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Chaudhary",[446],{"type":27,"image":447,"mobileImage":451},[448],{"src":449,"alt":9,"width":450,"height":450},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002F1730657296846.jpeg",800,[],"Head of Visibility and VP-Business","\u003Cp>Suraj is the Head of Visibility and VP-Business at Pixis. An ex-Bain consultant with experience across growth, strategy, and operations, he is a thought leader AI search visibility and helps businesses understand how discoverability is changing in the age of generative search. Having scaled Visibility to $3M ARR in just 2 months is a testimony to his understanding of the space!\u003C\u002Fp>",{"url":455},"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fchaudharysuraj\u002F?skipRedirect=true",[],{"title":458,"description":459,"advanced":460,"keywords":463,"social":464},"GEO is won prompt by prompt, not by a score | Pixis","See which brands AI engines name for each buyer question. Build a GEO competitor matrix, inspect the sources, and prioritize the prompts that matter.",{"canonical":461,"robots":462},"",[],[],{"facebook":465,"twitter":466},{"description":459,"title":458},{"description":459,"title":458},[468],{"type":27,"image":469,"mobileImage":474},[470],{"src":471,"alt":9,"width":472,"height":473},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002FGEO-is-won-prompt-by-prompt-not-by-a-score.png",1920,1360,[],[476,479],{"title":477,"slug":478},"SEO\u002FAEO\u002FGEO","seo-aeo-geo",{"title":480,"slug":481},"Pixis Visibility","pixis-visibility",true,[484],{"blocks":485},[486],{"type":487,"textBlock":488},"textBlock_Entry","\u003Cp>A buyer never sees your visibility score. They ask one question, read the answer, and consider the brands it names. If your brand is missing from that answer, a strong average across unrelated questions does little to help you.\u003C\u002Fp>\u003Cp>That is why I want GEO work to start with the individual prompt. Which question did we miss? Who appeared instead? What information did the answer use, and what could we improve?\u003C\u002Fp>\u003Cp>A competitor matrix makes those questions visible. It maps the prompts you track against the brands AI engines name, so you can inspect your presence question by question and engine by engine.\u003C\u002Fp>\u003Cp>The score still has a job. It tells you whether visibility is moving across your tracked set. The matrix tells you where to investigate and what work to prioritize.\u003C\u002Fp>\u003Ch2>Key Takeaways\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cstrong>Read the prompts behind the score.\u003C\u002Fstrong> Two brands can have the same visibility rate while appearing in very different customer decisions.\u003C\u002Fli>\u003Cli>\u003Cstrong>Separate presence, position, and citations.\u003C\u002Fstrong> Getting named, appearing early, and earning a link to your website are different outcomes.\u003C\u002Fli>\u003Cli>\u003Cstrong>Compare engines and repeat runs.\u003C\u002Fstrong> One answer is a snapshot. A repeated pattern gives you a stronger basis for deciding what needs attention.\u003C\u002Fli>\u003Cli>\u003Cstrong>Inspect the evidence before writing.\u003C\u002Fstrong> A missing mention may call for a page update, clearer product information, or better third-party coverage. It does not automatically require a new article.\u003C\u002Fli>\u003Cli>\u003Cstrong>Prioritize relevant customer decisions.\u003C\u002Fstrong> Start with prompts where your product fits the need and a stronger answer would matter to a buyer. An empty row is not automatically an easy win.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>What a competitor matrix shows\u003C\u002Fh2>\u003Cp>In GEO, a competitor matrix is a grid of tracked prompts and the brands named in AI answers. Each row represents a question. Each brand has a column. The cells show how that brand performs for the question under the selected measurement conditions.\u003C\u002Fp>\u003Cp>That is different from a feature comparison matrix, where rows might contain pricing, integrations, and product capabilities. Here, the subject is the AI answer itself: whether a brand appears, where it appears, and how consistently it is included.\u003C\u002Fp>\u003Cp>A hypothetical example shows why the row matters:\u003C\u002Fp>\u003Cp>For “What is AI advertising software?”, your brand appears in all three runs. Competitor A appears in two, and Competitor B in one.