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A post from the (ever useful and bookmark-worthy) DejanSEO blog back in January drew to my attention a revamp of the search query data reported on in Google’s Webmaster Tools. Since then I’ve been looking at its functionality and how it can be put to use for SEO purposes, the results of which I’m going to share in this post.
Gone is the rounding of number to the nearest bazillion and specific numbers now greet you. Bearing in mind that both Webmaster Tools and Google Analytics are plainly Google products, the use of Clicks in one tool (WMT) and Visits in another (GA) make me wary of what attribution model is being used to define the metrics on display. There are other niggles I’ve got that I’ve yet to find further clarification on, maybe you can help me?
So diving in a bit deeper it’s worth noting that you’ve roughly 90 days of data which is a good sample, but nowhere near as comprehensive as Google Analytics clearly. The paranoid android in me has meant I’ve been archiving this data should the generosity be rescinded leaving us with absolutely no keyword friendly data sets to play with.
To view a comparison however you’ll need to limit yourself to up to 30 days’ worth of data. This is still a worthwhile exercise as CTR and Average position variances are worth tracking despite the short data sample demanding it be a regular manual job to add to the list.
Top tip: To display more than 500 rows, hack the URL like this:
So that you get something like this:
Use the star function to effectively ‘favourite’ priority search queries; these being brand terms or key product terms
Using the star filter, isolate those top terms and analyse their performance.
Moving outside of the priority queries, to make the data a bit more manageable you can exclude the queries with less than 10 impressions.
Statistically, each niche is going to throw up its own positional traffic share. As a guide typically position one accounts for a third of all subsequent clicks with the drop off from 3 to 10 pretty drastic (this post suggests you’ll get over 50% of clicks appearing first in a SERP) so if you want natural traffic to become a significant traffic stream for your site you need to be aiming for those top three spots for the terms that make you money.
It can be a daunting task knowing where to start but I see there being three groups to focus on:
Using the Top Pages tab in the WMT Search Query report you can assume that pages of your site that are sitting in position 5-10 clearly have been deemed authoritative. They combine relevant on page content with quality contextual back links (naive, moi?) so it stands to reason that there is momentum to be capitalised on.
Moving these pages into the top three positions is likely to reap traffic dividends. To make an impact here look at the following:
Those pages languishing in positions between 11-20 are easy to overlook. But if they are duking it out in a set of results amounting to more than 6 figures, the likelihood again is that there is some quality here worth exploring.
Action: Export to CSV, filter by Avg. Position 11-20 and then find some candidates for a bit of TLC.
Spying terms that don’t normally sit on your list of usual suspects should be a greeted with some glee. These are likely to be tricky to spot and will require a magnifying glass and your list of priority keywords. Spotting any new terms not on that list could mean you’ve found new opportunities.
Here you may be seeing the early benefit from new content you’ve pushed live. It can also mean that there is content enjoying some Hummingbird or LSI benefits so scrutinize where your target keyword is and its relation to other terms supporting the overall theme of the page. You could very well have a new set of keyword or content opportunities to make the most of.
So there are several ways that the recent Webmaster Tools updates can benefit online marketers. How have you found the revamped data – still shaky or have you been able to successfully use the insight contained there? Let me know in the comments, I’d be very interested in your thoughts.
In today’s multichannel world, there are mountains of data which provide insights into how users have interacted with your business and their path to conversion (or non-conversion). It is important to understand performance with multichannel marketing, which can be achieved through attribution modelling. Attribution refers to assigning credit to something (a channel, touchpoint, etc.) for the role it played in the final conversion. An attribution model is a rule, or set of rules, that assigns this credit correctly to the right channel or touchpoint.
For a long time, Bing, the UK’s second-largest search engine, has been underappreciated and, in some instances, even ignored. Often regarded as the inferior search engine to market leader Google, Bing has historically struggled to appeal to many in the digital world. Most PPC analysts would give justified reasons for neglecting Bing for so long; these include the volume of traffic and the user experience just not matching up to Google. However, the validity of these assessments is now diminishing. Bing has grown and improved rapidly in the last couple of years; if you are not integrating it into your comprehensive digital marketing plan, you run the risk of missing out on a large portion of your chosen market and significant revenue.