TF-IDF is a fundamental SEO concept, but modern search engines use more advanced AI-driven approaches. Understanding TF-IDF helps content creators write better, more relevant content, but it should be combined with AI-based strategies for the best results.

By Andrew Juma – Founder of The AJ Center, an award-winning end-to-end digital marketing firm. Follow Andrew on LinkedIn.

TF-IDF, which stands for Term Frequency-Inverse Document Frequency, is a mathematical model used to determine how important a word is in a document relative to a collection of documents. It was originally developed in the field of information retrieval and text mining, but today, it plays a significant role in search engine optimization (SEO) by helping to analyze the relevance of content on a webpage.

Although Google’s search algorithms have evolved with artificial intelligence (AI) and machine learning, TF-IDF remains an essential concept in SEO. It is used by some SEO tools to evaluate the importance of keywords in a page’s content compared to top-ranking competitors.

This article explains what TF-IDF is, how it works, its significance in SEO, its limitations, and how it can be used to improve content rankings on Google.

What is Term Frequency (TF)?

Term Frequency (TF) is a measure of how often a word appears in a document. The formula for calculating it is:

TF = Number of times a term appears in a document / Total words in the document

For example, if the word "SEO" appears 15 times in a 1,500-word article, the TF score would be 15/1500 = 0.01.

What is Inverse Document Frequency (IDF)?

Inverse Document Frequency (IDF) is used to measure how rare or unique a word is across multiple documents. A term that appears in many documents has a low IDF score, while a term that appears in very few documents has a high IDF score. The formula for IDF is:

IDF = log(Total number of documents / Number of documents containing the term)

For example, if the word "SEO" appears in 10,000 documents out of 1,000,000 total documents, then the IDF score would be:

IDF = log(1,000,000 / 10,000) = log(100) = 2

This means "SEO" is common across many documents, reducing its significance in ranking calculations.

How TF-IDF is Calculated

The TF-IDF score is calculated by multiplying TF by IDF:

TF-IDF = TF × IDF

A high TF-IDF score means the word is highly relevant to a specific document and could be important for SEO.

How Search Engines Use TF-IDF

Google’s Algorithm and TF-IDF: While Google does not directly use TF-IDF in its ranking algorithm, the concept still plays a role in how search engines understand content relevance. Google employs machine learning models like RankBrain and BERT to analyze the significance of words in context. However, these models are based on TF-IDF principles to determine how well a webpage matches a user’s query.

For instance, if a TF-IDF analysis shows that certain high-ranking pages frequently contain terms like "SEO best practices" and "Google ranking factors," including these terms naturally in content may improve relevance.

TF-IDF vs. Keyword Density

Keyword density simply counts how many times a keyword appears, while TF-IDF measures term importance relative to other documents. This makes TF-IDF more effective than keyword density, as it prevents keyword stuffing and encourages natural writing.

How to Use TF-IDF for SEO

TF-IDF Tools for Content Optimization:

• Surfer SEO – Compares TF-IDF scores of top-ranking pages and suggests missing keywords.
• SEO PowerSuite – Analyzes the most important terms competitors use.
• Ahrefs Content Explorer – Identifies underutilized keywords with high search intent.

Steps to Optimize Content Using TF-IDF:

• Analyze top-ranking pages for important TF-IDF terms.
• Compare missing words between your content and competitors.
• Naturally integrate high-TF-IDF words into headings, body text, and metadata.
• Ensure content readability to avoid keyword stuffing.
• Monitor ranking improvements by rechecking TF-IDF scores.

Real-World Example: TF-IDF in SEO Strategy

A case study by Search Engine Journal found that pages optimized with TF-IDF terms experienced 12–18% ranking improvements within two months.

Limitations and Future of TF-IDF in SEO

Does Google Still Use TF-IDF? Google’s ranking systems have evolved beyond basic TF-IDF models. Today, the search engine uses:

• BERT – Improves understanding of word context.
• Latent Semantic Indexing (LSI) – Recognizes relationships between words.
• MUM – Understands queries across text, images, and videos.

Alternatives to TF-IDF for SEO:

• Entity-based SEO – Google analyzes topics rather than single words.
• Vector-based Search (Word Embeddings) – Uses AI to understand meaning.
• Natural Language Processing (NLP) Tools – Improve semantic SEO.