\u003C\u002Fp>\u003Cp>For “Best AI advertising software for a team without a designer,” your brand is absent from all three runs. Competitor A appears every time, and Competitor B appears twice.\u003C\u002Fp>\u003Cp>For “Can AI advertising software work with our existing approval process?”, your brand appears once, Competitor A twice, and Competitor B not at all.\u003C\u002Fp>\u003Cp>\u003Ci>These are illustrative results for one engine, not Pixis customer data or a reproduction of the product's cell calculation.\u003C\u002Fi>\u003C\u002Fp>\u003Cp>The first row looks healthy. The second exposes a gap in a specific buying situation. The third suggests an implementation question worth investigating.\u003C\u002Fp>\u003Cp>An aggregate score compresses those differences. The matrix preserves them.\u003C\u002Fp>\u003Ch2>Why one AI visibility score cannot set your priorities\u003C\u002Fh2>\u003Cp>Suppose two brands appear in 30% of their tracked prompts. One appears mainly in definitions and general how-to answers. The other appears in vendor comparisons and questions about choosing a platform for a particular use case.\u003C\u002Fp>\u003Cp>The percentages match. The questions do not. Without inspecting the prompt set, you cannot tell whether either brand is appearing at the moments its team cares about.\u003C\u002Fp>\u003Cp>Even within one row, a mention needs context. An answer can name your product as its first recommendation, include it late in a list, or mention it only to explain why it does not fit. Those are different outcomes.\u003C\u002Fp>\u003Cp>I would keep three observations separate:\u003C\u002Fp>\u003Cp>\u003Cstrong>Presence:\u003C\u002Fstrong> Was the brand named?\u003C\u002Fp>\u003Cp>\u003Cstrong>Position:\u003C\u002Fstrong> Where did it appear within the answer, using a consistent recording method?\u003C\u002Fp>\u003Cp>\u003Cstrong>Citation:\u003C\u002Fstrong> Did the answer link to your website or to another source discussing your brand?\u003C\u002Fp>\u003Cp>A brand can be recommended without a link to its own website. A page can also be cited without its owner being recommended as a vendor. Counting both as the same success makes the report harder to act on.\u003C\u002Fp>\u003Ch2>Build a competitor matrix in five steps\u003C\u002Fh2>\u003Ch3>1. Start with questions your buyers actually need answered\u003C\u002Fh3>\u003Cp>Pull wording from sales conversations, support tickets, customer research, and relevant Search Console queries. Include questions about the category, the task, the vendor choice, and the practical constraints around adoption.\u003C\u002Fp>\u003Cp>The constraint often makes the prompt useful. “Best advertising software” is a different question from “best advertising software for a small team without a designer.” Removing the team requirement makes the monitoring easier but the result less relevant.\u003C\u002Fp>\u003Cp>Use Search Console as a source of observed Google queries, not a record of private chatbot conversations. Complete, relevant questions can become test prompts. Fragments need context before you expand them.\u003C\u002Fp>\u003Cp>Keep the original wording beside the version you test. If you add a region, budget, or audience assumption, record it.\u003C\u002Fp>\u003Ch3>2. Tag each prompt by the decision it supports\u003C\u002Fh3>\u003Cp>Funnel stage is a useful starting point. Add the product line, use case, audience, and market where they change the answer.\u003C\u002Fp>\u003Cp>A definition prompt may support education. A comparison prompt may support a shortlist. An implementation prompt may resolve an objection that is holding up a purchase.\u003C\u002Fp>\u003Cp>Do not assume every shortlist question matters more than every educational question. Prioritize against your actual customer journey. The question that prevents a buyer from adopting your product deserves attention even if it does not contain “best” or “versus.”\u003C\u002Fp>\u003Ch3>3. Collect answers across engines and repeated runs\u003C\u002Fh3>\u003Cp>Run the same wording across the engines you are monitoring, such as ChatGPT, Claude, Perplexity, and Gemini. Keep the collection date and relevant settings with the answers.\u003C\u002Fp>\u003Cp>Use consistent conditions where practical: geography, language, account or session context, and whether web search is enabled. Record the conditions you can control and acknowledge those you cannot.\u003C\u002Fp>\u003Cp>Run prompts more than once. Repetition helps distinguish a recurring omission from an isolated answer, but it does not eliminate variation. Look at the engine-specific results before interpreting a pooled total.