There are more than 1.8 billion websites on the internet. Each one is trying to rank for some keywords with technical SEO and content optimization techniques.
You must be too.
But more often than not, even after putting our heart and soul into planning, creating, publishing, and executing content marketing strategies, our pages don’t rank high enough.
What is the difference between your website and that top-ranking website?
There may or may not be a difference in the look and feel or marketing strategy of your website, but there is a difference between their content and yours.
The difference is they use high TF-IDF words in their content.
In its pursuit of bridging the gap between computers and human language, Google started using an information retrieval method to weigh the importance of specific words on the internet.
This is the TF-IDF method.
In this blog, we will talk about how Google analyzes the quality of relevant content on web pages using the TF-IDF method. We will also share a few useful tips on how content marketers can use TF-IDF knowledge in their SEO strategy to plan better campaigns and drive results.
What is TF-IDF?
TF-IDF stands for ‘term frequency-inverse document frequency.’ It is most commonly used in information retrieval programs of machine learning.
It's a measure of the importance of specific words and phrases in keywords and the general content on the internet.
As part of SEO (search engine optimization), TF-IDF can help find a list of terms to rank higher in search results pages.
For example, say you're looking for information on Python programming. Then, TF-IDF results might indicate that the term “Python” is most important to search engines because it appears more often in top-ranking content than any other word or phrase related to Python programming.
Google search algorithms analyze thousands of web pages related to a search term and identify important contextual words used in the top-ranking pages.
Ultra-common words like “a,” “an,” “in,” “on,” "at," and “the” carry little meaning on their own. They help us establish content flow and practice grammatical accuracy. From a search standpoint, they are not that important. And TF-IDF is programmed to assign such words lesser value than important terms.
The priority of the TF-IDF method is to look for the most value-adding words or phrases related to a keyword or search phrase based on the number of times it appears in relevant content.
Then it compares the frequency of those contextual words in your document with the frequency of those terms in its collection of documents for the primary keyword.
If your content has most of the high TF-IDF words, it is identified as a good candidate for the SERPs the next time somebody searches for something related to the keyword.
Thus, with this method, Google has a comparative understanding of how relevant your content is to the keyword based on how many other contextual words you use besides the primary single-word keyword.
For a better understanding, let’s understand the calculations behind TF-IDF.
The TF IDF formula is a two-part calculation.
The first is term frequency:
TF: Frequency of a word in a document/total number of words
And the second is inverse document frequency:
IDF: log_e(Total number of documents / Number of documents with those words in it)
Let’s try and understand this better with a TF IDF example.
If a term such as “create” occurs 12 times in a document of 100 words targeting the keyword “how to create a cover letter,"
That was the first part of the calculation.
Let’s calculate the IDF.
For simplicity's sake, let's say there are a total of 10,00,000 documents for the target keyword, and the word "create" appears 409,000 times in those documents.
Your IDF value would be: IDF(create) = log_e(10,00,000/409,000) = 0.38
With this calculation, we now know the relative importance of the term “create” in targeting a keyword like “how to create a cover letter." The next step is to multiply both and find the importance of the word “create” when writing about “how to create a cover letter.”
Your TF x IDF score would be = 0.12 * 0.38 = 0.046
A high TF-IDF score means that the term is very important and used on all the top-ranking web pages. In contrast, a low TF-IDF score means that the term is rarely used in the corpus of documents.
The TF-IDF score of various terms helps Google understand the contextual value of words other than the regular keywords and gauge the quality of content on websites.
Take “email marketing,” for example.
The TF-IDF words for a keyword like “email marketing” could be “decision,” “advertising,” “abandonment,” “autoresponder,” and “converts.”
Here is a screenshot of the TF-IDF results for “email marketing.”
There are many tools online that will do the TF-IDF calculations for you and produce a list of important words related to your primary keyword.
The above screenshot is from Seobility’s TF*IDF Tool. It also shows a helpful graph about words with high TF-IDF scores related to your keyword. Other similar tools are Ryte, TF-IDF Tool, and Rankranger.
From the results above, we can pick a few topics for content marketing campaigns, such as
- “Planning” email marketing “campaigns”
- Best “time” to “send” “emails”
- Email marketing “content”
- “Best” email marketing “subjects”
- How to deal with “abandonment” in email marketing
PS: We have picked these words from the results above.
If you use these words in your on-page SEO, you could include a combination of TF-IDF + primary keyword in the meta description, meta title, and structured schema. This is how you can use TF-IDF for technical SEO and come up with content topics to create amazing content with high-ranking potential.
However, for the content of your blogs, a slightly better way to find important words is NLP analysis. It builds on the logic behind TF IDF in SEO and factors in real-world user behavior on search. It gives weight to the words that people use while searching for information. This way, you have the best of both worlds, competitor content, and user search behavior.
