Search Tactics: What Is TF-IDF and Should You Lose Sleep Over It?

Search Tactics: What Is TF-IDF and Should You Lose Sleep Over It?

A silver bullet. A magical formula. An undiscovered tool.

Is TF-IDF worth the recent hype? Those on the lookout for a leg-up in the fickle world of SEO will have asked the question, “What is TF-IDF?” at some point.

Have you?

We’re unpacking this concept to determine how important it is in today’s ranking war, and if it is something your team should invest time and effort into. Let’s learn together.

What Is TF-IDF?

Term Frequency Inverse Document Frequency (TF-IDF) is a formula that scores or weights a word or term and renders it as a number, or vector representation. In this formula, each term has a term frequency value (TF) and an inverse document frequency (IDF) value based on a corpus or set of documents.

What is its purpose?

The theory behind this formula is to highlight terms and phrases that are important to your business and compare them to similar documents online. The idea is to filter out commonly used words and give weight to less frequently used terms that your business wants to rank for.

For example, words such as “and”, “but”, “the”, and “like” will appear in most documents. Logically, if a word appears in every document, then it may not matter as much in distinguishing between them. This is why we need to weigh the frequency of the term against its relevance.

What Is the TF-IDF Formula?

The TF-IDF formula to ascertain numerical score looks like this:

Wt,d = TFt,d log(N/DFt)

This represents a term (t) in a document (d), the weight (Wt,d) where TFt,d is the number of instances in the document. DFt is the number of documents containing the term and N is the total number of documents in the set or corpus.

A TF-IDF Example

Let’s assume the corpus contains 10 million documents but only 3 million of those contain the word “goldfish”, which is the term you’re interested in. You’ve written a 100-word post on “goldfish” in which it appears 12 times. Your formula will look something like this:

TF (goldfish) = 12/100 or 0.12

IDF (i.e. log {DF}) is determined by the total documents (10,000,000) divided by the number of documents containing the term “goldfish” (3,000,000).

IDF (goldfish) = log (10,000,000/3,000,000) = 1.52.

Therefore the product of TF*IDF = 0.12 * 1.52 = 0.182.

Thankfully there’s absolutely no need to write this on your hand because there are several tools available that will spit out a TF-IDF score for you in no time at all.

How Useful Is TF-IDF in SEO Today?

With the data science bit out of the way, we want to know how relevant this tactic is in today’s fast-paced SEO world. After all, the concept of TF-IDF is now over five decades old!

As a document retrieval solution, TF-IDF is brilliant. As a search tactic, it has pros and cons.

The Problem With Term Frequency

In the recent past, many businesses would try to rank their content based on keyword density, which is more or less the same as the term frequency technique. However, it was easy to manipulate using black hat techniques such as keyword stuffing. Google and other search engines soon grew tired of this and tweaked their algorithms to compensate.

Are keywords still important? Certainly!

Does knowing the frequency of a particular term in your content compared to your competitors help with your SEO strategy? Not exactly.

Here’s why.

The vector offered up by this formula does not take into account known ranking factors such as:

  • The semantic relationship between words
  • The quality of the content
  • The authority of the writer
  • The context of the search query
  • The links to the content from outside sources

A good SEO strategy includes so much more than a word count and factors in a host of both on-page and off-page signals.

Google Algorithms and Tools

Some of the biggest online brains have developed incredibly smart algorithms and tools to enhance search engine optimization.

By way of example, the Google Hummingbird update allows machines to grasp relationships between words and expressions. The Google Rankbrain algorithm built on Hummingbird to more accurately assess search intent. A close examination of Latent Semantic Indexing (LSI) highlights similar terms and associated phrases to your focus keyword for context.

By using these complex tools, high-quality content can be offered to users in the right context. This is, after all, what search engines should be doing, right?

Where does that leave us on the topic of TF-IDF?

Making Use of TF-IDF in SEO

TF-IDF can add to your overarching SEO strategy. For example, if you’re using a tool such as Semrush to check your TF-IDF score you will be able to collect valuable insights into:

  • What terms are important to your competition
  • What phrases search engines expect to see in your content for a particular topic
  • Similar phrases and niches that you can explore
  • Related topics based on co-occurrences of terms

Certainly, each SEO tweak will take you closer to your goal. Here’s is a sample SemRush screenshot of a cybersecurity keyword prior to optimization, notice how our usage is slightly lower than that of our competitors (you can find this in the content analyzer toolset).

Screenshot of SemRush TF-IDF example

How Would I Use TF-IDF on a Page?

As a stand-alone SEO tactic, this formula can’t offer any long-term advantages. Term frequency on its own is not a strong enough signal to push your content up the rankings.

However, when used carefully in the context noted in the preceding paragraph it can help. Here’s how you do it:

  1. Write your content based on your SEO strategy using natural language.
  2. Choose a TF-IDF tool and enter the phrase you want to optimize.
  3. Make use of the resulting list to enrich your content.
  4. Use co-occurring phrases that fit naturally into your content – don’t force it.

If you feel a headache coming on, we understand; there is a lot to consider.

SEO Strategies Made Simple

How much time and effort should you spend on TF-IDF? Should you use it at all, or add it to your collection of SEO tools? Are you aiming for results similar to this case study where a professional organization achieved 700K in deals and a 932% traffic boost by using the right strategy?

We know the answer, and we think that it’s time to evolve your marketing.

There is no silver bullet when it comes to effective SEO tactics and search strategy. The magic happens when you take a holistic view of your optimization efforts and partner with the right people.

The smart move right now is to contact our experienced team at ProductiveShop and let us walk the path of success with you.

Imran Selimkhanov | Founder at Productive Shop

Imran Selimkhanov

Imran is the founder and CEO of Productive Shop. He writes on B2B demand generation and SEO strategy topics to help startups understand how to win digital share of voice. Prior to Productive Shop, Imran led demand generation at an Oracle consultancy, ran an e-commerce site servicing LE teams and helped build PMO offices at technology startup companies. When he’s not at work, Imran can be spotted hiking in the Rockies, honing his clay shooting skills and tumbling off of black diamond ski tracks due to overconfidence in his skiing abilities.

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