Guide : Information Gain

TL;DR — Information gain is how beaucoup nouveau information votre page adds que a searcher hasn’t déjà seen in the autre results pour que topic. It’s pas the même as “good content” or “long content” — une page peut be thorough and encore ajouter nothing nouveau. The nom comes from a Google patent, but Google has jamais said it en réalité uses it to rank pages. Ce que it fait dire, in plain guidance, is: don’t simplement rewrite what’s déjà out là — ajouter something original.

Ce que information gain signifie

Information gain is an information-theory and patent concept souvent utilisé by SEOs; Google ne fait pas document a public ranking factor with a universal score sous ce nom. Evidence for this claim Official or primary documentation supporting the adjacent article claim, with scope limited to the source's published description. Scope: No ranking guarantee or undisclosed system mechanics are inferred beyond the cited source. Confidence: high · Verified: 2026-07-14 Google patent: Contextual estimation of link information gain Google’s helpful-content guidance fait demander si content adds original information, reporting, research, or analysis. Evidence for this claim Official or primary documentation supporting the adjacent article claim, with scope limited to the source's published description. Scope: No ranking guarantee or undisclosed system mechanics are inferred beyond the cited source. Confidence: high · Verified: 2026-07-14 Google: Helpful content guidance

Picture someone researching a topic. Ils ouvrir the premier result, lire it, alors ouvrir the second. Si the second page repeats everything the premier un said — simplement worded differently — ils learned nothing nouveau from it. Si it donne les a fresh données point, a first-hand story, or a différent angle, ils gained information.

That’s the whole idea. Information gain is à propos de novelty relative to ce que the searcher has déjà seen, pas à propos de how polished or complet a unique page is on its propre. A 4 000-word article que carefully restates the même consensus facts as the top ten results has low information gain. A short post with un genuinely nouveau number peut have élevé information gain.

Où the term comes from

Google was granted a patent (filed in 2018, granted in 2022) appelé “Contextual estimation of lien information gain.” It describes scoring a document pour how beaucoup additional information it contient au-delà documents a utilisateur has déjà seen — and en utilisant que to aider decide ce que to rank or montrer suivant.

Two choses to garder straight:

1. A patent n’est pas confirmation. Google fichiers thousands of patents. Having un proves Google pourrait do something, pas que it fait. Google has jamais confirmed it uses ce in live search ranking.
2. It’s pas a synonym pour “quality.” The patent is narrowly à propos de newness, pas general goodness.

Pourquoi c’est important plus now

Résultats de recherche are increasingly answered by AI — Google’s AI Overviews, AI Mode, ChatGPT, and others. Ces systems are very bon at summarizing what’s déjà been publié everywhere. So si votre page simplement dit ce que everyone sinon déjà dit, an AI peut absorb it and réponse the question sans vous.

Le contenu que survives que is le contenu AI can’t simplement paraphrase: original research, votre propre données, first-hand experience, expert quotes aucun un sinon has. That’s information gain En pratique.

Que faire à propos de it

• Avant publishing, demander: ce que fait ce page dire que lune pages déjà ranking don’t? Si the honest réponse is “nothing,” vous haven’t ajouté information gain.
• Ajouter something seulement vous have — a petit survey, a screenshot from a réel tester, a quote from someone on votre team who en réalité fait the fonctionner.
• Arrêter chasing “more” (plus words, plus subtopics, plus FAQs). Chase nouveau.

Vouloir the patent details, the scope caveats, the official Google language que echoes ce, and the practical playbook? Switch to the Avancé tab.

TL;DR — Information gain is novelty relative to the corpus a searcher has déjà seen on a topic — pas comprehensiveness, pas E-E-A-T, pas “quality” in the abstract. It’s named après a granted Google patent (“Contextual estimation of lien information gain,” filed 2018, granted 2022) que scores documents 0,00–1,00 pour how beaucoup nouveau information ils ajouter and ranks partly on que. But: Google has jamais confirmed live utiliser; the patent’s propre framing is à propos de ce que to montrer suivant après a utilisateur has seen some results, pas the premier page; and there’s aucun visible score, aucun API, aucun disclosed formula. Ce que is official is Google’s helpful-content guidance asking pour “original information, reporting, research, or analysis.” It matters plus in an AI-answer SERP parce que LLMs synthesize consensus pour free — seulement genuinely nouveau information forces a citation.

