SEO

Information Gain

Q: What is information gain in SEO?

Information Gain is a Google concept from a patent of 2022: how much new, unique information does a piece of content offer compared with sites that already rank? Content with high information gain is ranked preferentially.

Q: How do you generate information gain?

Through an original perspective and new facts. Your own studies, your own data, first-hand experience, different framings of a topic. Another ranking article with content identical to the top 10 has information gain close to zero.

Q: Why is information gain particularly important for GEO?

Because LLMs are optimised to recognise redundancy. If 30 sites say the same thing about a topic, one of them will be cited. Anyone who supplies their own facts, their own analyses or a new argument stands out and is cited more often.

Q: How do you measure information gain?

Through embedding comparison with top rankers. If your own content vector lies very close to existing rankers, information gain is low. A wide distance means high novelty. Rankmio uses pgvector for semantic comparisons in the Content Studio.

Also known as: Information gain score, New information patent

Information Gain is a concept from a Google patent granted in 2018 (US 9,953,027) that describes how search engines could evaluate documents according to their contribution of new information. The basic idea: a document that essentially reproduces the same information as already indexed sources has low information gain. A document with substantially new information, new data or new perspectives has high information gain. Although Google has never confirmed that the concept runs productively in the algorithm, much in the behaviour of the Helpful Content System suggests it does.

What information gain means in practice

Own data and research — studies, tests and statistics that nobody has published yet.
First-hand experience — your own test reviews with photographs, your own usage reports.
New perspectives — a familiar topic seen from an unusual angle.
Data aggregation — existing data brought together in a new, useful form.
Expert insights — knowledge that only someone with long industry experience can contribute.
Updates and revisions — new developments that are missing from older sources.

What low information gain means

Pure paraphrases of existing top 10 content without any contribution of your own.
AI-generated texts without human editorialisation — by definition, the LLM can only repeat what already exists somewhere.
Generic instructions without context-specific detail.
Listicle copies of the same top 10 rundowns from other magazines.
Affiliate reviews without your own tests.

Information gain and GEO

Indirectly very relevant for GEO: LLMs in the RAG phase favour sources with high information gain — a source that offers unique data is more valuable than three sources that all say the same thing. A domain with consistently high information gain becomes, over time, the ”anchor source" for a topic area — including in AI answers. Strategic tip: publish at least one information-gain-rich anchor article per topic cluster, with your own data or studies.

Example from practice

Example: instead of yet another ”GEO strategy 2026" article, a marketing magazine published its own quarterly study covering 1,200 German B2B domains, in which citation rate was actually measured across 5 LLM engines. Original data, effort that cannot be replicated. Effect after 4 months: the study is cited as a source in 38 further magazines, backlinks from DR 50+ sources arise organically, and the citation rate in ChatGPT on the topic stands at 67%. High information gain as a sustainable lever for brand authority.

Frequently asked questions

What is information gain in SEO?

Information Gain is a Google concept from a patent of 2022: how much new, unique information does a piece of content offer compared with sites that already rank? Content with high information gain is ranked preferentially.

How do you generate information gain?

Through an original perspective and new facts. Your own studies, your own data, first-hand experience, different framings of a topic. Another ranking article with content identical to the top 10 has information gain close to zero.

Why is information gain particularly important for GEO?

Because LLMs are optimised to recognise redundancy. If 30 sites say the same thing about a topic, one of them will be cited. Anyone who supplies their own facts, their own analyses or a new argument stands out and is cited more often.

How do you measure information gain?

Through embedding comparison with top rankers. If your own content vector lies very close to existing rankers, information gain is low. A wide distance means high novelty. Rankmio uses pgvector for semantic comparisons in the Content Studio.

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Last updated: 2026-06-17 · Browse all glossary entries