Information Gain

What is information gain?

Quick definition: Information Gain is a measure of how much unique, non-redundant information a piece of content contributes to a topic, relative to the other documents already ranking or already established as references for that subject.

The term traces back to a Google-published scoring method for evaluating documents partly on what they add beyond existing search results, not only on how well they match a query. The underlying idea is simple even where the scoring method is not: content that repeats the consensus view contributes little, while content that adds a fact, an angle, or a data point nobody else has published contributes more.

For B2B content, information gain is the difference between an article that restates five competitors’ talking points in a sixth voice, and one that includes something none of those five said.

Why information gain matters

Search engines and AI systems both have practical reasons to reward information gain. A search engine benefits from surfacing a range of genuinely useful results rather than ten versions of the same summary. A generative AI system synthesizing an answer from multiple sources benefits even more directly: a source that says something the others do not is more useful to cite, and more likely to get cited, than one that only confirms what the others already said.

This creates a real incentive problem for content that is well-produced but adds nothing. A polished, accurate, thoroughly researched article that says only what the top five existing articles already say has low information gain, whatever its production quality. That is a close cousin of what MSMC calls craptent, content that is competent but adds no value, and it is exactly the kind of content generative AI systems have the least reason to prefer.

How information gain works

Information gain is not a single toggle a writer can switch on. It comes from specific, identifiable additions to a piece of content:

• Proprietary data, a first-party number, result, or observation nobody else has published
• A first-hand account or direct experience that cannot be reproduced by summarizing other sources
• A falsifiable, specific claim, one precise enough that it could turn out to be wrong, rather than a vague generalization that cannot be tested
• A correction of a widely repeated but inaccurate or outdated claim already circulating on the topic
• A named example, case, or detail that competing content leaves out entirely

The opposite of information gain is consensus restatement: content whose claims are already fully covered, in substance, by the sources a reader or an AI system could already find.

Information gain vs. topical coverage

The two concepts are often confused, but they measure different things.

Topical coverage

Topical coverage asks whether a piece of content addresses the subtopics, questions, and angles a reader would expect on a given subject. It rewards completeness.

Information gain

Information gain asks whether the content adds anything beyond what is already available elsewhere on those same subtopics. It rewards differentiation, not just coverage.

Why both matter

Content can have strong topical coverage and near-zero information gain at the same time, if every subtopic it covers is already covered, in the same way, by other sources. Comprehensive and original are not the same property, and strong content needs both.

Business benefits of information gain

• Better odds of being the source an AI system chooses to cite, rather than one of several interchangeable restatements
• Stronger differentiation from competitors publishing similar, keyword-matched content
• Content that holds up over time, since a genuinely new data point or observation does not go stale the way a repackaged summary does
• A more defensible claim to thought leadership, since restated consensus does not build authority on its own

How MSMC approaches information gain

MSMC evaluates information gain directly through its Thought Leadership Originality Audit, which scores draft or published claims on novelty, falsifiability, evidence density, consensus divergence, defensibility, and ownability, rather than assuming that thorough or well-written content is automatically original. The same discipline underlies the Craptent Test, a self-administered check for whether content clears the bar of adding real value rather than simply existing.

If your company needs help improving how its content performs in search, answer engines, and AI-led discovery, contact MSMC.

FAQ

Key takeaways

• Information Gain measures how much unique information content adds relative to existing sources on the same topic.
• It differs from topical coverage: content can be comprehensive and still add nothing new.
• Low information gain is a close cousin of craptent, competent content that adds no real value.
• Proprietary data, first-hand observation, and falsifiable specific claims are the main levers for increasing it.
• MSMC scores information gain through its Thought Leadership Originality Audit.