{"id":2894,"date":"2026-10-02T03:24:52","date_gmt":"2026-10-02T03:24:52","guid":{"rendered":"https:\/\/maxaeo.ai\/blog\/competitor-ai-citation-audit-template\/"},"modified":"2026-10-02T03:24:52","modified_gmt":"2026-10-02T03:24:52","slug":"competitor-ai-citation-audit-template","status":"publish","type":"post","link":"https:\/\/maxaeo.ai\/blog\/competitor-ai-citation-audit-template\/","title":{"rendered":"Competitor AI Citation Audit Template for ChatGPT and Perplexity"},"content":{"rendered":"

By maxaeo.ai \uff5c Published 2026-10-02 \uff5c Updated 2026-10-02<\/em><\/p>\n

A competitor AI citation audit template<\/strong> reveals which sources ChatGPT and Perplexity use when recommending competing products\u2014and what evidence your brand lacks. Instead of counting links alone, the template connects buyer prompts, cited pages, supported claims, competitor advantages, and specific actions in one repeatable worksheet.<\/p>\n

\"Competitor<\/figure>\n

What Is a Competitor AI Citation Audit?<\/h2>\n

A competitor AI citation audit is a structured review of the sources that AI engines use to mention, compare, or recommend competing brands. It measures not only who appears in an answer, but also which public evidence supports that visibility and whether your brand can close the gap<\/strong>.<\/p>\n

Traditional competitor research focuses on rankings, backlinks, and content topics. An AI citation audit adds three distinct layers:<\/p>\n

    \n
  1. Answer layer:<\/strong> Which brands are mentioned, ranked, or recommended?<\/li>\n
  2. Citation layer:<\/strong> Which URLs and domains appear as supporting sources?<\/li>\n
  3. Evidence layer:<\/strong> What claim does each source prove\u2014features, pricing, integrations, security, use cases, or customer outcomes?<\/li>\n<\/ol>\n

    A citation does not automatically equal a recommendation. A competitor may be named without a source, cited without favorable positioning, or recommended because an independent comparison page supplies stronger evidence. Record these outcomes separately.<\/p>\n

    Which Prompts Should the Audit Cover?<\/h2>\n

    Use prompts that represent real buyer decisions rather than isolated keywords. A practical baseline is 12 prompts across two engines, repeated three times<\/strong>, producing 72 observations: 12 prompts \u00d7 2 engines \u00d7 3 runs.<\/p>\n

    Divide the prompt set into four intent groups:<\/p>\n

    \n\n\n\n\n\n\n\n\n
    Prompt group<\/th>\nExample structure<\/th>\nWhat it diagnoses<\/th>\n<\/tr>\n<\/thead>\n
    Category discovery<\/td>\n\u201cBest tools for [job]\u201d<\/td>\nShortlist visibility<\/td>\n<\/tr>\n
    Use case<\/td>\n\u201cBest [category] for [audience or need]\u201d<\/td>\nPositioning strength<\/td>\n<\/tr>\n
    Comparison<\/td>\n\u201c[Brand A] vs [Brand B]\u201d<\/td>\nCompetitive preference<\/td>\n<\/tr>\n
    Objection<\/td>\n\u201c[Category] with [security, price, or integration requirement]\u201d<\/td>\nEvidence depth<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

    Keep the wording, location, engine, and test conditions consistent. Record the model or product surface, run date, and full answer because results can vary between runs.<\/p>\n

    For broader prompt selection, use a B2B buyer prompt coverage framework<\/a> to map questions across discovery, evaluation, validation, and purchase stages.<\/p>\n

    What Fields Belong in the Template?<\/h2>\n

    The template should turn every observation into an attributable decision. Avoid a simple \u201ccited\/not cited\u201d sheet; it cannot explain why a competitor won or what your team should change.<\/p>\n

    Copy these fields into a spreadsheet:<\/p>\n

    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    Field<\/th>\nWhat to record<\/th>\n<\/tr>\n<\/thead>\n
    Audit date<\/td>\nExact test date<\/td>\n<\/tr>\n
    Engine and surface<\/td>\nChatGPT Search, Perplexity, or another tested experience<\/td>\n<\/tr>\n
    Prompt ID<\/td>\nStable identifier for retesting<\/td>\n<\/tr>\n
    Buyer stage<\/td>\nDiscovery, comparison, validation, or purchase<\/td>\n<\/tr>\n
    Exact prompt<\/td>\nUnedited wording<\/td>\n<\/tr>\n
    Run number<\/td>\nFirst, second, or third repetition<\/td>\n<\/tr>\n
    Brand outcome<\/td>\nAbsent, mentioned, cited, or recommended<\/td>\n<\/tr>\n
    Competitor outcome<\/td>\nMention and recommendation position<\/td>\n<\/tr>\n
    Cited domain<\/td>\nNormalized root domain<\/td>\n<\/tr>\n
    Cited URL<\/td>\nExact source page<\/td>\n<\/tr>\n
    Source type<\/td>\nOwned, editorial, review, community, documentation, or marketplace<\/td>\n<\/tr>\n
    Supported claim<\/td>\nThe statement the citation substantiates<\/td>\n<\/tr>\n
    Sentiment<\/td>\nPositive, neutral, mixed, or negative<\/td>\n<\/tr>\n
    Accuracy<\/td>\nAccurate, incomplete, outdated, or incorrect<\/td>\n<\/tr>\n
    Citation recurrence<\/td>\nNumber of runs containing the source<\/td>\n<\/tr>\n
    Evidence gap<\/td>\nMissing proof, weak page, third-party gap, or extraction gap<\/td>\n<\/tr>\n
    Target action<\/td>\nCreate, update, clarify, distribute, or monitor<\/td>\n<\/tr>\n
    Owner and due date<\/td>\nResponsible team and deadline<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

    Preserve the raw AI response with each row. This allows reviewers to verify context instead of relying on a score detached from the original answer.<\/p>\n

    How Should Competitor Citation Gaps Be Scored?<\/h2>\n

    A useful score prioritizes gaps by business relevance, competitor strength, recurrence, missing evidence, and the feasibility of taking action. The following 100-point Citation Opportunity Score<\/strong> is an original framework designed to prevent teams from chasing every observed URL.<\/p>\n

    Score each factor from 1 to 5:<\/p>\n

    \n

    Opportunity Score = (Prompt Importance \u00d7 5) + (Competitor Dominance \u00d7 4) + (Source Recurrence \u00d7 4) + (Evidence Deficit \u00d7 4) + (Actionability \u00d7 3)<\/strong><\/p>\n<\/blockquote>\n

    The maximum is 100 points.<\/p>\n