Ce que information gain en réalité is (and isn’t)

The cited patent describes possible méthodes, pas proof que a spécifique production ranking system operates exactly que façon. Evidence for this claim Official or primary documentation supporting the adjacent article claim, with scope limited to the source's published description. Scope: No ranking guarantee or undisclosed system mechanics are inferred beyond the cited source. Confidence: high · Verified: 2026-07-14 Google patent: Contextual estimation of link information gain Utiliser original-value guidance as an editorial principle, pas a measurable ranking guarantee. Evidence for this claim Official or primary documentation supporting the adjacent article claim, with scope limited to the source's published description. Scope: No ranking guarantee or undisclosed system mechanics are inferred beyond the cited source. Confidence: high · Verified: 2026-07-14 Google: Helpful content guidance

La plupart articles on ce topic quietly conflate three différent choses. Garder les separate:

• Information gain — the marginal nouveau information a document adds relative to ce que the searcher has déjà seen on the topic. It’s a delta.

• Comprehensiveness / topical depth — covering everything à propos de a topic. Une page peut be maximally comprehensive and have zero information gain si every fact in it déjà exists on lune pages ranking ci-dessus it.

• E-E-A-T / content quality — trust, expertise, experience, authority. Connexe, but a différent judgment. Vous pouvez be an unimpeachable expert and encore publish a page que adds nothing nouveau.

• Truth and accuracy — being novel isn’t the même as being correct. Une page peut ajouter a genuinely nouveau claim and encore obtenir it incorrect. Novelty, accuracy, and trust are separate judgments; treat originality as un input to publish, pas a substitute pour verifying it.

Si vous prendre un chose from ce page: information gain is specifically à propos de the delta, pas the depth and pas the trust. Que distinction is the chose nearly every competing explainer blurs.

Un plus distinction worth naming, parce que SEO borrowed the term loosely: “information gain” aussi has an older, plus technical meaning outside search. In information theory it traces back to Claude Shannon’s 1948 fonctionner on entropy and information measures — a mathematical account of how beaucoup a signal reduces uncertainty, unrelated to search rankings. In information retrieval research, a parallel idea montre up as novelty and diversity: méthodes comme Maximal Marginal Relevance (Carbonell and Goldstein, 1998) rerank or select results to reduce redundancy contre ce que a reader has déjà seen. Google’s patent draws on the même underlying intuition — nouveau relative to what’s déjà been affiché — but none of ces are interchangeable. The SEO industry’s “information gain” is shorthand construit on a spécifique patent, pas a direct application of Shannon’s math or of publié IR diversity algorithms, même though the instinct ils share (don’t simplement repeat what’s déjà là) rhymes à travers tout three.

The Google patent behind the term

Ce que it dit

The patent is “Contextual estimation of link information gain” (US20200349181A1, filed October 2018, granted June 2022; inventors inclure Victor Carbune and Pedro Gonnet Anders). The core definition, verbatim from the patent:

“An information gain score pour a donné document is indicative of additional information que is inclus in the donné document au-delà information contained in autre documents que were déjà presented to the utilisateur.”

It describes a machine-learning model que takes semantic vectors from documents the utilisateur déjà viewed plus données from a candidate nouveau document, and outputs, in the patent’s words:

“a quantitative score entre 0,00 and 1,00, with 0,00 indicating que aucun information gain is to be attendu”

— with 1,00 meaning the document contient seulement information pas in ce que was déjà seen. Documents may alors be ranked au moins partly on leur respective information gain scores.

