Read the full article: Embeddings for Marketers: Mapping Topic Space and Finding Gaps

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Excerpt:

Introduction

Modern content marketing is about more than just choosing the right keywords. Marketers are using embeddings – numerical vector representations of text – to map the meaning of all their articles and topics. In simple terms, an embedding turns each sentence or document into a list of numbers that machines can compare. This lets us “see” which articles are similar in topic or intent, even if they don’t use the same words. For example, in today’s search landscape, Google’s AI systems (like MUM and Gemini) use embeddings to understand the context and intent behind queries (www.ranktracker.com). By leveraging embeddings, marketers can plot their content in a “topic space” and spot clusters of related ideas. This approach reveals how well a content library covers different themes – and where the blind spots are.

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