from keywords to concepts: the semantic strategy I teach

Here's why you should adapt to semantically understanding queries and cross-platform search behaviour

Hi 👋🏻

'Keyword' research is dead - search has shifted from matching words to understanding meaning. If your strategy still starts with a list of terms, you’re already behind. It's long past time to evolve to a more sophisticated, user-centric approach focused on semantic understanding.

What’s changed:

• AI answers > blue links. Engines and assistants interpret entities, context, and prior sessions—then serve answers, not just results.
• Journeys are cross-platform. Google → YouTube → TikTok → Reddit → AI assistants. You need to map those query paths and meet intent at each step.
• Originality gets rewarded. Content that adds new information (not summaries) wins—this is information gain in practice.

What to do instead:​
Think in topics, entities, and user context—not just keywords. Build a semantic universe, then turn it into briefs that maximize information gain and visibility in AI search.

Semantic SEO: the micro-glossary that actually helps

Short, practical definitions for the concepts you need to suceed:

Entities–Attributes–Values (EAV)​
​What it is: Real-world things (entity), their properties (attributes), and specific details (values).
​Why it matters: Aligns content with how search + AI “think” and how customers compare.
​Do this: List your top 10 products/services → add 5 buyer-relevant attributes each → turn each [entity × attribute] into FAQ, comparison, and spec content.

Information Gain​
​What it is: How much new and useful info your page adds beyond what’s already out there.
​Why it matters: AI and search elevate pages that close gaps, not summaries.
​Do this: Audit the top results for your topic → highlight what’s missing (data, methods, examples, constraints) → add those sections first.

Knowledge Graphs​
​What it is: Networks of entities and their relationships.
​Why it matters: Powers query understanding, disambiguation, and answer snippets.
​Do this: Cluster keywords by entity (not just term similarity). Build a pillar for the entity; spokes for core attributes and common comparisons.

Search Intent (macro → micro)​
​What it is: From broad types (informational, transactional, etc.) down to micro-intents (price sensitivity, compatibility, brand vs. generic).
​Why it matters: Format + CTA should match intent stage.
​Do this: Tag each query with stage + micro-intent and pair a content format (e.g., “vs” pages, calculators, checklists, demos).

Query Sequences & Paths​
​What it is: The chain of searches across sessions and platforms until the goal is met.
​Why it matters: Reveals where to intercept and what to publish next.
​Do this: Map 3–5 common sequences (e.g., “best mirrorless → 4k vlog camera → Sony ZV-E10 low-light”) and create interlinked content for each step.

Synthetic (subtype: Entity-Related) Queries​
​What it is: AI-generated expansions that swap or refine entities/attributes to reflect how users actually search.
​Why it matters: Covers the long-tail that AI assistants surface as “related questions.”
​Do this: For each entity, systematically generate variants (brand, model, size, use-case, budget) and keep only those with clear buyer value.

Query Augmentation​
​What it is: Adding attributes/constraints that make a query more precise (engine-side or user-side).
​Why it matters: Be the page that already answers the augmented version.
​Do this: Pre-answer filters in your content: price ranges, compatibility matrices, sizing guides, “works with …” tables.

Context & Session Signals​
​What it is: Location, device, time, history, and active task.
​Why it matters: Changes the “right” answer and formatting.
​Do this: Offer quick-scan modules (sticky TL;DR, comparison table) for mobile/session speed; deeper sections for desktop research.

User Search Behavior​
​What it is: How people interact with results and your page (scroll depth, pogo-sticking, click paths).
​Why it matters: Signals whether you satisfied intent.
​Do this: Front-load the answer, show the proof, then expand. Kill fluff above the fold.

If all of this has you intrigued, then it's time to learn Semantic SEO Keyword Research.

Featured course 🌟

I wanted to put a spotlight on this Semantic AI-powered SEO Keyword Research Course, as it is becoming ever-more relevant with recent industry changes.

It's a practical system to:

• Map entities, attributes, and relationships for your niche
• Build a semantic keyword universe and cluster it logically
• Generate high-signal content briefs (tool + templates included)
• Optimize for AI/LLM answer inclusion and information gain
• Plan cross-platform query paths that convert

10+ hours of content; 20+ tools, checklists, scripts, and other resources. 🔥

New & upcoming:

• Fresh lesson + tool for turning your term universe into content briefs
• Upcoming tool for automated topical maps from your keyword universe
• Two new modules coming: on Synthetic Queries (by me) and AI Search (by Beatrice Gamba, Head of Innovation at WordLift) 🤩

START LEARNING TODAY ✨

Here's just a sneak peak of what one of our students - Sarah, had to say about this course:

Community perk: ✨ Use code COMMUNITY30 for 30% off at checkout.

Heads-up 💸: price increases when the new modules drop—lock in now.

60+ forward-thinking marketers are already taking our courses 💜

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Join over 600 AI/ML-interested marketers on our Slack community (if you haven't already) to stay up to date with discussions on AI/ML automation in SEO and marketing.

Here's some of the recent discussions there:

• ​Emina looking for Docker setup support ​
• ​Nikhil shared a tool for Programmatic SEO Content Idea generation
• ​What causes AI Search Traffic Volatility? - Discussion and Resources​
• ​Aimee demystifies Agentic, Agents and MCPs ​

Or check out a small sample of the resources shared:

P.S. I’m back from maternity leave 🐣 and shipping fast. Thanks for sticking with me through my absence—big updates will be rolling out on the MLforSEO platform over the next few weeks.

Happy learning! ✨

Lazarina