Ce que it doesn’t dire

Ce is où la plupart SEO content overreaches, so let me be precise à propos de the limites:

• Aucun confirmed live utiliser. A granted patent is evidence Google pourrait deploy ce, pas que it fait. Google has pas confirmed or denied en utilisant ce mechanism in ranking. Anyone telling vous it’s a confirmed ranking factor is guessing.
• Narrower scope que assumed. The patent’s framing centers on choosing ce que document or lien to surface suivant — think a conversational or assistant follow-up après a utilisateur has déjà viewed some results — pas on the premier page of ten blue liens. Roger Montti’s analysis at Moteur de recherche Journal rend ce scope point bien: the emphasis is on automated assistants, and ces scores aren’t décrit as applying to the premier définir of results (SEJ).
• Aucun public formula. The patent noms “semantic vectors” and salient extracted information, but discloses aucun weights, aucun fonctionnalité liste, and aucun reproducible scoring méthode. Ne faites pas trust quelconque article que hands vous a precise “how Google calculates it” formula — it’s invented.
• It’s a patent family, pas un filing. Google was granted a continuation in the même family — US12013887B2, même title, même assignee (Google LLC), priority date back to the original October 2018 filing. A continuation signifie plus disclosed claim language exists in the family, pas que anything nouveau à propos de deployment has been confirmed — a granted patent and its continuation encore seulement establish ce que Google disclosed it pourrait construire, pas what’s running in production.

Bill Slawski’s early breakdown at Go Fish Digital framed the underlying problem Google is solving — que quand nombreux documents share a topic, ils tend to contain similaire information — and how the patent proposes ranking partly on information gain scores to adresse it (Go Fish Digital).

Google’s official guidance que echoes the idea

Here’s the honest partie la plupart explainers skip: aucun Google document, blog post, or spokesperson uses the phrase “information gain” in public search guidance. The term is industry shorthand. But Google’s réel official guidance — “Creating helpful, reliable, people-first content” — repeatedly describes the même underlying idea in its self-assessment questions. Ces are verbatim from que page:

“Fait le contenu provide original information, reporting, research, or analysis?”

“Si le contenu draws on autre sources, fait it éviter simply copying or rewriting ceux sources, and à la place provide substantial additional valeur and originality?”

“Fait le contenu provide insightful analysis or interesting information que is au-delà the obvious?”

“Fait le contenu provide substantial valeur quand comparé to autre pages in résultats de recherche?”

Que dernier un — substantial valeur comparé to autre pages in résultats de recherche — is à propos de as fermer as Google’s official language obtient to the information-gain concept sans en utilisant the term. Ce is the strongest legitimately-official tie-in disponible, and it’s pourquoi “information gain” caught on as utile shorthand pour a réel principle Google clearly cares à propos de, même though it’s pas Google’s word.

Fait Bing utiliser information gain?

Pas que I’ve trouvé. Aucun Bing or Microsoft document or spokesperson uses the term in the material I’ve reviewed. Bing’s publicly discussed ranking factors — site and author reputation, content completeness, semantic relevance — point in a similaire direction (depth and originality versus peers), but that’s an adjacent idea, pas the même claim and pas the même vocabulary. Si someone quotes Bing en utilisant the literal phrase “information gain,” treat it with suspicion jusqu’à vous voir the principal source.

Pourquoi information gain matters plus in an AI-answer-heavy SERP

Ce is the partie que rend information gain plus que a patent-trivia curiosity.

AI has lire the internet. ChatGPT, Claude, Gemini, and Google’s propre AI Overviews and AI Mode are excellent at un spécifique chose: synthesizing and repackaging ce que déjà exists à travers the web. Nathan Wahl at Animalz rend the argument sharply — ces systems peut déjà reiterate and repackage everything that’s been publié, so the question becomes pourquoi publish anything que isn’t additive. Quand Google synthesizes an réponse, it pulls from multiple sources — Wahl’s framing is que vous don’t besoin to outrank giants si votre content contient information theirs doesn’t (Animalz).