A tool like Scalenut can help you find the most important NLP terms for your target keywords and location. The advanced Scalenut NLP analysis feature combs through thousands of web pages and search terms to give you a list of the most important NLP terms for your content.
TF-IDF is not keyword stuffing
TF-IDF is not keyword stuffing. It is similar to keyword density but more complex in its calculations.
In fact, it is the antidote to keyword stuffing. By understanding the value of various words related to a keyword, Google search algorithms can detect keyword-loaded web pages that offer little to no value for visitors.
As we saw in the above example, TF-IDF helps you find impactful words that add value to your content. These words should be used in addition to and not as a replacement for your keywords. Instead of stuffing, TF-IDF helps content creators find additional words to help them rank for your target keyword.
However, with rapid developments in AI technologies like NLP, there is a better way to find such words.
NLP analysis: A better alternative to TF-IDF analysis
Instead of depending on a single method, NLP helps search engines literally understand the meaning of words in a sentence.
Google’s BERT update was the first major NLP search algorithm. BERT stands for bidirectional encoder representations from transformers.
This goes a step ahead of the TF-IDF approach. Instead of evaluating words randomly, NLP helps the search algorithm understand the meaning of words bidirectional, i.e., in context to the entire sentence.
With the rise of voice search, Google needs to know what users mean when they search for something. From processing spoken language to replying, everything is done with the help of NLP programs in the background.
The content industry is changing rapidly to adapt to this change in search algorithms. AI-powered content and SEO tools like Scalenut enable content marketers to create high-quality search-engine-optimized content.
Platforms like Scalenut are great tools for content marketers because they can find important NLP terms, get competitor outlines, research the most important topics for those NLP terms, and create content on those topics.
Optimize your content for Key Terms using NLP analysis
At Scalenut, we understand the importance of TF-IDF words and amplify their logic with NLP and in-house smart linguistic analysis algorithms to determine which words will produce the best results.
With every SEO Document you create, Scalenut produces a detailed analysis of the top-ranking web pages on the internet. This analysis contains a list of high-TF-IDF key terms related to your target keyword, searched by the majority of users in your target location.
Using these NLP key terms in your content will increase the indexability and credibility of your content. When search algorithms analyze your text, they will realize that you have used the best TF-IDF words most naturally. They will start showing that web page as a search result for your target keyword.
That is our dream journey envisioned for every Scalenut user. And it has come true for numerous people.
Notice how we used NLP terms to amplify certain sections of the content?
Instead of “community for your brand,” “community of subscribers for your brand” adds a well-defined meaning to the sentence.
And isn’t the ultimate goal of email marketing to generate a long list of subscribers? Including the word “subscribers” sounds more “natural,” doesn’t it?
The idea behind NLP terms is to help marketers choose better words for their content.
When should you use TF-IDF analysis and NLP Terms
As a marketer, the most important part of your job is ensuring that the content you create makes back twice as much as what you put into it. TF-IDF is just one of several factors that Google uses to identify good content and produce the best search results.
Calculating TF-IDF is no small feat. It requires serious computational powers that are beyond those of a normal computer. And the tools that offer a TF-IDF analysis usually test and score words on a much smaller data set than Google’s index.
The words that the tools suggest may or may not be the most important, as Google’s data set and its TF-IDF algorithm work with more words and a larger pool of documents.
That being said, knowing what TF-IDF is and how it works will help you find relevant terms associated with the primary keyword. When combined with NLP terms, it will act as a north star for finding content gaps and creating SEO topics that are sure to rank well.
The following are a few ways you can use TF-IDF analysis and NLP terms in your content marketing strategy:
- Upgrading existing content to ensure that you use all the important related terms to your primary keyword.
- Aligning your blogs with a single core topic. TF-IDF and NLP terms related to your primary keyword can help you develop an extensive blogging calendar with a series of related insightful blogs.
- Quickly analyzing the depth and breadth of the content in your industry niche. A TF-IDF analysis of the most common keyword of your industry will tell you what topics have been covered already and which topics are there for the taking.
Note: TF-IDF is not the only way, always create content focused on your users
Optimizing your content with the help of TF-IDF and NLP terms is a great way to increase the search engine relevance of your content.
However, it is extremely important that we always keep the internet user in mind while creating content. The most effective way of getting that top-ranking position in SERPs is by publishing high-quality, actionable content for your target audience.
At Scalenut, we love content and pride ourselves on having the best content marketing and SEO platform, but we also acknowledge the importance of the human element in content. “Man + machine" is the best approach for successful content marketing.
Thinking about teaming up with an awesome content marketing and SEO machine?
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