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logo",81,36,[],{"asset":393},[394],{"type":27,"image":395,"mobileImage":401},[396],{"src":397,"alt":398,"width":399,"height":400},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002FLogos\u002Flogo-google-partner.svg","Google Partner logo",87,61,[],[403,408,413],{"buttonLink":404},[405],{"ariaLabel":9,"target":9,"url":406,"text":407,"entryType":12},"https:\u002F\u002Fpixis.ai\u002Fprivacy-policy\u002F","Privacy Policy",{"buttonLink":409},[410],{"ariaLabel":9,"target":9,"url":411,"text":412,"entryType":12},"https:\u002F\u002Fpixis.ai\u002Fleapus-csr-policy\u002F","Leapus CSR Policy",{"buttonLink":414},[415],{"ariaLabel":9,"target":9,"url":416,"text":417,"entryType":12},"https:\u002F\u002Fpixis.ai\u002Ffulfillment-policy\u002F","Pixis Fulfillment Policy","Pixis",{"uri":420,"id":421,"title":422,"url":423,"postDate":424,"dateUpdated":425,"slug":426,"sectionHandle":427,"type":428,"authors":429,"seo":445,"asset":455,"categories":464,"intro":9,"showBanner":474,"contentArea":475,"articleSelect":495,"schemaOrganization":9,"schemaWebsite":9,"schemaWebpage":9,"schemaBreadcrumb":9,"schemaArticle":9,"schemaFaq":9,"schemaSoftwareApp":9,"siteName":418},"blog\u002Fhow-to-score-content-for-e-e-a-t-clarity-and-accuracy","43415","How to Score Content for E-E-A-T, Clarity and Accuracy","https:\u002F\u002Fpixis.ai\u002Fblog\u002Fhow-to-score-content-for-e-e-a-t-clarity-and-accuracy\u002F","2026-11-13T01:23:00-05:00","2026-11-13T01:23:10-05:00","how-to-score-content-for-e-e-a-t-clarity-and-accuracy","blog","blog_Entry",[430],{"fullName":431,"asset":432,"position":440,"bio":441,"linkedIn":442,"authorPage":444},"Janvi Arora",[433],{"type":27,"image":434,"mobileImage":439},[435],{"src":436,"alt":9,"width":437,"height":438},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002FIMG_9067.jpg",846,925,[],"SEO Specialist","\u003Cp>Janvi is an SEO Specialist at Pixis, working across SEO, generative engine optimization (GEO), and AI search. A Christ University alumna, she focuses on understanding how brand discovery is changing as search expands from ranked results to AI-generated answers. Her work covers content optimization, search visibility, and helping brands adapt to the evolving ways people find information online.\u003C\u002Fp>",{"url":443},"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fjanviarora\u002F",[],{"title":446,"description":447,"advanced":448,"keywords":451,"social":452},"How to Score Content for E-E-A-T, Clarity and Accuracy | Pixis","Score content for E-E-A-T, clarity, brand voice and factual accuracy with a 1 to 5 rubric, written anchors, weighting and a two-editor calibration check.",{"canonical":449,"robots":450},"",[],[],{"facebook":453,"twitter":454},{"description":447,"title":422},{"description":447,"title":422},[456],{"type":27,"image":457,"mobileImage":463},[458],{"src":459,"alt":460,"width":461,"height":462},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002Fhow-to-score-content-for-eeat-clarity-and-accuracy-featured.png","Pixis Visibility content score and content editor cards with a claim verified badge and a score of 4.2",1920,1360,[],[465,468,471],{"title":466,"slug":467},"SEO\u002FAEO\u002FGEO","seo-aeo-geo",{"title":469,"slug":470},"Pixis Visibility","pixis-visibility",{"title":472,"slug":473},"Content Marketing","content-marketing",true,[476],{"blocks":477},[478,481,493],{"type":479,"textBlock":480},"textBlock_Entry","\u003Cp>Two editors read the same article. One calls it strong, the other calls it generic and thinly sourced. Both are right about something, and neither can point to a standard the other would accept. Multiply that by a library of a few hundred pages and \"content quality\" becomes a matter of who argues longest.\u003C\u002Fp>\u003Cp>A scoring rubric fixes the argument, not the article. It splits quality into separate dimensions, gives each a 1 to 5 scale, and writes down what a 1, a 3 and a 5 actually look like. The written anchors are the part that matters. Without them, a number is just an opinion with a decimal point.