That’s the mechanism. Si votre page restates consensus, an AI Overview absorbs it and réponses sans sending anyone to vous. Si votre page contient something seulement vous have — a proprietary number, a réel experiment, an expert’s first-hand prendre — an AI system has to draw on vous specifically to inclure que fact, plutôt que paraphrasing it from wherever sinon it apparaît. That’s pas a guarantee: retrieval, inclusion in the model’s context, generation, and citation are separate steps, and originality clearing un of les doesn’t mean it clears the rest. Scoped research on generative-engine visibility (Aggarwal et al., “GEO: Generative Engine Optimization”) trouve que ajout sources, statistics, and quotations to content peut améliorer visibility in AI-generated réponses in leur testé setups — evidence que originality helps, pas proof que it guarantees a citation, a ranking, or trafic. Information gain is a precondition pour being cited au lieu de paraphrased, pas a promise of it.

Bernard Huang at Clearscope has tied ce to Google’s Knowledge Graph: aim pour content que covers concepts and entities on the fringe of ce que Google déjà “knows” à propos de a topic, parce que LLMs are great at consensus but genuine novelty is encore où humans ajouter valeur (Clearscope). Amanda King’s early Moteur de recherche Land framing captured the core idea — an information gain score is essentially a mesurer of how unique votre content is versus the rest of the corpus, and content risks being demoted si it lacks uniqueness même quand it’s simplement the même ideas in différent words (Moteur de recherche Land). Andrew Holland plus tard reframed the whole chose pour the AI era: the term signifie différent choses to différent personnes, but the job is to garder increasing the rate of information gain — and increasingly we’re optimizing pour AI, pas simplement pour Google (Moteur de recherche Land).

I’ve seen ce from the autre direction, aussi. I ran some tests trying to rank content in Google’s AI Mode, and le contenu simply didn’t rank — même though it was, as far as I pourrait tell, meilleur and plus relevant que pages que did. Un of the possibilities I floated publicly was a lack of information gain in the articles: si le contenu is a well-written restatement of what’s déjà out là, là may be nothing pour the system to reward (my post on X).

Ce que en réalité produces information gain

Synthesizing à travers the sources — and my propre experience — here’s ce que genuinely moves the needle:

• Original research, surveys, and experiments. The la plupart defensible formulaire. Run a study, publish the données, let others cite vous. At Ahrefs, our best-performing content skews heavily toward données studies pour exactly ce raison.
• Proprietary / first-party données. Internal product or usage données nobody sinon peut reproduce. Si vous have a dataset, que dataset is votre information gain.
• Expert interviews and first-hand experience. Ce is my go-to tactic. Plutôt que shipping unsourced generated text, message a handful of réel subject-matter experts vous déjà have accès to — over Slack, email, or a rapide AI-assisted appel — a few times a week, and capture leur réel stories and experience. That’s original information que didn’t exist on the web avant vous publié it. I applied ce to my propre rebuild of Ahrefs’ SEO technique hub (roughly 160 AI-generated pages) by layering expert examiner and reader feedback on top so the pages carry something au-delà regurgitated consensus.
• Contrarian or mis à jour takes on consensus. Testing a widely repeated claim and reporting ce que en réalité happened is information gain, même quand the “study” is petit.
• Building on a predecessor’s fonctionner. Prendre someone’s publié research, extend it, ajouter the suivant données point. You’re ajout to the corpus, pas copying it.

Ahrefs’ propre content-quality ladder (Si Quan Ong’s “How to Create Quality Content”) is a utile map ici: it climbs from simple listicles up to research studies and original ideas — effectively an information-gain ladder, prizing first-hand données over aggregation, sans ever en utilisant the term.

How to evaluate votre propre content pour information gain

Adapt Google’s helpful-content questions into a pre-publish gut vérifier:

• Ouvrir the top results pour votre target requête. Liste ce que chaque un déjà dit.
• Pour votre draft, highlight every sentence que adds a fact, number, angle, or experience pas on que liste. Si nothing is highlighted, vous have a rewrite, pas a resource.
• Demander si an AI pourrait réponse the requête entièrement from lune pages déjà ranking. Si yes, votre seulement chemin to relevance is contributing something ceux pages lack.
• Sanity-check the “value compared to other pages in search results” question literally — pas “is this good,” but “is this additive.”

