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Information Gain

Information Gain refers to the amount of genuinely new information a piece of content adds relative to existing documents on the same topic. The concept originates from a Google patent and describes how much original value content delivers beyond what a user has already seen.

  • Information Gain measures the amount of new information a piece of content adds compared with existing documents on the same topic, a concept that originates from Google's "Contextual Estimation Of Link Information Gain" patent (filed in 2018, granted in June 2024).
  • The core ideas are avoiding redundancy and prioritizing originality: content that simply restates existing search results carries less value than content offering differentiated insight and first-party data.
  • That said, Information Gain is a concept described in a patent rather than a confirmed direct ranking factor at Google, so it is safest to treat it as guidance for building 10x content and strengthening originality.

Overview

Information Gain refers to the amount of new information a single piece of content provides beyond the existing documents that already cover the same topic. The term originates from Google's patent "Contextual Estimation Of Link Information Gain," which was filed in 2018 and granted in June 2024.

As the patent describes it, an information gain score measures "additional information that is included in a given document beyond the information contained in documents the user has already viewed." After a user sees the results for an initial query, the system identifies a second set of relevant documents the user has not yet viewed and scores and ranks them according to how much new information each one contains relative to what has already been seen.

Avoiding Redundancy and Prioritizing Originality

The essence of Information Gain lies in reducing the "sameness" of content. A page that copies another document outright, or merely rephrases it, adds almost no new information. When a searcher has already encountered the same material across several pages, the value of yet another page listing identical information drops sharply. The Information Gain lens asks not "how comprehensively did you cover the topic" but "what did you add that wasn't there before."

Content Implications: Differentiated Insight and First-Party Data

The surest way to raise Information Gain is to include information that cannot be found elsewhere. Proprietary survey data, real user reviews, original experiment results, primary interviews, and analysis from a fresh angle are all examples of first-party data you produce directly, and each creates originality on its own. If you become the sole source of a particular statistic or insight, your differentiation on that data point is absolute. This is also precisely the territory that generative AI, which excels at quickly synthesizing widely agreed-upon information, struggles to replicate.

A Caution Against Overinterpretation

Information Gain is, above all, a concept described in a patent, not a direct ranking factor that Google has officially confirmed. The existence of a patent does not mean the technology is applied to search rankings exactly as written. The phrase itself is also used differently across contexts such as machine learning, the Google patent, and information foraging theory, so its meaning varies from person to person. It is worth noting in particular that this patent focuses on ranking a "second set of results" anticipating follow-up questions, rather than the initial search ranking, and that it references automated assistants and chatbots far more than traditional search. For these reasons, Information Gain is best taken not as a confirmed signal but as a direction for building 10x content and strengthening originality.

Execution Checklist

  • Read the top-ranking documents for your target topic first, and distinguish between what is already well covered and what is missing.
  • Fill at least one gap competing documents fail to address, such as an unanswered question or a missing perspective.
  • Include at least one source of first-party data, such as your own survey, experiment, usage data, or interview.
  • Review paragraphs that simply restate existing information and replace them with new analysis or examples.
  • Cut redundant repetition added purely to increase length, and structure the content so the added information stands out clearly.
  • Cite the data and sources you reference to ensure credibility and verifiability.

References and Sources

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