\u003C\u002Fp>\u003Cp>The four dimensions that cover most editorial risk are E-E-A-T, clarity, brand voice and factual accuracy. They fail independently: a polished, on-brand article can still carry a wrong statistic.\u003C\u002Fp>\u003Cp>This guide covers the inputs you need, the rubric with its anchors, how to weight and record scores, how to check that editors agree, and how to turn scores into an update queue.\u003C\u002Fp>\u003Ch2>Key takeaways\u003C\u002Fh2>\u003Cul>\u003Cli>Score E-E-A-T, clarity, brand voice and factual accuracy separately, each on a 1 to 5 scale with written anchors for every level.\u003C\u002Fli>\u003Cli>A simple weighting of 35 for E-E-A-T, 25 each for clarity and factual accuracy, and 15 for brand voice keeps trust-related signals at 60 percent of the total.\u003C\u002Fli>\u003Cli>Every score needs a one-sentence piece of evidence that a second editor could verify, or it cannot be defended in a review.\u003C\u002Fli>\u003Cli>Calibrate by having two editors score the same article. A gap of more than one point on any dimension means the anchor language needs tightening.\u003C\u002Fli>\u003Cli>Pixis Visibility can supply the page-level technical and content scores and hold your brand voice settings; claim verification and the final judgment stay with an editor.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Gather the inputs before you score anything\u003C\u002Fh2>\u003Cp>The rubric only produces comparable scores when every editor works from the same references. Assemble these first:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>A written brand voice reference:\u003C\u002Fstrong> a brand book or style guide that specifies tone, vocabulary, reading level and terms to avoid.\u003C\u002Fli>\u003Cli>\u003Cstrong>The article's source list or claim log,\u003C\u002Fstrong> so every statistic and factual statement can be traced to its origin.\u003C\u002Fli>\u003Cli>\u003Cstrong>The published page and its author page,\u003C\u002Fstrong> because author credentials and transparency signals are part of E-E-A-T.\u003C\u002Fli>\u003Cli>\u003Cstrong>Google's Search Quality Rater Guidelines\u003C\u002Fstrong> as the reference for E-E-A-T language about experience, credentials, sourcing and transparency.\u003C\u002Fli>\u003Cli>\u003Cstrong>One rubric owner,\u003C\u002Fstrong> a named editor who maintains the anchors and settles disputes, so scores stay comparable across articles and across human and AI-written drafts.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Score each dimension against written anchors\u003C\u002Fh2>\u003Cp>Score each article in a single pass across all four dimensions. Read the whole piece once, then score each dimension against its anchor row rather than against your memory of other articles.\u003C\u002Fp>\u003Ch3>E-E-A-T\u003C\u002Fh3>\u003Cp>A 1 means no named author, no sources and no sign of first-hand experience. A 5 means a credentialed author, stated first-hand experience, sourced claims and transparent disclosure. Weight trust most heavily, because \u003Ca href=\"https:\u002F\u002Fdevelopers.google.com\u002Fsearch\u002Fdocs\u002Ffundamentals\u002Fcreating-helpful-content\" rel=\"noopener noreferrer\" target=\"_blank\">Google's guidance on helpful content\u003C\u002Fa> describes trust as the most important of the four E-E-A-T aspects.\u003C\u002Fp>\u003Cp>Judge expertise from the content and verifiable credentials, not from an author bio that simply says \"expert.\" A claim of experience should show up as specifics only someone who did the work would know.\u003C\u002Fp>\u003Ch3>Clarity\u003C\u002Fh3>\u003Cp>Check the structure, whether the article answers its question in the first section, and readability signals such as sentence length and walls of unbroken text. A \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fglossary\u002Fclarity-scoring\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">clarity scoring\u003C\u002Fa> pass asks how easily the intended reader can extract the meaning and act on it.