Hey gang, I have a bit of a surprise for you this month… for me at least. You see, for those that know me, I generally don’t do “educational”. Why? For a few reasons:
- I’m not a fan of giving Google our strategies
- I’m not a fan of giving the bad guys our (negative SEO) strategies
- I’m an SEO and don’t generally give my competitors our strategies.
Yeah, I know… not real neighborly of me, but it is what it is. I prefer to teach folks how to think for themselves and about how search engines work. The real dirt I share in private. Not this month, though.
We’re going to look at some simple data points that I have been finding may not be all that common. Just for a change… bad guys won’t care and Google wouldn’t mind.
Google Query Data and your SEO Program
First off you need to know where to find the data in question. Simply go over to Google Webmaster Tools and navigate to: Search Traffic > Search Queries:
Now, because it’s much groovier, I prefer to actually import the data into Google Analytics. So be sure to do that before we move along. The data is easier to massage and we can even create custom reports.
To get started we’re going to be looking at a few things; brand and non-brand data. Our goal, is to ensure we’re getting the most from our SERP display as well as our keyword targeting.
Non-Branded Traffic Data
We’re now going to be looking at the non-branded traffic. To do that, let’s strip out the brand traffic from the report. I actually prefer doing this by downloading the spreadsheet, but for now we’ll just use the GA report.
Just use the ‘filter’ to strip out any core brand related terms. By that I mean the website/company. If you sell ‘brands’ of some form, keep those in.
What we should now have left is something that looks like this (sorry, not sharing client data):
Now we’re starting to get a better view of things. You can now start to massage data via any of the headings, (impressions, clicks, etc.). Mildly interesting, right?
But we can’t actually use this yet. You see, this data is all the Google traffic. Sooooo… that’s not entirely a clear picture that’s actionable.
Next I want you to use the ‘Secondary Dimension’ and set it to: ‘Google Property’:
This will break down the query terms by:
- Web search (can also break this down into geo later)
- Image search (traffic from image search queries)
- Mobile (traffic from searches on mobile devices)
This is a hugely important filter. In a lot of cases the data in the image queries isn’t really what we’re after and can skew the reporting. It can also give insights into the types of people visiting the site.
Consider the below data, which is the same keyword, via different properties:
As you can see above, the image search traffic really does make a mess of things. If you didn’t know any better, your data would be off wildly. Not ideal.
Not only does the impressions and Average Position for the images skew things, but we can also now see the click-through rate (CTR) is more than doubled for the non-mobile traffic.
What to Do With Your Non-Branded Query Data
Now that we’ve sorted out how to get at this non-branded query data, what good is it? For starters, that’s situational like most things.
Approach analytics with a question to be answered. But here are a few things I generally look for:
Right away we want to start looking at the SEPR CTRs on the various terms. Let’s look at the example below:
Right away we can see, for web search, these two related non-brand terms are vastly different. The one that’s actually got a worse Average Position, has a much worse CTR. In fact, it’s 0 percent.
We want to start looking at the title and meta descriptions for the pages (display elements) to try and understand why. The goal is to start to tweak things to improve them.
Focusing Term Targeting
Another issue that can cause some less than ideal CTR is of course the terms in question. We’ll often see better clicks with a more targeted phrase or term over a generalized non-modified one. Problem is that the generalized terms tend to have greater impressions.
You will have to understand your own query spaces to best gauge what an optimal SERP CTR is going to be. What is important is to learn from the data and adjust your SEO program and keyword/phrase targeting accordingly.
Another aspect we often look for here is the Average Position and where we might be able to make some simple adjustments to increase it. Let’s consider these:
Considering the positions, between 6-8 on average, they have a fairly healthy CTR. Given what we know about click bias, they are seemingly performing fairly well. What we’ll do in these situations is to look at improving the rankings of these pages with some internal link ratios (seek to increase without breaking the page mapping).
Dealing with Branded Traffic
While much of the same concepts we’ve looked at can be related to brand traffic, we can also learn some things about how searches are being interacted with on our actual brand traffic. To get this started just reverse the filtering we started with to only include the branded traffic to the site.
Some things we can consider here:
- Average Position: Why in the heck aren’t we ranking at the top for some brand related terms? This is obviously a bit of an issue and we want to start to find ways to strengthen this.
- Images: We’re not ranking well for images on our brand, but we should at least cross reference some conversion data to see if this is even worth researching further. In most cases, this is a definite no.
- Performance: What are some branded terms underperforming? Establish any terms that are under 25 percent for a high Average Position and find out why. It may be due to poor display elements (title, meta descriptions, rich snippets, etc.)… or it could be a high new traffic element. Again, cross reference with conversion data while you’re at it.
- Property: Again, as with the non-branded, we want to look at the performance of the web and mobile referrers.
Once you have identified some potential issues, it’s probably a good idea to go and create some custom reports to get a better sense of what is actually happening here. It also might be a good idea to also look at different time frames to get a sense of any temporal elements related to seasonality or ongoing promotional work being done outside of search.
Now, it must be noted in all cases that we need to actually look at the SERP in question most times to ensure that there aren’t other elements taking place. These can include:
- Personalization – geo or otherwise
- Universal elements – images, videos, etc.
- Authorship (leads to higher CTR)
- Real estate above the fold (in mobile and web)
These should be taken into account when doing your analysis.
Qualifying and Optimizing for the Traffic
It should be noted that during the entire process we need to actually qualify the traffic. From the terms that are bringing traffic, to terms we’re actively targeting to the Google property sending the traffic. Look at these against known goals and other conversion data to ensure any strategy changes you make due to this data makes sense.
- Does it seem like the clicks would be qualified traffic?
- Can the display of my page in the SERP be better optimized for this query?
- What is the searching trying to do?
- Where is the searcher located? Will their device change behavior?
- Is the SERP display for the page compelling to click?
- If the searcher selects the page in the SERP; will the page match their expectations?
Each situation is different as you will have different primary and secondary conversion points. The main goal here today was to get you thinking about this data and what it can do for your SEO program. We often can find plenty of ways to tweak things on the site to help improve these numbers. In a world of risk-adverse link building, it pays to try and find some quick wins.
Google’s query data is a great place to start.
Query Data: More from Google
Below is a video that is also worth watching when starting to come to grips with the query data elements. You can also get some more details here.
General notes from the video:
- Impressions only count if it was showed to a user. If they didn’t go to Page 2 (where your listing was), it won’t count.
- Average Position doesn’t count multiple listings on a single SERP. Only the first.
- Qualified traffic = targeted traffic to terms. Can adjust the on-site to adapt where it doesn’t align.
- Sort by clicks, not impressions; gives a better sense of those actually reaching the site.
- Look for qualified and unqualified traffic
- Look at ‘pages’ to ensure the right pages come up for the terms (page mapping)
- If you’ve duplicate issues; use 301 or rel=canonical
- Look at CTR to ensure it’s optimal. If not, look at the SERP display to potentially improve.
And there we have it… I hope you start using this data more and find some easy quick wins of your own.
Until next time, play safe!