\u003C\u002Fp>\u003Ch3>4. Let the answers reveal the competitor set\u003C\u002Fh3>\u003Cp>Start with your known rivals, then record other brands the answers repeatedly include. Your AI competitor set can differ from the companies you encounter in sales deals.\u003C\u002Fp>\u003Cp>Normalize brand aliases so a company name and its product name are not counted as two unrelated competitors. Preserve the original answer so you can check the classification later.\u003C\u002Fp>\u003Cp>Record whether the brand is recommended, mentioned incidentally, or described negatively. Keep the linked sources separately. This lets you investigate an inaccurate description without treating every additional mention as progress.\u003C\u002Fp>\u003Ch3>5. Build the grid and preserve the answers behind it\u003C\u002Fh3>\u003Cp>Lay out prompts as rows and brands as columns. Use a defined cell measure, such as mention frequency across repeated runs, and label it clearly.\u003C\u002Fp>\u003Cp>Keep a supporting record with the engine, run date, brand position, recommendation context, citation URLs, and full answer. The heatmap should help you select a row; the underlying answer should help you understand it.\u003C\u002Fp>\u003Cp>Save a baseline before making changes. Repeat the same prompt set on a consistent schedule, and keep newly added prompts separate when comparing periods. Changing the question mix can change the score without any improvement on the original questions.\u003C\u002Fp>\u003Ch2>How to read the Competitor Matrix in Pixis Visibility\u003C\u002Fh2>\u003Cp>The Competitor Matrix in \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fproducts\u002Fpixis-visibility\u002F\">Pixis Visibility\u003C\u002Fa> brings prompts and competing brands into a heatmap, with your brand's column first. Brand headers show rank, visibility rate, and average position. The Platforms tab separates the view by engine.\u003C\u002Fp>\u003Cp>The workflow described here runs each prompt three times across four engines, giving you 12 answers to inspect. That provides more evidence than a single response, while still leaving room for variation between runs and engines.\u003C\u002Fp>\u003Cp>Read cell shading against the metric shown in the interface. A darker cell indicates stronger performance under that measure. It does not explain why the brand appeared, and it does not predict how much work will be required to change the answer.\u003C\u002Fp>\u003Cp>Four patterns are worth investigating:\u003C\u002Fp>\u003Cp>\u003Cstrong>Your brand is absent while rivals appear repeatedly.\u003C\u002Fstrong> Inspect the recommendation criteria and cited sources. If your product fits the question, investigate whether your coverage is incomplete or whether relevant external evidence is missing.\u003C\u002Fp>\u003Cp>\u003Cstrong>One rival appears across related prompts.\u003C\u002Fstrong> Look for the shared use case, claims, and cited pages. The work may require a coordinated update across the theme rather than one article addressing one question.\u003C\u002Fp>\u003Cp>\u003Cstrong>A rival appears often but late.\u003C\u002Fstrong> Read how the answer treats each mention. An early recommendation and a late incidental reference are different outcomes. Strengthen the evidence for the selection criteria relevant to your product before treating position as the problem.\u003C\u002Fp>\u003Cp>\u003Cstrong>Few brands appear in a row.\u003C\u002Fstrong> Check whether the question asks for a vendor recommendation at all. It may reveal an underserved buyer need, or it may simply call for an explanation. Reassess the prompt before commissioning content.\u003C\u002Fp>\u003Cp>A pale row might contain an opportunity. It might also contain a question that needs an explanation rather than a vendor recommendation. Read the answer before calling it unclaimed territory.\u003C\u002Fp>\u003Ch2>Inspect the winning answers before creating a brief\u003C\u002Fh2>\u003Cp>A missing brand mention is a finding. It is not yet a diagnosis.\u003C\u002Fp>\u003Cp>Take the prompt about software for a team without a designer. Open the answers where competitors appear and inspect what those answers emphasize. Do they discuss templates, creative review, onboarding, or the need for specialist support?\u003C\u002Fp>\u003Cp>Then read the cited pages. Check whether the sources support the recommendation and whether your own pages explain the same requirements with accurate, specific information.