It depends. There are three different types of long-tail keywords, and some are no easier to rank for than their short-tail counterparts, while one type has only recently become visible thanks to AI-powered research tools.
In this article, you’ll learn the differences between all three types of long-tail keywords, why they matter more than ever in an AI-driven search landscape, and how to find and target them.
Long-tail keywords are search queries that get a small number of searches per month. They tend to be longer and more specific than their “head” or short-tail counterparts and, therefore, often have a higher conversion rate.
For example, the keyword “meditation” is a “head” keyword because it gets 211k searches per month. The keyword “can meditation make you smarter” is a long-tail keyword because it only gets 50 searches per month.
Long-tail keywords got their name from their position on the “search demand” curve. If we plot all search queries that people have performed in Google in the course of a month and order them by their search volumes, it’ll look somewhat like this:
At the “head” of the curve, we have a tiny number of keywords with super high search volumes, while the “tail” consists of billions of keywords with very low search volumes.
In Ahrefs’ U.S. database, there are just under 18,000 keywords with search volumes of more than 100k searches per month.
On the other hand, there are 2.3 billion keywords that have fewer than 10 searches per month.
Here’s what this looks like in a pie chart:
Keywords with fewer than 10 searches per month account for almost 93% of our U.S. keyword database. This should not really come as a surprise, given that roughly ~15% of daily Google searches are new and have never been searched before.
But to qualify as “long-tail,” a keyword doesn’t necessarily have to get fewer than 10 searches per month.
There’s no specific search volume threshold that defines a keyword as “long-tail”. It mostly depends on the head keyword you’re comparing it to.
What is a big mistake, though, is defining long-tail keywords by their length in words. There are many one-word keywords that get fewer than 100 monthly searches, and there are keywords five words long (or more) with hundreds of thousands of monthly searches.
So it is not the length of a keyword that makes it a long-tail. It’s the search volume of that keyword.
Here are three reasons why you should consider making long-tail keywords an integral part of your SEO strategy.
Reason 1. Long-tail keywords are (generally) a lot less competitive
Let’s say you’ve just launched a blog about cryptocurrencies. There are lots of popular keywords with high search volumes that could potentially drive LOADS of traffic to your blog:
Those keywords are tempting to target. But let’s be real. What are the chances that your blog will rank for any of them anytime soon?
All of the above keywords have a high Keyword Difficulty (KD) score. This means it’ll be incredibly hard to get to the first page of Google for any of them. And if your website is brand new, that feat will be plain impossible.
Now, let’s look at some of the less popular search queries for the topic of Bitcoin:
Their keyword difficulty scores are low. This means that even a new website has a chance to rank in the top 10 search results and get a few visitors from those keywords.
Reason 2. Long-tail keywords are (generally) easier to address
Let’s continue comparing the two sets of keywords above.
“How to buy bitcoin” seems like a rather straightforward question to answer. But if you look at the top-ranking page for that search query, it is actually 3,400 words long.
Conversely, the top-ranking page for “how to cash out large amounts of bitcoin” is just 1,000 words long.
The more general the search query, the more detail you’ll have to include when addressing it. Specific queries can often be answered briefly and still satisfy the searcher.
You can also find groups of similar long-tail queries and address them with pages that share most of their content. For example:
The same website builder would likely suit therapists just as well as teachers or actors. This means you can target all these different searches with pages that have 80% similar content, while the remaining 20% will be tailored to each user profile.
Reason 3. There are LOTS of long-tail keywords
Yes, each individual long-tail keyword won’t open floodgates of traffic to your website. But as you address more and more of them, the search traffic will eventually compound to something pretty substantial.
Back to the example of “best website builder for __” keywords. A search in Ahrefs’ Keywords Explorer returns over a thousand of them:
Since there are lots of long-tail keywords in pretty much every industry, you’re unlikely to suffer from a shortage of them
Not all long-tail keywords are the same.
Some long-tail keywords represent unique search queries, while others are merely less popular variations of more popular topics. Some are searched for by humans on conversational AI platforms; others are synthetically generated by AI to gather additional context before generating a response to a user‘s prompt.
Understanding the differences between them shapes how you find and target them.
Type 1: Supporting long-tail keywords
Some long-tail keywords are simply less popular variations of a more popular query. We call these supporting long-tail keywords.
“Best healthy treats for dogs” is a long-tail keyword because it receives only 100 monthly searches:
But there are other search queries that mean the same thing with much bigger search volumes:
If you search for each of these in Google, you’ll find the same pages ranking at the top. Google understands that different people phrase their searches differently while looking for the exact same thing, so it ranks the same pages for all these variations.