L’essentiel

Information gain is a réel, utile concept sitting on top of an unconfirmed mechanism. Don’t sell it internally as a confirmed Google ranking factor with a score vous pouvez dial — that’s pas vrai, and it’ll burn votre credibility. Sell it as the chose that’s demonstrably working in an AI-heavy SERP: arrêter publishing better-optimized versions of ce que déjà exists, and commencer publishing ce que seulement vous pouvez. That’s the moat.

Connexe reading in ce cluster: entity SEO, balisage de données structurées pour AI, GEO, and AEO tout connecter to how vous obtenir surfaced and cited une fois votre content en réalité has something nouveau to dire.

AI summary

A condensed prendre on the Avancé version:

• Information gain = novelty relative to ce que a searcher has déjà seen on a topic. It’s the marginal delta, pas comprehensiveness and pas E-E-A-T. A thorough page peut have zero information gain.
• The nom comes from a granted Google patent (“Contextual estimation of lien information gain,” filed 2018, granted 2022) que scores documents 0,00–1,00 pour how beaucoup nouveau information ils ajouter and ranks partly on que.
• Google has jamais confirmed live utiliser. The patent’s propre framing is à propos de ce que to montrer suivant après a utilisateur has seen some results — pas the premier page. There’s aucun visible score, aucun API, and aucun disclosed formula; quelconque “information gain score” in a outil is an approximation.
• What’s official is Google’s helpful-content guidance asking si content provides “original information, reporting, research, or analysis” and “substantial value when compared to other pages in search results” — the même idea, sans the term.
• Pourquoi c’est important now: AI Overviews and AI Mode synthesize consensus content pour free. Genuinely nouveau information — original research, proprietary données, first-hand expertise — is ce que an AI system has to draw on vous pour au lieu de paraphrasing elsewhere, though that’s pas a guarantee of citation, ranking, or trafic (retrieval, inclusion, generation, and citation are separate steps).
• Novelty isn’t accuracy. Une page peut ajouter a genuinely nouveau claim and encore be incorrect — treat originality as un input to publish, pas a substitute pour verifying it.
• Ce que produces it: original studies, first-party données, expert interviews and first-hand experience, contrarian/mis à jour takes, extending others’ research.
• Beware fabricated specifics — precise “core mettre à jour confirmed information gain, +X% visibility” stats with aucun principal source are a myth pattern, pas données.

Documentation officielle

Principal sources. Remarque que Google’s guidance jamais uses the term “information gain” — the patent fait, but a patent is a legal filing, pas a statement à propos de live ranking.

Google — the patent (principal source, pas “guidance”)

• Contextual estimation of lien information gain (US20200349181A1) — the filing the term is named après: the 0,00–1,00 scoring description and the “additional information beyond documents already presented to the user” definition.

Google — official helpful-content guidance (uses the concept, pas the term)

• Creating utile, reliable, people-first content — the self-assessment questions à propos de original information/research/analysis and substantial valeur versus autre results. Ce is the closest official language to information gain.

Quotes from the source

On-the-record language. The Google items ci-dessous are verbatim from principal sources (the patent and Google’s helpful-content docs). Industry voices are paraphrased in the article corps plutôt que quoted, parce que I couldn’t independently re-verify leur exact wording contre the original pages — I’m pas going to put words in quotation marks I can’t confirmer.

Google — the patent (US20200349181A1)

• “An information gain score pour a donné document is indicative of additional information que is inclus in the donné document au-delà information contained in autre documents que were déjà presented to the utilisateur.” — Contextual estimation of lien information gain. Lire the patent
• “a quantitative score entre 0,00 and 1,00, with 0,00 indicating que aucun information gain is to be attendu” — the patent’s décrit scoring scale (1,00 meaning the document contient seulement information pas déjà seen). Lire the patent

Google — helpful-content guidance (the concept, sans the term)

• “Fait le contenu provide original information, reporting, research, or analysis?” Jump to quote
• “Si le contenu draws on autre sources, fait it éviter simply copying or rewriting ceux sources, and à la place provide substantial additional valeur and originality?” Jump to quote
• “Fait le contenu provide insightful analysis or interesting information que is au-delà the obvious?” Jump to quote
• “Fait le contenu provide substantial valeur quand comparé to autre pages in résultats de recherche?” Jump to quote

Playbook: ajout réel information gain

A repeatable traiter pour turning a “yet another article on X” draft into something additive.