\u003C\u002Fp>\u003Cp>The action should follow what you find:\u003C\u002Fp>\u003Cul>\u003Cli>If your product fits but the relevant page omits the capability, update that page with a clear explanation and evidence.\u003C\u002Fli>\u003Cli>If an external review contains outdated information, investigate the correction with that publisher.\u003C\u002Fli>\u003Cli>If the answer relies on a comparison your site does not address, decide whether distinct comparison coverage would help the buyer.\u003C\u002Fli>\u003Cli>If your product does not meet the constraint, accept that the recommendation may be appropriate.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Google's \u003Ca href=\"https:\u002F\u002Fdevelopers.google.com\u002Fsearch\u002Fdocs\u002Fappearance\u002Fai-features\">guidance on AI features\u003C\u002Fa> describes how AI Mode and AI Overviews can use multiple related searches to develop responses. That is a useful reason to investigate supporting subtopics as well as the exact prompt. It is not evidence that every engine selects brands in the same way.\u003C\u002Fp>\u003Cp>When an answer misstates your product, our guide to \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fblog\u002Fthe-ai-narrative-gap-the-story-you-tell-vs-what-ai-says\u002F\">the AI narrative gap\u003C\u002Fa> explains how to compare the description with verified facts.\u003C\u002Fp>\u003Ch2>Put each relevant prompt into a work queue\u003C\u002Fh2>\u003Cp>The matrix becomes useful when a row has an owner and a next action. I would use four queues, then prioritize within them by customer relevance and the evidence available.\u003C\u002Fp>\u003Ch3>Defend answers where your presence is slipping\u003C\u002Fh3>\u003Cp>Compare prompts you previously appeared in with the latest repeated runs. Inspect whether the change is isolated to one engine, whether citations changed, and whether your information remains current.\u003C\u002Fp>\u003Cp>A single missing mention may be variation. A persistent decline across comparable checks deserves investigation. Review the page, technical accessibility, and competing sources before deciding what to change.\u003C\u002Fp>\u003Ch3>Correct inaccurate or misleading descriptions\u003C\u002Fh3>\u003Cp>Verify the underlying fact first. If the answer describes a real limitation, explain it clearly. If it contains outdated or incorrect information, identify the cited source where available and make the current evidence easier to find.\u003C\u002Fp>\u003Cp>Treat accuracy as a separate objective from visibility. More mentions of an incorrect claim would make the problem worse.\u003C\u002Fp>\u003Ch3>Strengthen relevant prompts where you already appear\u003C\u002Fh3>\u003Cp>Inspect answers where your brand is included but the recommendation is weak, late, or poorly explained. Look for a selection criterion your content does not substantiate.\u003C\u002Fp>\u003Cp>An early mention is useful only in context. Being named first in a list of unsuitable products is not the same as being recommended for the buyer's need.\u003C\u002Fp>\u003Ch3>Address omissions where your product fits\u003C\u002Fh3>\u003Cp>Start with missing prompts that matter to a customer decision and where you have credible information to contribute. Review existing coverage before commissioning a page.\u003C\u002Fp>\u003Cp>An empty row is not automatically cheaper to improve than a row with an established rival. The effort depends on the answer's requirements, your product's fit, and the evidence already available.\u003C\u002Fp>\u003Cp>For each selected row, record the prompt, target engine, observed issue, proposed change, owner, and review date. That turns the matrix into a work plan an editor or SEO team can execute.\u003C\u002Fp>\u003Ch2>Measure the prompt you worked on\u003C\u002Fh2>\u003Cp>After publishing an update, allow for discovery and indexing where relevant, then repeat the same prompt under comparable conditions. Immediate testing can provide a check, but it may be too early to evaluate the update's effect.\u003C\u002Fp>\u003Cp>Compare mention frequency, recommendation context, position, and citations against the baseline. Preserve the answers so the team can inspect what changed.\u003C\u002Fp>\u003Cp>A darker cell is evidence of movement in the measured result. It is not proof that your edit caused it. Model updates, competitor activity, and answer variation can also contribute.