Information Gain – Lesson Preview
This lesson dives into one of the most fascinating intersections between machine learning and modern SEO, the concept of information gain. You’ll learn how Google uses this score to evaluate how much new or unique information a page provides compared to what users have already seen, a critical signal for ranking and de-duplication in search results.
By unpacking real Google patents and examples, the lesson connects theory with SEO strategy. You’ll see how information gain is calculated using semantic models, co-occurrence analysis, and neural networks and how these same ideas can guide you to create more distinctive, valuable content.
Marketers and SEOs will walk away with a deeper understanding of why pages with higher information gain scores tend to rank better, and how to incorporate this thinking into keyword research, SERP analysis, and content planning to stand out in saturated topics.
What you’ll learn (why it matters)
- Understand information gain because it’s central to how Google values unique information.
- Decode Google’s patents and logic because knowing how the system works helps you align content.
- Apply semantic modeling concepts because it connects keyword research to meaning, not volume.
- Spot and fill information gaps because users and algorithms reward novelty.
- Evolve content strategy because programmatic and user-focused SEO depend on informational uniqueness.
Key concepts (with mini-definitions)
- Information Gain — a score measuring how much new useful information a document provides beyond what users already know.
- Decision Trees — machine learning models that calculate information gain to identify the most informative attributes.
- Semantic Feature Vector / Embedding — numerical representation of a document’s meaning used for scoring.
- Co-occurrence Rate — The frequency with which phrases appear together; used to assess semantic relatedness.
- Phrase-Based Indexing — A Google method for ranking and de-duplicating documents based on related phrases.
- Knowledge Graph Expansion — Process of growing Google’s entity collections using information gain to identify new relationships.
- Entity-Attribute-Value (EAV) Model — Framework for analyzing entities and their attributes to structure keyword research.
Tools mentioned
Word2Vec, ClearScope, Google patents and neural networks
Practice & readings
- Suggested exercise: Identify one of your top pages and analyze competing content to find information gaps. What new insights could you add?
- Recommended reading: Bill Slawski’s analyses of Google patents on information gain.
- Optional resource: ClearScope and Bernard Huang’s work on applying information gain in topical authority.
Key insights & takeaways
- Google prioritizes novelty and user value over keyword repetition.
- Information gain connects SEO with semantic understanding, not metrics.
- High-scoring pages combine unique knowledge and clear context.
- Marketers must balance user familiarity with informational innovation.
- Focusing on new, proprietary insights builds long-term ranking resilience.
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