1. Map the corpus premier. Ouvrir the top 10 results pour votre requête. In a doc, liste the distinct claims, données points, and angles chaque un déjà covers. Ce is the baseline the searcher va have “already seen.”

2. Trouver the gap. Où is the consensus thin, outdated, or untested? Ce que question do the ranking pages raise but pas réponse? Que gap is votre target.

3. Pick votre source of novelty. In rough order of durability:

• Original research / a survey / an experiment vous run.
• First-party or proprietary données vous déjà have.
• Expert interviews and first-hand experience.
• A contrarian or mis à jour prendre vous pouvez en réalité prise en charge.

4. The expert-interview loop (my par défaut). Vous almost toujours have subject-matter experts dans reach. A few times a week, message a handful of les — Slack, email, or a short AI-assisted appel — with un sharp question. Capture the story, the number, the “actually, in practice…” detail. Attribute it. That’s original information que didn’t exist on the web avant vous publié it.

5. Attribute everything. Source votre claims. Lien out. Montrer votre données. Content que sources où choses came from reads as additive; unsourced generated text reads as filler — and increasingly obtient treated que façon.

6. Pre-publish vérifier. Highlight every sentence in votre draft que isn’t déjà on un of the top-10 pages. Si little is highlighted, don’t ship it yet — go back to step 3.

7. Couche, don’t dump. Si you’re generating content at scale, don’t arrêter at the generation. Couche expert examiner, reader feedback, and réel exemples on top so chaque page carries something au-delà consensus. Volume sans novelty is the chose AI absorbs pour free.

Anti-patterns: how “information gain” goes incorrect

Treating it as a confirmed ranking factor with a score vous pouvez dial. It’s a granted patent Google has pas confirmed en utilisant in live ranking. There’s aucun Search Console field, aucun log signal, aucun public API. Quelconque “information gain score” in a outil is que tool’s approximation, pas Google’s. Don’t pitch it internally as a confirmed lever — you’ll lose credibility quand someone demande pour the source.

Repeating suspiciously precise, uncited stats. A wave of 2026-dated posts assert choses comme “the [date] core mettre à jour confirmed information gain as the dominant signal, +15–25% visibility pour original content, –30–50% pour templated content,” bas to spécifique timestamps. Aucun principal source substantiates quelconque of it. Ces lire as fabricated or hallucinated specifics riding a buzzword. Si a stat à propos de information gain has aucun traceable principal source, don’t repeat it.

Confusing “write more” with information gain. Length and comprehensiveness ne sont pas information gain. A 5 000-word article que rehashes consensus has moins of it que a 400-word post with un nouveau données point. The FAQ-stuffing déplacer — bolt 50 or 100 FAQs onto une page — is the clearest exemple: it adds words, pas novelty, and it doesn’t fonctionner.

Conflating it with E-E-A-T or “quality.” Connexe but distinct. Vous pouvez be a genuine expert and encore publish une page que adds nothing nouveau to the corpus. Information gain is the marginal delta, pas the trust/expertise judgment.

Assuming it s’applique to the premier page of results. The patent’s propre framing centers on ce que to surface suivant — assistant/follow-up contexts — après a utilisateur has seen some results, pas necessarily the initial ten blue liens. La plupart explainers skip ce scope caveat; don’t.

Claiming Google or Bing said “information gain.” Neither has utilisé the literal phrase in public ranking guidance que I’ve trouvé. It’s industry shorthand construit on a patent (Google) and general helpful-content principles. Treat quelconque attributed “information gain” quote from Google or Bing with suspicion jusqu’à vous voir the principal source.

Prompts pour information-gain reviews

Compare my draft with these competing-page notes. Build a claim matrix with one row
per substantive point and columns for: already common in the corpus, genuinely new,
new only in wording, supported by first-hand evidence, source needed, and remove or
keep. Do not assign a fake information-gain score. Finish with the three strongest
original contributions and the evidence needed to publish each.

