{"id":4802,"date":"2024-08-05T13:58:26","date_gmt":"2024-08-05T17:58:26","guid":{"rendered":"https:\/\/beomniscient.com\/?p=4802"},"modified":"2025-12-11T08:37:38","modified_gmt":"2025-12-11T13:37:38","slug":"case-against-keyword-difficulty","status":"publish","type":"post","link":"https:\/\/beomniscient.com\/blog\/case-against-keyword-difficulty\/","title":{"rendered":"The case against keyword difficulty"},"content":{"rendered":"\n

Any prioritization model built upon keyword difficulty is flawed and leads to suboptimal outcomes.<\/strong><\/p>\n\n\n\n

There, I said it. <\/p>\n\n\n\n

Models, including how we prioritize ideas, have inputs and they have outputs. If you have bad inputs, you have bad outputs. And keyword difficulty is a pretty bad input. <\/p>\n\n\n\n

Feeding a Fed Horse: Why KD is (Almost) Useless<\/strong><\/h2>\n\n\n\n

Gaetano<\/a> mentioned most of the flaws with keyword difficulty in the quote above, even going into detail on how Ahrefs and Semrush calculate the metric differently. <\/p>\n\n\n\n

I\u2019ll give you a few more reasons I don\u2019t like it, but here\u2019s my main contention: it has low to no predictive validity. <\/strong><\/p>\n\n\n\n

I only saw one study<\/a> correlating keyword difficulty scores with performance, but the misuse of statistics was quite egregious, barely worth mentioning. For the nerds, see Anscombe’s quartet<\/a> or why correlation doesn\u2019t always mean correlation<\/a>.<\/p>\n\n\n\n

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I would not call this strongly predictive<\/em><\/p>\n\n\n\n

What is Keyword Difficulty?<\/strong><\/h2>\n\n\n\n

It\u2019s a number used to approximate how hard it is to rank for a given keyword. The higher the number, the more difficult it is to rank your page (usually on a scale of 1-100).  <\/p>\n\n\n\n

All tools calculate this their own way. Ahrefs\u2019 score<\/a> \u201cis based on the number of referring domains (RDs) the Top 10 ranking pages (organic search results only) for a given keyword have.\u201d<\/p>\n\n\n\n

Semrush uses a more complex formula<\/a>, a confluence of weighted variables like median number of referring domains, median authority score, search volume, ratio of do-follow \/ no-follow links, presents of featured snippets, etc. <\/p>\n\n\n\n

They\u2019ve also introduced a personalized keyword difficulty score<\/a> that takes your domain into account when scoring opportunities. <\/p>\n\n\n\n

While I think the additional variables and weighting system is a major improvement on the simplistic use of referring domains, there are still many factors it doesn\u2019t account for and cannot predict, some of which I\u2019ll cover below. <\/p>\n\n\n\n

First, Where Keyword Difficulty Can Be Useful<\/strong><\/h2>\n\n\n\n

Let’s start with a note of positivity, a little compromise if you will. <\/p>\n\n\n\n

Keyword Difficulty can be useful as a first pass filter when dealing with large quantities of keywords to sift through.<\/p>\n\n\n\n

For example, if you’re working on a newer website, you can probably safely filter out most 80+ KD keywords. <\/p>\n\n\n\n

More interesting to me is using keyword difficulty as the first pass on additional research. <\/p>\n\n\n\n

Laura Gray<\/a>, Organic Growth Strategist at Omniscient, told me she uses KD as such:

\u201cIt’s sometimes more of a litmus test. You often find that a difficult keyword coincides with a broader term (and usually much higher volume) or a term that is dominated by websites with a strong online presence and domain ratings. If you find your keyword is a high difficulty, is it actually targeted enough?<\/p>\n\n\n\n

An example here is [client]. They are a leader in UGC content, but they don’t rank great for the baseline term ‘UGC’ (46K, very hard). Over time this might be important to the domain, but the reality is they only do UGC content specifically for [audience]. So instead of spending time getting lost in the broad term of UGC, it’s better to target the more focused keywords around [specific audience-related variants of UGC keywords].\u201d<\/p>\n\n\n\n

I had to obfuscate some of the details here, of course, but you get the picture. <\/p>\n\n\n\n

Now, the problems with keyword difficulty…<\/p>\n\n\n\n

KD doesn\u2019t equate to \u201ccost\u201d <\/strong><\/h2>\n\n\n\n

The reason keyword difficulty is useful at all is because it introduces a negative variable in prioritization formulas. <\/p>\n\n\n\n

Volume or impact attempts to quantify the upside. Keyword difficulty, theoretically, should counter that with how hard, expensive, probable, or time consuming it will be to achieve that upside. <\/p>\n\n\n\n

But keyword difficulty, as implemented by popular tools, doesn\u2019t actually do that. <\/p>\n\n\n\n

Here\u2019s a very easy thought experiment. <\/p>\n\n\n\n

Imagine HubSpot, a company with a domain rating of like 1000, has two potential keyword targets with the associated metrics:<\/p>\n\n\n\n