Lengthy Tail web optimization in 2021: How You Can Have It All or Die Making an attempt
Keywords. Long-tail keywords. The chunky middle. The chonky thorax. Is it any wonder why most people outside of SEO think we’re talking gibberish? If you ask a dozen SEOs which keywords are considered “long-tail”, you get 13 opinions and 17 fistfights.
We can agree that with advances made by Google in natural language processing (NLP), the long tail of search has exploded. However, I will argue that NLP has imploded the long tail too and understands how and why our collective health can be saved.
What Exactly is the Long Tail of SEO?
The long tail of search is the unlimited space for low volume (and often low competition) keywords. Tactically, long-tail SEO focuses on competing for a large number of low-volume keywords rather than focusing on a small number of high-volume keywords.
Long-tail SEO encourages us to let go of vanity as high volume so-called “vanity” keywords are often inaccessible or at best empty our bank accounts. Low volume keywords may seem less attractive on the surface, but when you start competing with hundreds or thousands of keywords they represent more traffic and ultimately more sales than some vanity keywords.
You’ve probably seen a graphic of the long tail like the one above. It’s a very nice performance curve, but purely hypothetical. And while you might smile and nod when you see it, it’s difficult to translate this into a world of keywords. It might be helpful to re-imagine the long tail of SEO:
I’m not sure if the “Lying Snowman of SEO” will ever catch on, but I think it helps to illustrate that while head keywords are high in volume on their own, the combined volume of the long tail is the head or the head Head darkens middle. Like the familiar curve, this visualization dramatically underestimates the true girth of the long tail.
What are long-tail keywords?
In the words of the old SEOs, “It depends.” Typically, long-tail keywords are low-volume, multi-word phrases. However, the long tail is relative to your starting point. In the past, it was believed that any piece of the long tail was less than competitive. However, this is changing as people begin to see the benefits of targeting certain phrases with clear intent (especially commercial intent).
Not only is “widget” targeting expensive, but the intent of the finder is ambiguous. Targeting “Buy Blue Widgets” will limit the intent and “Where to Buy Acme Widget LOL-42” will laser focus you on an audience. As searchers and SEOs adapt to natural language searches, previously “long-tail” keywords can result in higher volume and competition.
The long tail exploded
Google told us that 15% of the searches they see every day are new. How is that possible? Are we creating so many new words? This is sus, bruh!
I can explain it to you in a very short story. The other day my (half-Taiwanese) 10 year old daughter couldn’t remember what her Chinese zodiac sign was and asked Google Home:
Hey Google, what’s the beast for the 2010 Chinese New Year Calendar thing?
It’s easy to get into the voice devices aspect, but whether or not you believe in the future of voice devices, the reality is that voice search in general has increased the need for natural language search and Google is just getting better When dealing with natural language, we use it more often (this is our default mode). This is particularly evident in children who never had to learn to obscure their search for outdated algorithms.
How can we hope to target keyword phrases that literally evolve as we speak? Fortunately, NLP cuts both ways. As Google understands the context better, the algorithm realizes that many variations of the same phrase or question are essentially the same. What leads us to …
The long tail has imploded
In 2019, I conducted a keyword research case study at SearchLove London on British mega-retailer John Lewis. During my research, I was surprised to see how many searches Google automatically redirected. It’s obvious that Google is assuming that people who searched for “Jon Lewis” in the UK probably meant “John Lewis” (sorry, Jon):
It is interesting to note that Google is gradually and quietly moving away from the previously prevailing “Did you mean?” among the more assertive (some may say aggressive) “showing results for …” In this case, optimization is probably pointless for Jon Lewis in the UK.
I was expecting a rabbit hole, but ended up in a full hare abyss. Consider this search:
Hjohjblewis ?! I came across this misspelling quite by accident, but I imagine it is an attention deficit cat and a keyboard adjacent to a cat. This level of rewrite / redirect was shocking to me.
Misspellings are just the beginning, however. What about very similar long-tail phrases that don’t show any kind of rewrite / redirect but show very similar results?
Note that in the US, these terms mostly produce results about former US Representative and civil rights leader John Lewis, showing how much not only can intentions shift between locations, but how Google’s reinterpretations change dynamically can.
In the same year, I did an experiment for MozCon that looked at long-tail issues, e.g. B. “Can you undo a 301 redirect?” To demonstrate that posts written around a particular question can often apply to many forms of that question. At the time, I had no way of measuring this phenomenon other than showing that the post was graded for variations of the phrase. I recently re-analyzed my 2019 keywords (with rankings starting April 2021) using a simplified form of rank-based overlap (RBO) called RBOLite. RBOLite ranked the similarity between two lists, giving a score of 0-1. As the name suggests, this score tends towards the higher-ranking items, so a shift at # 1 has more impact than a shift at # 10.
Here are the results for a selection of the phrases I followed up for the 2019 post, with the post title displayed at the top (and with a perfect match of 1.0):
You can visually see how the similarity of the results differs when changing and removing certain keywords and how this leads to a complex interaction. What intrigues me is that changing the question set from “Can you” to “How are you?” Or “How are you?” Made little difference in this case, while removing “301” or “Forward” had more impact. Switching from “you” to “I” was relatively minor per se, but had a positive effect on other changes. Even the SERPs with “Undo” instead of “Reverse” were fairly similar, but this change had the biggest impact.
Note that the weekly RBOLite score for the original phrase was 0.95, so even the same SERP will vary over time. All of these values (> 0.75) have a reasonable degree of similarity. This post ranked first for many of these terms, so these scores often represent shifts further down the top 10.
Here is another example based on the question “How do I upgrade my domain eligibility?” As above, I recorded the RBOLite similarity scores between the main phrase and the variations. In this case, the weekly score was 0.83, which suggests some background flow in the keyword area:
An immediately interesting observation is that the difference between “improve” and “increase” was negligible – Google easily equated the two terms. My time was spent debating which keyword to use to eat other projects or sandwiches. As before, the change from “How do I” to “How do you” or even “How to” made relatively little difference. Google even understood that “Domain Authority” is often replaced by “DA” in our industry.
Perhaps counterintuitive, adding “Moz” made more of a difference. This is because the SERP became more brand-like (Moz.com received more mentions). Is that necessarily a bad thing? No, my post is still number one. However, if you look at the entire first page of the SERPs, adding the brand name leads to a pretty significant shift in intent.
The long tail is dead. Long live the long tail.
For the past decade, the long tail has exploded and then imploded (in many ways due to the same forces), and yet somehow we ended up in a completely different keyword universe. So where are we – the poor souls who are destined to wander this universe?
The good news in this post (I hope) is that we don’t have to work our way to death to target the long tail of Search. It doesn’t take 10,000 pieces of content to rank on 10,000 variations of a phrase, and Google (and our visitors) would greatly prefer it if we didn’t spin that content. The new post-NLP long tail of SEO requires us to understand how our keywords fit into semantic space, map their relationships, and cover the core concepts. While our tools will inevitably improve to meet this challenge (and I am directly involved in such projects at Moz), our human intuition can go a long way for now. Study your SERPs carefully and you can find the patterns to turn your own long tail of keywords into a chonky thorax of opportunity.