\u003C\u002Fp>\u003Ch3>Brand voice\u003C\u002Fh3>\u003Cp>Read a sample paragraph against the brand book: tone, vocabulary, reading level and never-mention terms. A 5 reads as if your best in-house writer wrote it. A 1 could belong to any company in your category. For how much brand context AI drafts need to reach the upper levels, see \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fblog\u002Fhow-much-brand-context-does-ai-content-need\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">how much brand context AI content needs\u003C\u002Fa>.\u003C\u002Fp>\u003Ch3>Factual accuracy\u003C\u002Fh3>\u003Cp>Run claim checks. Every statistic needs a named source, the source should be primary or authoritative, and dated data should be current. Treat any accuracy score below 4 as a stop: the article goes to review before you average anything.\u003C\u002Fp>\u003Ch2>The rubric anchors\u003C\u002Fh2>\u003Cp>Write the 1 and 5 anchors first, then fill in 2 to 4. Keep each anchor short enough that two editors can apply it the same way.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Score 1.\u003C\u002Fstrong> E-E-A-T: no named author; no sources; no first-hand experience. Clarity: hard to follow; walls of text; no answer in the first section. Brand voice: generic; could be any company. Factual accuracy: unsourced claims; wrong or missing dates.\u003C\u002Fli>\u003Cli>\u003Cstrong>Score 2.\u003C\u002Fstrong> E-E-A-T: named author but no credentials or experience signals. Clarity: understandable but bloated or full of jargon. Brand voice: correct topic; misses brand-specific vocabulary. Factual accuracy: some sources, none primary.\u003C\u002Fli>\u003Cli>\u003Cstrong>Score 3.\u003C\u002Fstrong> E-E-A-T: named author; some links; a date somewhere. Clarity: reads with effort; some confusing sections. Brand voice: mostly on-brand; one or two off-voice sentences. Factual accuracy: claims sourced but not all primary; some dates stale.\u003C\u002Fli>\u003Cli>\u003Cstrong>Score 4.\u003C\u002Fstrong> E-E-A-T: credentialed author; some first-hand experience; sourced claims. Clarity: reads easily; minor jargon; answers its question early. Brand voice: consistent voice; rare drift. Factual accuracy: well sourced; current dates; mostly primary sources.\u003C\u002Fli>\u003Cli>\u003Cstrong>Score 5.\u003C\u002Fstrong> E-E-A-T: credentialed author; stated first-hand experience; sourced claims; transparent disclosure. Clarity: reads effortlessly; no jargon; logical flow. Brand voice: on-brand throughout. Factual accuracy: every claim verified against a primary source; dates current.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Weight the scores and record the evidence\u003C\u002Fh2>\u003Cp>Combine the four scores into one weighted total: E-E-A-T at 35 percent, clarity and factual accuracy at 25 percent each, and brand voice at 15 percent. Adjust the weights if your context demands it, for example by raising accuracy in regulated industries, but keep them fixed across the library so totals stay comparable.\u003C\u002Fp>\u003Cp>An illustrative example: an article scores 3 on E-E-A-T, 4 on clarity, 2 on brand voice and 4 on factual accuracy. The weighted total is (3 × 0.35) + (4 × 0.25) + (2 × 0.15) + (4 × 0.25) = 3.35 out of 5.\u003C\u002Fp>\u003Cp>Record a one-sentence justification for each score, specific enough that a stranger could check it. \"Scored 2 on brand voice because the reading level is two grades above the brand book\" is evidence. \"Felt off-brand\" is not.\u003C\u002Fp>\u003Cp>Once the anchors are written, scoring one article takes an experienced editor roughly 10 minutes. Building the rubric, including the anchor table and the brand voice reference, takes about half a day.