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Long Tail Search & Web Analytics

Long Tail Search & Web Analytics

There is a theory called “The Long Tail” that noted that a relative handful of blogs have many links going into them but “the long tail” of millions of blogs have only a handful of links going into them. It is very similar to the bowtie theory.

This same Long Tail theory has been applied to search in that a handful of queries drive a lot of clicks to a site but in reality there are sometimes tens of thousands of other terms which actually convert at a higher rate.

There was a great article by Danny Sullivan on Search Engine Watch relating to The Long Tail and Search and how the top 50% of searches generated 80% of the search volume.

However there is also the argument that the bottom 20% of searches also generate 60% of the sales. In other words, those terms which are more focused and specific are more likely to convert. For example, a search for a generic term like “lawyer” is less likely to convert that “New Jersey divorce lawyer.”

Therefore, as a search engine marketer we need to not only focus on those high traffic more generic words, but also put some effort towards lower traffic, but more than likely higher converting keywords.

This is a common tactic in todays SEM. In fact it’s the norm for PPC marketers – to have exposure on high traffic (and high cost terms) even if they don’t get clicks. This helps build brand awareness. Then the budget is more focused on focused terms that generally cost less but convert at a higher rate.

Or organic search marketers we see this every day. My large clients get tons of clicks from more generic phrases but the conversions happen on the more specific terms.

And my biggest problem with this right now is that I have no easy way to measure those thousands (And sometimes tens of thousands) of individual conversions effectively.

I feel today’s analytics packages need to be more flexible and automatically group referrals into keyword baskets, much like today’s modern PPC programs.

This way you could easily determine if a Geographic referral (for example) is “worth” more than a product specific referral.

Through grouping of keywords much better analysis of traffic, both paid and organic, could be completed to determine where tactics and strategy could better be applied.

For example, using the above situation, if my site is positioned well organically for both geographic and product specific terms, yet I am seeing more geographic referrals which lead to conversions, then I will want to develop a strategy to enhance this. I would want to emphasize my organic geographic placement and try to further increase my rankings here.

But without the grouped analytics I’ve mentioned this is difficult to do at this time.

For example, one of my clients is a high profile legal site which gets millions of search referrals from hundreds of thousands of visitors every month. Of the hundred thousand or so Google referrals, I know that about 75% are more generic terms, but what I really want to know is whether the other 25% – the better converting terms – are primarily geographic terms (like New Jersey Lawyer) or searches for specific types of lawyers (like divorce lawyer).

However, because there are so many terms in the Long Tail I can’t easily determine this.

Therefore I am calling on Analytics vendors to address this situation. I NEED to know what terms are better for my clients. Sure I can guess that geographic terms are likely better in this case, but I need proof and I can’t easily get it.

Analytics should be more like search engines in a sense. Rather than showing me all the referrals, show my the organic referrals from Google which may contain a location. And don’t make my have to type in the location (although make it an option), the analytics should be smart enough to group keywords, with some guidance.

Why can’t Hitbox, Urchin and Webtrends understand that bankruptcy terms and financial terms may be related, or that Paris and Europe terms are related.

There needs to be more intuition programmed into the analytics to help the average user be able to determine where to focus.

That being said, if there was such functionality in an Analytics package I would think it would become one of the most used features. Because if I could slice and dice the data umpteen different ways to find out if New York is more popular than New Jersey (in terms of search) then i would be able to better target both my paid and organic campaigns.

That’s because I could see that if I’m paying higher PPC costs for New York terms, but find that New Jersey actually converts better then I will shift budget to New Jersey terms.

Conversely, if I find New Jersey terms convert better and I’m doing paid campaigns for both NY and NJ even though I’m ranking highly for both, why wouldn’t I try moving some budget to other areas which could do better?

Also, having such intuitive analytics could help find those organic terms which have lower PPC costs because others haven’t considered them yet conversions are good.

As you can see, there could be many different ways to use an analytics package that was able to more effectively allow you to use the data.

Because if you can increase the occurrence of higher converting keywords in both your organic and paid campaigns you will increase your sales. Even if your total search volume drops because less emphasis is placed on generic terms, as long as there’s a positive ROI in the end, does it matter?

Consider it another way: My experience has shown me that more specific terms have higher conversion rates than more general terms. Even if the general terms gets more traffic, there are a greater earnings from the specific term.

The point I’m trying to make is that even though a term has high referrals, it isn’t likely as highly converting as other terms. Therefore one needs to be able to see the whole picture and with today’s analytics this is extremely difficult without lots of manual intervention (exporting to a spreadsheet or database and performing complex analysis).

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Rob Sullivan is a SEO Consultant and Writer for Textlinkbrokers.com

Screen Shot 2014 04 15 at 7.21.12 AM Long Tail Search & Web Analytics
Loren Baker is the Founder of SEJ, an Advisor at Alpha Brand Media and runs Foundation Digital, a digital marketing strategy & development agency.
Screen Shot 2014 04 15 at 7.21.12 AM Long Tail Search & Web Analytics

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13 thoughts on “Long Tail Search & Web Analytics

  1. Many thanks for creating this blog site. this is really helpful for me and my friends!
    We would be honored if we could be added to this blogger. We are from the World Business for sale is the leading independent businesses for sale listing service.

  2. Funnily enough, I was writing about this earlier today on my “corporate” blog, before I read this entry:
    Traffic not position is important.

    The trouble is, you’re looking to use analytics to optimise for longtail – but IMO that’s not how it works. What you do is ensure that you’re targeted pages are targeted for a whole range of keywords. That way, you don;t simply target individual keywords, but combinations of keywords.

    This is where SEO for longtail comes into play – not through prediction, but through availing yourself to the dynamic possibilities.

    2c.

  3. Pingback: - Tech Mentat
  4. You may wish to consider joining the discussion at the HitTail forum. It’s a product in formation that specifically addresses long tail keywords. There is no conversion tracking and dropping into keyword buckets, but we could very easily develop that if people expressed the interest.

  5. Rob,
    We developed a product that I believe addresses some of the needs you express here. In particular, it groups long-tail search queries in ways that make them more actionable for SEO and PPC efforts. You can try it free (or check out the demo) at http://www.concentrateme.com.

  6. There is great value in a well-managed long tail. The tricky and somewhat arbitrary aspect of measuring the long tail is defining the head/tail demarcation. I like the idea of considering a subset that can be managed by hand as the head. We find that an useful way to compare campaigns with each other and over time is to plot the cumulative contribution to total revenue by the top x% keywords in terms of traffic. You can see an example of such a plot in some of our own long tail research:

    http://www.clicks2customers.com/c2cblog/paid-se

  7. There is great value in a well-managed long tail. The tricky and somewhat arbitrary aspect of measuring the long tail is defining the head/tail demarcation. I like the idea of considering a subset that can be managed by hand as the head. We find that an useful way to compare campaigns with each other and over time is to plot the cumulative contribution to total revenue by the top x% keywords in terms of traffic. You can see an example of such a plot in some of our own long tail research:

    http://www.clicks2customers.com/c2cblog/paid-se