\u003C\u002Fp>\u003Cp>Repeat the check and look for a sustained pattern. If the result does not improve, revisit the diagnosis before generating more content. The issue may be product fit, external evidence, or a page that still does not answer the constraint.\u003C\u002Fp>\u003Cp>Track business outcomes separately where attribution is available. The matrix measures your presence in the answers you collected; it does not measure how many buyers saw those answers or how many deals they influenced.\u003C\u002Fp>\u003Ch2>Start with ten questions your team can act on\u003C\u002Fh2>\u003Cp>Choose ten questions that recur in customer conversations. Keep the wording specific, include the buying constraints, and select questions your product can answer credibly.\u003C\u002Fp>\u003Cp>Run them in the Analysis Hub and open the Competitor Matrix. Inspect a missing row, a row where your presence has weakened, and a row where you appear but the explanation is inaccurate or incomplete.\u003C\u002Fp>\u003Cp>For each, write down the evidence and the next action. I would rather see three rows with a clear diagnosis and an owner than a hundred prompts added to a dashboard nobody uses.\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fproducts\u002Fpixis-visibility\u002F\">Explore Pixis Visibility\u003C\u002Fa> to compare answers across engines and turn prompt-level findings into content work.\u003C\u002Fp>\u003Ch2>Frequently Asked Questions\u003C\u002Fh2>\u003Ch3>What is a competitor matrix in GEO?\u003C\u002Fh3>\u003Cp>It is a grid that compares tracked prompts with the brands named in AI answers. Depending on the measurement, cells can show mention frequency or other performance indicators. It helps you inspect individual questions instead of relying only on an aggregate visibility score.\u003C\u002Fp>\u003Ch3>How is it different from a product comparison matrix?\u003C\u002Fh3>\u003Cp>A product comparison matrix compares capabilities, pricing, or other vendor attributes. A GEO competitor matrix compares brand presence across AI answers to selected prompts.\u003C\u002Fp>\u003Ch3>How many prompts should I track?\u003C\u002Fh3>\u003Cp>Start with a set that covers relevant customer decisions and that your team can review. Ten recurring buyer questions can be a useful pilot. Expand by product line, audience, or use case when those distinctions change the answer and you have capacity to act on the findings.\u003C\u002Fp>\u003Ch3>Why do competitors appear where my brand does not?\u003C\u002Fh3>\u003Cp>Inspect the answer and its sources before deciding. Competitors may fit the stated need better, have clearer supporting information, or appear in sources the engine uses. A missing mention alone does not establish which explanation applies.\u003C\u002Fp>\u003Ch3>How often should I rerun prompts?\u003C\u002Fh3>\u003Cp>Use a consistent cadence suited to the pace of your work. Monthly checks can provide a baseline, with additional checks after meaningful updates and enough time for discovery. Repeat an unexpected result before treating it as a persistent change.\u003C\u002Fp>\u003Ch3>How is a competitor matrix different from share of voice?\u003C\u002Fh3>\u003Cp>Share of voice aggregates brand presence against a defined competitor set using a specified calculation. A competitor matrix preserves the individual prompts behind that aggregate. Check the denominator and collection method before comparing scores across tools or periods.\u003C\u002Fp>\u003Ch3>Does a missing brand mention mean I need a new page?\u003C\u002Fh3>\u003Cp>No. An existing page may need a substantive update, an external source may need correction, or the product may not fit the question. Create a separate page when the intent warrants distinct coverage and you can contribute useful evidence.\u003C\u002Fp>\u003Ch3>Does an improved cell prove a content change worked?\u003C\u002Fh3>\u003Cp>It shows that the measured answer pattern improved. Repeat checks and source inspection help assess whether the result persists and whether the updated material appears to contribute. The cell alone does not establish causation.\u003C\u002Fp>",[],{"blogBanner":491},{"bannerIcon":492,"bannerBrandName":497,"bannerHeading":502,"bannerCta1Text":503,"bannerCta1Url":93,"bannerCta2Text":504,"bannerCta2Url":272},[493],{"url":494,"title":495,"width":496,"height":496},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002Fv-logo.svg","V logo",78,[498],{"url":499,"title":500,"width":501,"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",1791890927163]