\u003C\u002Fp>\u003Ch2>Check that the rubric is consistent\u003C\u002Fh2>\u003Cp>A rubric is only useful if different editors produce similar scores. Three checks tell you whether it does.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Double-score a sample.\u003C\u002Fstrong> Have a second editor score the same article without seeing the first scores. Within one point per dimension means the anchors work. A wider gap means the anchor wording is too vague; tighten it before the next pass.\u003C\u002Fli>\u003Cli>\u003Cstrong>Audit the evidence sentences.\u003C\u002Fstrong> Pick a few at random and confirm a third person can verify each one from the page.\u003C\u002Fli>\u003Cli>\u003Cstrong>Watch the trend, not single scores.\u003C\u002Fstrong> An illustrative pattern: a library average moving from 2.8 to 3.5 over a quarter shows the rubric is changing what gets published.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Then connect scores to outcomes. If scores rise while search traffic falls, compare the before-and-after content impact in Google Search Console to see which dimension, if any, moves traffic for your site. A dimension that never correlates with outcomes may be measuring the wrong thing.\u003C\u002Fp>\u003Ch2>Where scoring programs break down\u003C\u002Fh2>\u003Cp>Most rubrics fail in the first quarter for predictable reasons.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Scoring on gut feel.\u003C\u002Fstrong> Without written anchors, editors disagree and scores stop being comparable.\u003C\u002Fli>\u003Cli>\u003Cstrong>Treating polish as accuracy.\u003C\u002Fstrong> A well-structured article can still carry a wrong claim. This is why clarity and accuracy are separate dimensions.\u003C\u002Fli>\u003Cli>\u003Cstrong>Accepting self-declared expertise.\u003C\u002Fstrong> An author page that asserts expertise is not evidence of it.\u003C\u002Fli>\u003Cli>\u003Cstrong>Scoring once.\u003C\u002Fstrong> Sources age and voice guidelines change. High-traffic pages need re-scoring on a schedule.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>For YMYL-adjacent topics such as health, finance or legal, bring in a specialist reviewer when accuracy needs expert verification your team cannot do in-house.\u003C\u002Fp>\u003Ch2>Score AI-written drafts with the same rubric\u003C\u002Fh2>\u003Cp>Use the same anchors for AI drafts, but read the experience and accuracy rows more strictly. In its January 2025 update to the \u003Ca href=\"https:\u002F\u002Fstatic.googleusercontent.com\u002Fmedia\u002Fguidelines.raterhub.com\u002Fen\u002F\u002Fsearchqualityevaluatorguidelines.pdf\" rel=\"noopener noreferrer\" target=\"_blank\">Search Quality Rater Guidelines\u003C\u002Fa>, Google told raters that the Lowest rating applies when all or almost all of a page's main content is copied, paraphrased, auto or AI generated, or reposted with little to no effort, originality or added value.\u003C\u002Fp>\u003Cp>For an AI draft, check every claim against its source and ask what a person actually added: a first-hand example, a tested method, an opinion the team will stand behind. For more on producing AI content that holds up, see \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fblog\u002Fhow-to-create-ai-content-that-ranks-and-gets-cited\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">how to create AI content that ranks and gets cited\u003C\u002Fa>.\u003C\u002Fp>\u003Ch2>Where Pixis Visibility fits\u003C\u002Fh2>\u003Cp>Part of this rubric is mechanical, and that part can come from a platform instead of a spreadsheet.\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fproducts\u002Fpixis-visibility\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">Pixis Visibility\u003C\u002Fa> runs page audits that give each page a technical score and a content score, list the gaps with their severity, estimate the traffic opportunity, and keep score and version history. Its content rubric scores each page on criteria including clarity and readability, factual accuracy, brand voice, and structure and flow, and flags E-E-A-T signal gaps such as missing citations. That covers much of the structural side of clarity and gives you a baseline before an editor reads a word.