This means if your page ranks for “healthy dog treats” (6.8k searches), it will automatically rank for many of its long-tail variations too. You don’t need a separate page for each one. Rather, you should target them all with a single page.
But how do you know if a long-tail keyword you’re looking at is a part of a broader topic or not?
Here at Ahrefs, we developed a feature called Parent Topic to help you identify when this is the case.
Enter any keyword into Keywords Explorer, and we check the top-ranking page for that keyword and see if there’s a more popular search query this page is ranking for.
This is how it looks for “best healthy treats for dogs”:
The Parent Topic here is “healthiest dog treats” (2.5k searches). If you rank for that search query, you’ll automatically rank for many of its supporting long-tail keywords too.
Type 2: Topical long-tail keywords
Now let’s look at a different kind of long-tail keyword: “fly bites on dogs ears.”
This time, the Parent Topic is the same as the original keyword, meaning this is genuinely the most popular way to search for this thing.
You can safely target it with a dedicated page, and once you rank for it, you’ll automatically rank for all its less popular variations too:
Things aren’t always this clear-cut, though. Take “natural sleep aid for dogs”, for instance.
The Parent Topic suggests it’s just a variation of “sleep aid for dogs” (1.6k searches), but the word “natural” makes it meaningfully distinct. People searching for it want a natural alternative to pharmaceutical options, and the current top results don’t specifically address that.
So there’s a good case for creating dedicated content for that keyword. The absence of a page that directly addresses it is an opportunity.
In other words, you shouldn’t always blindly trust whatever the Parent Topic tells you. It’s just a computer algorithm and has its flaws and limitations. It is always a good idea to analyze the top-ranking pages for your keyword and figure out if your search query represents a distinct topic or a part of a broader topic.
Type 3: Conversational long-tail keywords
The search demand curve has always had a long tail. But AI-powered search has extended it into a territory where individual queries have effectively zero measurable search volume, yet real demand exists in large quantities.
We call this the conversational long-tail, though it’s also sometimes referred to as the “infinite tail”.
Conversational long-tail keywords are the queries people search on AI platforms like ChatGPT, Gemini, and Google’s AI Mode.
Unlike a traditional Google search, where years of conditioning taught people to compress their needs into two or three words, AI platforms invite full sentences, context, and nuance.
For example, “What’s the best type of meditation for someone who struggles to sit still and has about ten minutes in the morning?” is a real expression of demand, but it’s unlikely to be phrased identically by any two people. This means it will never accumulate enough repetitions to register in a traditional keyword database.
Topical and supporting long-tail keywords have low search volume. But over 95% of conversational long-tail keywords have no measurable search volume. Not because nobody is searching, but because everybody is searching differently.
There’s a second layer to this, too.
When you ask an AI platform a complex question, it doesn’t just search for your exact words.
It quietly breaks your question down into smaller, simpler sub-questions, searches for answers to each, then combines them into a single response. This process is called query fan-out.
Your content might be pulled into an AI response not because it matched the user’s original prompt, but because it answered one of the sub-queries the AI generated from it.
In other words, the conversational long-tail includes not just what people type, but all the related questions AI systems generate in the background to answer them. You can uncover these using Ahrefs’ Brand Radar:
From an SEO perspective, this changes what it means to target a topic.
Your content is no longer evaluated just against the phrase someone typed. It’s also assessed across a broader network of related questions synthetically generated by AI, most of which also have zero recurring search volume.
Now that we’ve covered all three types of long-tail keywords, let’s look at how to turn them into a strategy that actually drives traffic.
There are a number of methods you can use to find long-tail keywords. Here are the most effective.
1. Use Ahrefs’ Keywords Explorer
The fastest and most reliable method for long-tail keyword research is to use a purpose-built platform like Ahrefs.
Search for any word or phrase that defines your niche in Keywords Explorer. Navigate to the Matching terms report, and then use the search volume filter to surface thousands of long-tail keywords instantly.
If your website is new and has limited authority, use the filter for keyword difficulty to find the least competitive options.
Also, make sure to try the Questions tab. It returns long-tail queries phrased as questions, which tend to be among the easiest to rank for and the most likely to trigger AI responses in search results.
2. Check which keywords your competitors rank for
Another great source of long-tail keywords comes from your competitors since they’ve likely already done a lot of the hard work for you.
Plug any competitor’s URL into Site Explorer, go to the Organic keywords report, and use the filters to find long-tail opportunities they’re already capturing.
