal links.
Now breathe and smile
Odds are, your Google Analytics setup is fine. If you are seeing any of these issues, though, you have somewhere to start in diagnosing and addressing the data.
We've looked at six of the most common points of friction I've encountered with Google Analytics and how to start investigating them: self-referrals, bounce rate, iframes, traffic jumps, cross domain tracking and internal campaign tagging.
What common data integrity issues have you encountered with Google Analytics? What are your favorite tools to investigate?
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