\u003C\u002Fp>",{"type":482,"asset":483,"assetWidth":492},"asset_Entry",[484],{"type":27,"image":485,"mobileImage":491},[486],{"src":487,"alt":488,"width":489,"height":490},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002Fpixis-visibility-content-rubric.png","Pixis Visibility page audit content rubric scoring clarity and readability, structure and flow, factual accuracy, brand voice and more on a 10-point scale",1440,778,[],"large",{"type":479,"textBlock":494},"\u003Cp>Its Strategy Brain holds your ICP, business objective, brand book, voice settings such as reading level and claim sensitivity, and must-include and never-mention terms. Drafts are written in that voice, which gives editors a consistent starting point for the brand voice row. After you update a page, the Google Search Console connection shows before-and-after content impact per page and per query.\u003C\u002Fp>\u003Cp>What stays manual: verifying each statistic against its primary source, judging first-hand experience, and assigning the final scores. Those are editorial calls, and the rubric owner should keep them.\u003C\u002Fp>\u003Ch2>Turn scores into an update queue\u003C\u002Fh2>\u003Cp>A scored library is a work order. Fix factual accuracy scores of 1 or 2 first, whatever the traffic, because wrong content damages trust wherever it is read. Then take high-traffic pages that score 2 or below on clarity or brand voice, since those fixes reach the most readers per hour of work.\u003C\u002Fp>\u003Cp>Start small: write the anchors, score ten pages with two editors, and tighten the wording wherever they disagree. Then score every new article before it ships. To keep older drafts from slipping below the bar, see \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fblog\u002Fhow-to-keep-stale-drafts-from-eroding-content-quality\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">how to keep stale drafts from eroding content quality\u003C\u002Fa>.\u003C\u002Fp>\u003Cp>Get started with \u003Ca href=\"https:\u002F\u002Fvisibility.pixis.ai\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">Pixis Visibility\u003C\u002Fa> to see page-level technical and content scores and the search impact of each update you make from your queue.\u003C\u002Fp>\u003Ch2>FAQs\u003C\u002Fh2>\u003Ch3>Is E-E-A-T a direct Google ranking factor?\u003C\u002Fh3>\u003Cp>No. Google says E-E-A-T itself is not a specific ranking factor, but its systems use a mix of factors that identify content with good E-E-A-T. Treat the rubric as an editorial quality bar that makes weak pages visibly weak.\u003C\u002Fp>\u003Ch3>What does a good score look like?\u003C\u002Fh3>\u003Cp>A reasonable internal target is 4 or above on every dimension, with nothing below 3 on factual accuracy allowed to publish. Raise the accuracy floor for regulated topics. Track your own trend rather than chasing an external benchmark.\u003C\u002Fp>\u003Ch3>How often should I re-score existing content?\u003C\u002Fh3>\u003Cp>Score every new article before it ships. Re-score high-traffic pages quarterly and the rest annually, and immediately when the underlying data ages, the brand guidelines change, or a page's search traffic or AI citations drop.\u003C\u002Fp>",[],{"blogBanner":497},{"bannerIcon":498,"bannerBrandName":503,"bannerHeading":508,"bannerCta1Text":509,"bannerCta1Url":79,"bannerCta2Text":510,"bannerCta2Url":259},[499],{"url":500,"title":501,"width":502,"height":502},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002Fv-logo.svg","V logo",78,[504],{"url":505,"title":506,"width":507,"height":60},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002Fvisibility-logo.svg","Visibility logo",191,"Increase your visibility on Google and AI search","Book a demo","Signup for free",1794562089353]