Your SEO needs machine learning to stay competitive
Search engines like Google now rely on AI and machine learning in their ranking algorithms that go far beyond exact keyword matching. Instead, they aim to understand the intent behind a query – what the user is really looking for. Today, effective SEO isn’t just about targeting the right words, but understanding how people think, search, and interact with content.
Traditional SEO tools often treat keywords and URLs as isolated elements. But in reality, two queries that look similar – even if they share the same words – can reflect very different user needs and lead to completely different sets of URLs. That means you risk optimizing for the wrong terms, competing in the wrong space, or missing the actual opportunities altogether.
Instead of relying on exact matches, modern SEO now models keyword and page relationships through machine-learned vector embeddings that reflect meaning and real user behavior. These embeddings help reveal how users truly think and navigate, far beyond what surface-level keyword practices can capture.
How vector embeddings reshape SEO
Clickstream-trained vector embeddings translate actual user journeys, capturing not just the words that people search, but how they move through content the actions they take.
At Datos, we build these embeddings using behavioral signals from a proprietary panel of tens of millions of daily active users. Using machine learning, embeddings map the semantic relationships between search queries, URLs, and user journeys, revealing patterns that traditional tools simply miss.
For search platforms, content strategists, and SEO agencies, this means moving from guesswork to behavior-driven optimization – creating content and targeting strategies that truly align with how users search and what they expect to find.
Here’s how embeddings, especially when powered by clickstream data, are reshaping how modern SEO works.
What are embeddings?
Embeddings are numeric vector representations of text (in this case, search queries or URLs) that capture semantic relationships between terms. For example, “best running shoes” and “top sneakers for jogging” might live close together in vector space even though they share no exact words – as would the URLs that typically map to those queries.
But unlike static word embeddings, we train our models on behavioral context: sequences of queries and clicked URLs. This lets us capture not just semantics, but also navigation patterns, and intent flow. It means a product page that frequently receives traffic from “best laptops for gaming” will live near other URLs serving similar intent.
Want a deeper look at how we create embeddings? Head over to our blog.
Real SEO use cases for vector embeddings and semantic search
Smarter Keyword Research
Instead of only finding exact keywords that competitors rank for, embeddings let you:
- Discover semantically related terms you’re missing even if they’re phrased differently
- Spot opportunity clusters: not just keywords, but topics you haven’t covered
- Prioritize based on actual traffic patterns and semantic similarity
Example: Your competitor ranks for “eco-friendly baby wipes” – vector embeddings might show you’re also missing “natural wipes for newborns” and “biodegradable diapers” as part of the same cluster.
With this insight, you can create content that matches what users actually want, leading to better alignment with intent and stronger SEO performance.
Find Hidden Competitors
Your real competitors may not be who you expect. Vector embeddings can reveal:
- Domains that serve similar search intents
- Pages that are receiving traffic in the same semantic territory
- Niche players stealing long-tail traffic from you
Example: A Reddit thread or Quora answer might appear next to your content in search. Even though they’re not direct business competitors, they’re fighting for the same attention.
This helps you identify where you’re losing visibility, which content gaps to fill, and how to position your content more effectively. You can also analyze these competitors’ content strategies to learn what’s resonating with your shared audience, and use that to guide your own topics, format choices, and distribution tactics.
Search Intent Clustering
Embeddings can group keywords by user intent, not just by matching words. This helps you understand:
- What users are really trying to do – learn, compare, or buy, etc.
- Which keywords belong to the same topic cluster, even if phrased differently
With a clear view of intent and topic structure, you can tailor your content to match where users are in their journey. That means creating the right type of page – educational guides for informational queries, comparison pages for evaluation intent, and product pages for transactional searches.
Content Optimization and Grouping
Vectors reveal semantically related topics that should be addressed within the same page to better meet user expectations.
Example: If you’re writing about electric vehicles, vector embeddings might surface related terms like
- battery range
- charging speed
- EV tax incentives
- range anxiety
because those concepts are close in vector space. Including these helps create more comprehensive, user-aligned content that meets real search intent.
Embeddings could also reveal how users conceptually group topics, helping you understand which keywords belong to the same cluster and which don’t. This allows you to structure your content more clearly, creating separate pages for distinct topics rather than combining unrelated ideas. When your content structure aligns with how users search, it’s easier for Google to interpret your pages and rank them appropriately.
Example: If embeddings show that charging infrastructure and green energy belong to different clusters, you’ll know not to combine them into a single page about electric vehicles. Keeping them separate helps avoid confusing search engines and improves clarity for both indexing and ranking.
The new SEO is already here – start with vector embeddings
AI-driven search isn’t a future concept, it’s happening now and shaping how users discover and engage with content. Search engines are no longer just matching keywords; they’re interpreting intent, context, and behavior. To stay competitive, SEO strategies need to evolve in the same direction.
Using embeddings based on clickstream data gives you a real advantage: you’re not guessing what users want, you’re seeing it in their actual journeys. By understanding how people search, what they expect to find, and how topics connect in their minds, you can build content that ranks better, converts more, and genuinely serves your audience.
Whether you’re just starting to explore semantic search with vector embeddings, or looking to go beyond traditional SEO keyword tools, you don’t need to build everything from scratch. We’ve already done the heavy lifting, training embeddings on real behavioral signals from millions of users so you can start using them right away. With Datos, you get access to intent-rich, ready-to-use vector embeddings that help you create content and strategies aligned with how people actually search today.
At Datos, we offer embeddings built on behavioral signals from a global panel of tens of millions of users, giving you access to real, intent-rich insights you can act on. If you’d like to explore how this can power your SEO strategy, get in touch.