Consider using filters like:
- Intent: To remove any of their branded keywords.
- Volume: To show lower-volume keywords that are more likely to be in the long tail of the search demand graph for your topic.
- Difficulty: To find easy opportunities, you may also be able to rank for.
- Organic traffic: To see which keywords deliver clicks and visitors to your competitors and are worth targeting first.
Repeat this for five to ten competitors, and you’ll have enough long-tail keyword ideas to keep your content production going for months.
3. Mine AI platforms for conversational long-tail keywords
AI platforms are now among the richest sources of long-tail keyword intelligence. Not because they give you search volume data, but because they show you how people actually talk about topics when they’re not compressing their thoughts into a search box.
Ahrefs’ Brand Radar gives you structured access to this data across six AI search platforms, including AI Overviews, AI Mode, ChatGPT, Gemini, Perplexity, and Copilot.
For each platform, you can see the queries that are prompting AI responses, the responses themselves, and which brands are being mentioned.
You can also see the fan-out queries for some of these platforms, like ChatGPT and Perplexity:
To extract additional long-tail variations from this data, try pasting a set of prompts and fan-out queries from Brand Radar into an LLM like ChatGPT or Claude with a prompt like:
“Based on these queries, what are the most common underlying questions and subtopics people are trying to understand? List them as specific long-tail keyword variations.”
This turns a set of AI platform signals into a structured list of content opportunities in seconds.
4. Browse niche forums, communities, and social media channels
Niche forums, communities, and social channels remain a valuable source of long-tail keywords precisely because people ask questions there in their own words; unfiltered, specific, and often more revealing than anything you’d find in a keyword database.
For example, here’s a question posed on Quora:
A quick check in Ahrefs’ Keywords Explorer reveals that “marketing manipulation” is actually a long-tail keyword with a low difficulty score.
The upside of this method is that forum threads have some good discussions, which can be useful when creating content on that topic.
The downside is that mining long-tail keywords from forums can be a rather tedious process.
What makes this data even more valuable today is that these same sources of conversational queries and data (Reddit threads, YouTube videos, Quora discussions) are among the most frequently cited sources in AI-generated responses.
So the questions people ask there aren’t just long-tail keyword opportunities for traditional search; they’re a signal for the kind of content AI platforms are actively pulling from when answering questions in your niche.
That’s why we’ve been adding many new reports to Brand Radar to help make this process easier.
The Reddit report surfaces threads where your brand or competitors are mentioned, so you can see exactly how people are talking about topics in your niche and how these conversations surface in search results.
Rather than spending hours manually browsing subreddits, you get a structured, filterable view of the conversations that matter.
You can also get some more insights from the YouTube and TikTok reports, which show videos that are commonly cited in AI answers.
Video titles and transcripts are a particularly rich source of conversational long-tail language. Creators tend to explain topics the same way their audience thinks about them, which means the language in transcripts closely mirrors the kind of natural, specific queries that make up the conversational long-tail.
To get the most out of this conversational data, try copying a set of thread titles, questions, and discussion snippets into an LLM and asking it to identify recurring questions and long-tail keyword patterns across them.
For example: “Based on these community discussions, what are the most common underlying questions people are trying to answer? List them as specific long-tail keyword variations I could target with content.”
What would otherwise take hours of manual browsing becomes a few minutes of structured analysis, and the output maps directly to content opportunities in both traditional and AI-powered search.
5. Use Google Search Console for longer query data
If your site already gets traffic, Google Search Console is an underused source of long-tail discovery.
Go to the Performance report and filter for longer, more conversational queries by using regex, like:
^(how|why|what|which|where|when|can|is|are|does|should)
This returns all queries containing a question, which tend to be longer, more specific, and more conversational than head terms.
These are your easiest quick wins, surfacing pages already ranking for queries you’ve never directly targeted that could use a bump to improve performance.
Final thoughts
Long-tail keywords have always been the underdog opportunity of SEO: low volume individually, but powerful in aggregate. That hasn’t changed.
What has changed is the length of the tail.
As AI search encourages people to ask questions more expressively, queries are getting longer, more specific, and increasingly unique. The tools doing the answering are now AI systems that synthesize across many sources rather than serving up a single ranked result.
The fundamentals remain the same: find the topics your audience cares about, address them thoroughly, and build content that compounds over time. The difference now is that “thoroughly” means covering the full cluster of intent around a topic, not just the phrases that show up in a keyword tool.
If you have any questions, feel free to reach out on X/Twitter.
