TF-IDF (WDF*IDF).

TF-IDF consists of two parts. Term frequency (TF) counts how often a term appears in a document. Inverse document frequency (IDF) weights this value according to how rare the term is in the whole collection. A word such as 'and' appears everywhere and therefore receives hardly any weight. A word such as 'handover guarantee' is rare and is weighted heavily if it appears frequently in a document. The product of the two values shows which terms particularly characterise a document.

The method dates from the 1970s and was long a foundation of search systems. In German-speaking SEO, the variant WDF*IDF became known around 2012. WDF (within-document frequency) dampens raw frequency logarithmically so that long texts and repetitions do not count excessively. SEO tools calculate WDF*IDF for the top-ranking pages for a search term and show which terms typically appear there and are missing from your own text.

The benefit lies in topic coverage. A text about end-of-tenancy cleaning that lacks terms such as 'handover', 'property management', 'oven' or 'windows' probably covers the topic incompletely. A WDF*IDF analysis makes such gaps visible. However, Google has never confirmed using TF-IDF as a ranking factor, and modern search systems work with language models and vector embeddings, which capture meaning far better than word counts.

Today WDF*IDF is therefore a research aid, not an optimisation target. Rewriting texts until a tool's curves match produces easily interchangeable content that says the same as everyone else. It makes more sense to use the analysis as a checklist and then fill the text with your own knowledge, examples and experience.