DRAFT:
[paste]

COMPETING-PAGE NOTES:
[paste]

Turn these raw first-party materials into an evidence plan, not finished prose.
Separate proprietary data, direct observation, expert experience, original examples,
and repeatable analysis. For each candidate contribution, state the claim it could
support, the minimum method disclosure, limitations, and what would make the claim too
weak to publish. Do not infer results that are absent.

[paste interview notes, dataset description, tests, or case documentation]

Frameworks pour creating réel information gain

The corpus–claim–evidence framework

1. Corpus: Summarize ce que the current utile pages déjà agree on.
2. Claim: State ce que votre page voudrait ajouter au-delà que consensus.
3. Evidence: Nom the données, observation, experience, or source que rend the nouveau claim defensible.
4. Boundary: Document où the evidence ne fait pas generalize.

Si the claim changements seulement phrasing, it n’est pas nouveau. Si the evidence ne peut pas prise en charge the claim, it n’est pas ready.

The novelty ladder

• Restatement: Même facts, différent wording.
• Synthesis: Existing facts connected in a utile nouveau façon.
• Application: Existing knowledge testé in a spécifique context.
• Observation: First-hand evidence others ne faites pas have.
• Discovery: A reproducible finding que changements ce que the reader knows.

The ladder is a planning model, pas Google’s hidden score.

Information gain cheat sheet

Reality vérifier: Google exposes aucun public information-gain score or formula. Evaluate the contribution and its evidence, pas a made-up decimal.

Ressources utiles

My writing & speaking

• Fait AI Search Trafic Convert Meilleur Que Traditional Search? (Ahrefs) — the données behind pourquoi AI-search visibility is worth chasing, qui is the raison information gain matters commercially.
• “GEO? AEO? LLMO? What’s With All This AI SEO Stuff?” — my Ahrefs Evolve 2025 deck on AI search, où the “publish what only you have” argument lives.
• My AI Mode ranking-test post (X) — the natural experiment où content que didn’t rank in AI Mode may have lacked information gain.
• Patrick Stox on Building in the GEO Era (Unscripted SEO Podcast) — où I lay out the “message five real experts, source your claims” tactic.
• Cutting-Edge AEO Strategies with Patrick Stox (Marketing Speak) — the episode framing information gain as the réel moat, with the Ahrefs data-studies point.
• How to Créer Quality Content (Ahrefs, by Si Quan Ong) — house content: the quality ladder from listicles up to original research, effectively an information-gain ladder.

From autour the industry

• Contextual estimation of lien information gain (US20200349181A1) — the réel patent. Lire the source avant vous trust anyone’s summary of it.
• Creating utile, reliable, people-first content (Recherche Google Central) — the official self-assessment questions que décrire the concept sans the term.
• Google’s Information Gain Patent Pour Ranking Web Pages (Roger Montti, Moteur de recherche Journal) — the la plupart careful piece on scope: assistant/follow-up context, pas the premier page.
• Ce que is information gain in SEO & Pourquoi c’est important (Amanda King, Moteur de recherche Land) — the early mainstream definition.
• Information gain: ce que ce SEO “buzzword” really signifie (Andrew Holland, Moteur de recherche Land) — the AI-era reframe.
• Information Gain in SEO (Bernard Huang, Clearscope) — the Knowledge Graph framing and practical viewpoints.
• Information Gain: The SEO Theory que AI Made Mandatory (Nathan Wahl, Animalz) — the strongest “why now” argument tied to AI synthesis and multi-source citation.
• Information Gain Scores (Bill Slawski, via Go Fish Digital) — the original technical breakdown of the patent.
• Ce que Is Information Gain in SEO & Fait Google Mesurer It? (Rachel Handley, Semrush) — a solid additional definitional overview.

Testez vos connaissances: Information Gain

Five rapide questions on ce que information gain is, où it comes from, and ce que to do à propos de it. Pick an réponse pour chaque, alors vérifier.

Journal des modifications