Ok – So you’ve done your architecture review, 301’ed the non www to the www version, cleaned up your nav, done your KW research, built great content, attracted and pursued some quality links and in the process you’ve achieved some solid rankings for your targeted terms, aaand you are seeing a bump in traffic and conversions – Congrats!
Often times it is at this point in the process that you might consider relaxing a bit while reaping the benefits from the work you’ve already done. Which is a nice thought, except that your competitors are almost certainly working hard to unseat you from your new post atop the SERPs. So what to do now? If you’ve already optimized your targeted pages, I’d say leave them be – while a tweak here and there might have an impact, your time (or your client’s time) would be better spent on linking, building more, quality content – and analyzing your current performance metrics.
In this post I’m going to cover a simple, but effective way to dig into some analytics data to uncover the proverbial ‘low hanging fruit’. This process can be done monthly, quarterly – even once or twice a year. The main takeaway should be that there is always room for improvement – and often times it is just a cut/paste away.
Striking Distance KWs
I included many of the fundamental elements of SEO in the intro because looking for “Striking Distance KWs” is something that will yield the most benefit after you have already optimized to some extent.
The concept is simple:
- After you have a good amount of data to analyze (say 6 months or so) look at your analytics to see which terms are driving traffic AND conversions (more on this later).
- Then separate out your targeted list of terms (that you’ve been tracking), and you should be left with a healthy list of alternate versions, specific long-tail queries and some misspellings – in other words, a whole bunch of terms that you either ignored during your KW research, or that never popped on your radar.
- Next, run them in a rank checker and see where you have some opportunity. The term ‘Striking Distance’ applies to terms that are hovering towards the bottom of page 1, or the top of page 2 (Rankings 7-20 ish) – and since the initial export of terms was filtered for those that converted, you know that there is value in driving more traffic from that term.
Now that the concept has been explained, lets get into some nitty gritty…
The size of your site, whether or not you are in ecommerce, how much content you produce regularly and what type of conversion metrics you track will all play a roll in how effective this type of analysis will be. As a general rule, I would not recommend spending too much time on this type of analysis for a smaller site. For example, while www.seerinteractive.com does have a growing number of pages, our site is not geared towards driving any one type of conversion. In addition, there is not too much variance in the terms that are driving traffic to our pages. In my experience this type of review can be a great for ecomm sites, or sites where content creation/aggregation is a key component.
- The first thing to do here is to export your referring KWs from a solid amount of time – again, the idea here is to find KWs that would otherwise not be on your radar…I’m going to use 6 months in this example.
- Next, make sure that you are looking at non-paid examples that are excluding your brand name.
- Finally, tab-over so that you can view how these KWs have driven conversions (you’ll need to have goals set-up for this portion) and sort by Conversion Rate. Hmm – seeing some junk in there? That is the point! … Kinda 🙂
- Although this practice is more or less designed to help you identify some of those ‘junk KWs’ that convert, using Weighted Sort in this case (which takes into consideration the number of visits) will help you prioritize and find the valuable pieces of data.
OK, now you’ve got a list of your top converting KWs over a long period of time, which itself is a pretty neat piece of data – but now you can take that, export it and begin to find how folks are getting to your site and where the opportunities lie.
As mentioned previously, this is a fairly fluid process, so if you find that adding additional filters (visits>25, or conversions >2, conversion%>5%) helps you find a better data set, by all means do it.
Long-tail & Ranking Analysis
By using a modified version of a great spreadsheet originally created by Brett Snyder, you can take your exported list of KWs (I used the max export of 500, but you can export much more) and paste them into the KW Counter. This will break out the terms neatly and assign a numerical value for the number of words in the phrase. The spreadsheet that I’ve referenced is available for download through this link.
Now you can filter the data and look at your head and long-tail terms specifically. In this example I filtered out all the phrases with 4-5 words – which pulled a total of 124 longer-tail terms that I know are converting. Then, using a V-Lookup I was able to remove our main, targeted list…leaving me with 115 terms!
I then sorted these alphabetically to find groupings, and ran these terms through a rank checker (Note: I used the SEO Book Rank Checker).
And Voila! Now I have the final data set to analyze. To summarize:
- I have pulled a large date range for converting KWs
- Used weighted sort to find those that were driving traffic and conversions
- Identified which of those terms were 4-5 words long
- Removed the terms we had been targeting in our campaign
- Pulled rankings on the remaining set
With this final data set you can sort your rankings and pull out terms that fit the ‘Striking Distance’ description (again, this can vary greatly, depending on your site and available data set). In this example, I focused on rankings 7-20…leaving me with 21, solid opportunities for improvement.
Further analysis of this data set found that 2 of the KWs were misspellings and that one was a brand confusion, leaving me with 18 KWs to review. Of these 18 it is safe to bet that the majority have not been linked internally and that on-site optimization has been minimal. Assuming you were thorough in your KW research, these terms were probably reviewed at some point – why you decided not to focus on them initially is not the important thing now, what is important is to determine what flexibility you have to tweak your pages to increase their rankings, now that you know they convert.
Hopefully this inspires some folks to dig further into their data! I also want to thank Jess, who presented a similar concept to our team last week and really stoked the fire for this post. If you have any additional thoughts on how to evaluate striking distance KWs or how to use this type of data set, feel free to drop a line in the comments section.
Follow Mark on Twitter @lavoritano