Embeddings
Data & TrackingAlso: Vector Embeddings · Text Embeddings · Semantic Embeddings
Quick definition
Embeddings are numerical representations of words, sentences or documents that capture meaning and relationships. An AI model converts text into a list of numbers called a vector. Similar meanings produce similar vectors, which lets machines find related content without relying on exact keyword matches.
How it varies across Australia
Adoption of embedding-based search among Australian businesses is concentrated in technology and enterprise categories. Most mid-market Australian businesses still rely on keyword-based site search and retrieval, leaving meaningful relevance improvements on the table. The gap between early adopters and the average is widening.
See data and tracking maturity across Australian industries →The three things embeddings make possible
Finding content by meaning rather than exact words. A search for 'running shoes for sore knees' finds 'footwear for joint pain' without keyword overlap.
Embedding your own content so an AI model can pull relevant pieces into its answer. The foundation of most enterprise AI chatbots.
Comparing the vector of what a user just read or bought against vectors of other content to find what to show next.
What it actually means
The best way to understand embeddings is to think about a map. On a regular map, things that are geographically close are plotted close together. Embeddings do the same thing for meaning. Words, sentences or documents that mean similar things get plotted near each other in a very large mathematical space. The distance between two points on that map is a measure of how semantically related they are.
In practical terms, an embedding model takes a piece of text and converts it into a list of hundreds or thousands of numbers, called a vector. The model has learned, from training on enormous amounts of text, that 'mortgage broker' and 'home loan advisor' should produce similar vectors, even though they share no words. This is the core capability that makes modern AI search feel smarter than the keyword search that came before it.
For marketers, embeddings matter in three overlapping ways. First, they are what powers semantic search in AI-first search engines and AI overviews, which means content strategy built only around exact-match keywords is increasingly fragile. Second, they are the foundation of retrieval-augmented generation (RAG), the technique most enterprise AI chatbots use to pull relevant company knowledge into an AI answer. Third, they underpin recommendation engines and personalisation layers that predict what a user wants next.
The attribution and analytics implications are real. As more traffic comes through AI-assisted search, the query a user typed may look nothing like the content they landed on. Understanding why a piece of content surfaces in AI-generated answers requires thinking about semantic proximity, not just keyword density.
Keywords match strings. Embeddings match meaning. That difference is the entire story of how AI search is different from what came before.
How it shows up
Embeddings show up in marketing infrastructure in a few specific places. In site search, an embedding-powered engine returns results based on intent rather than keyword match, which usually lifts click-through rate and reduces zero-result searches. In email and content personalisation, embeddings let you match a user's reading history to articles they haven't seen yet. In AI chatbots on your website or in your CRM, embeddings let the model retrieve relevant chunks of your product documentation, pricing pages or FAQs before generating an answer.
The indirect effect on content marketing and SEO is harder to see but just as important. Google's AI Overviews use embedding-based retrieval to select which content to surface. A page that comprehensively covers a topic from multiple angles will tend to produce a vector that matches more query types than a page optimised narrowly for one phrase.
The Australian context
Australian businesses building RAG systems on their own content often underestimate the quality bar for the source material. Embedding a knowledge base full of outdated PDFs, inconsistent product descriptions and legacy FAQ pages produces a retrieval system that confidently returns wrong answers. The quality of what you embed determines the quality of what the AI retrieves. The Australian regulatory context adds a layer: privacy obligations under the Privacy Act mean that what you embed, where you store the vectors, and who can query them all carry compliance implications. This is particularly acute for businesses in finance, health and legal, where embedding client or patient information into a shared vector store requires careful governance.
Where people get this wrong
Related terms
Common questions
Do I need to understand embeddings to do SEO?
You don't need to know the maths, but you need to understand the implication: search engines increasingly rank on meaning, not keyword frequency. Writing comprehensive, natural content that covers a topic from multiple angles is the practical version of optimising for embeddings. The technique is the same. The mental model is different.
What is a vector database and why does it matter for marketers?
A vector database stores embeddings and lets you query them by similarity. It's the infrastructure layer that makes RAG chatbots, semantic search and recommendation engines work at scale. Marketers rarely build vector databases directly, but they increasingly work with vendors whose products run on them.
How are embeddings different from keywords?
Keywords match exact strings of characters. Embeddings match meaning. A keyword search for 'cheap flights Sydney' only matches pages with those exact words. An embedding-based search finds pages about 'affordable air travel from New South Wales' even without the exact phrase. The gap between the two is where most semantic search improvements live.
Can embeddings help with email personalisation?
Yes. Embedding the articles or products a subscriber has engaged with, then finding the nearest unread items in your content library, is how modern recommendation engines suggest 'you might also like' content. The same approach works for product recommendations in ecommerce email flows.
Keep exploring
About New Rebellion
New Rebellion is a marketing intelligence consultancy. We build tools, score Australian businesses on how their marketing actually performs, and publish Debrief every day. This dictionary is part of how we work in the open.
How we think →