Let’s now move on to the final text optimization, the redaction of the rewritten and keyword-enriched texts consisting of clean semantics, paragraphs, and chronological headings.
WDF * IDF + Keyword Density = The Final Text Optimization
WDF*IDF is a formula that can be used to determine the optimal distribution of topic-relevant keywords within a text document or a website. This formula is used, among other things, in OnPage optimization, usually to be able to present topics particularly comprehensively.
In addition to considering the Keyword Density (German: Keyworddichte), the relevance of a website can be increased.
Keyword Density refers to the frequency with which a search term (keyword) appears in a single document. Keyword Density is a percentage value that can be calculated from the absolute number of occurrences of the term and the total number of terms contained in the document.
To avoid unnaturally sounding and over-optimized texts, it is a general rule of thumb not to exceed a Keyword Density of 1.5%.
These methods examine the semantics of our content by, for example, comparing it with keywords from other texts. The goal of this final text optimization is to increase the relevance of our content for individual search terms. We will not go into the technical background at this point (see formula on Wikipedia).
WDF*IDF is just a crutch for content relevance. In 2012, there was a real hype about it in the SEO world when Karl Kratz „rediscovered“ this formula. Simply put, this formula calculates how your text relates to other texts on the same topic or keyword, and which other keywords should appear in what proportion in this thematic context. Sounds complicated? It is. Fortunately, there are tools like termlabs.io or RYTE’s Content Success:
Texts about dog biscuits seem to mainly contain ingredients and recipes. Often, these biscuits seem to consist of oats – at least, this screenshot from RYTE suggests that.
WDF * IDF analysis can help you find important terms for your SEO text. This way, you can filter out keywords that appear relatively frequently in a text about „dog biscuits“ but are not found in every text on the web. „And“, for example, often appears in texts about dog biscuits, but also in all other texts. In contrast, words like „ingredients“, „recipes“, „oatmeal“, and „treats“ are more likely to appear only in a text that deals with dog biscuits.
What this means for you: WDF * IDF tools are great for checking whether you have considered all keywords and interests related to your search query. However, I don’t start counting keywords in the text. So I use the tool more for research than for writing.
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