What it means
Vectorization is the step where a search engine or LLM turns your text into numbers so it can compare meaning instead of matching exact words. The system breaks content into tokens, converts those tokens into embeddings, and places them as points in a high-dimensional space. Words and phrases that mean the same thing land close together, even when the spelling is completely different. Google moved through this progression over years: as Kevin puts it, it learned to "understand word N-grams first and then embeddings and vectors later."
Why it matters
Vectorization collapses synonyms and paraphrases into the same mathematical neighborhood, which kills exact-match keyword strategy. You no longer win by repeating a keyword 12 times. You win by covering a concept from multiple phrasings inside one chunk, because all of those phrasings resolve to roughly the same vector. This also explains how engines detect near-duplicate pages: 2 pages that say the same thing in different words still point in the same direction.
Picture a fintech blog with 2 pages, "best budgeting app" and "top money management tools." Pre-vectorization, those read as separate keywords. Post-vectorization, an engine measures them at 0.82 cosine similarity, treats them as the same intent, and splits authority between them instead of ranking either one.
How to use this knowledge
Cover the concept, not the string. Within a single section, use the natural variations a reader would (budgeting, expense tracking, money management) so the chunk vectorizes for the full cluster of queries.
Audit for accidental overlap. Run cosine similarity across your own pages; anything above 0.7 is competing with itself and should be consolidated.
Stop counting exact-match keywords. Brief writers around intent and entities, not a target density that vectorization has made irrelevant.
Write declaratively. Clear "X is Y" sentences create cleaner vectors than vague, hedged phrasing.
Growth Memo guidance
Google might just now get better at really understanding content quality. Besides being able to understand word N-grams first and then embeddings and vectors later, it seems Google was never able to understand what good content is without user signals.
The best way to measure similarity between two or more pages is cosine similarity between their tokenized embeddings. Basically, you compare how similar two pages are by turning their text into numbers and seeing how closely those numbers point in the same direction.
Vector (embedding) — the numerical output of vectorization that represents one chunk of text.
Cosine similarity — the measure of how closely two vectors point in the same direction.
Keyword cannibalization — what happens when two of your pages vectorize too closely and compete.
Semantic search — retrieval based on vectorized meaning rather than literal keyword matching.
Chunk — the unit of text that gets vectorized and retrieved independently.