The 30-Day Plan Workbook

• Day 1 Write 40 to 50 buyer questions that never contain your brand. Write them the way a buyer types, split across researching, comparing and ready-to-buy intents: category questions, alternatives questions, problem questions. A question with your name in it only confirms what someone already knew to look for; the broad ones are the real test. Start tracking them across ChatGPT, Perplexity, Gemini, Claude and Grok.

  Role: you are a senior B2B buyer researching [category, e.g. AI visibility software] for [audience, e.g. a UK SaaS team]. You have never heard of [brand] ([website]). Task: write exactly 50 questions such a buyer would type into ChatGPT or Perplexity, in three groups: 20 researching questions about the problem space, 15 comparing questions that may name real competitors such as [competitor 1] and [competitor 2], and 15 ready-to-buy questions. Rules: never include the name [brand] in any question. No two questions may open with the same three words. Write the way people type, not marketing copy. Good example: which tools track brand mentions in ChatGPT. Bad example: what are the best cutting-edge AI visibility solutions on the market today. Output: a numbered list under the three group headings, nothing else.

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• Day 2 Baseline share of voice against 3 to 5 named competitors. Visibility on its own says little; the number that moves decisions is your share of answers against the rivals a buyer would actually weigh. Record the baseline per engine, per prompt group, and screenshot it: the before picture cannot be taken later.

  Role: you are a marketing analyst designing an AI share of voice baseline. Context: my brand is [brand] ([website]); the competitors are [competitor 1], [competitor 2] and [competitor 3]; the market is [category]. Task: 1) design a tracking sheet with the exact columns to record per question, per platform, for ChatGPT, Perplexity, Gemini, Claude and Grok; 2) define how to score a mention, a recommendation and a position, and fill in one worked example row; 3) give the share of voice formula per platform. Rules: repeated runs are mandatory; state how many runs per question make the number trustworthy and why a single run is statistical noise. Output: the column list, the scoring rules, the worked example row, the formula, then five bullets on run counts. Under 400 words.

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• Day 3 Run the wrong-facts audit. Ask each assistant who you are, what you cost, who you are for. Record every factual error and the overall sentiment. Correcting what AI says wrongly about you is usually the fastest visible win in the whole plan, because the fix is your own pages saying the true thing extractably.

  Role: you are a sceptical buyer who has never heard of [brand]. Context: below are an AI assistant's answer about [brand] and the true facts. Task: compare them and list every error, outdated claim and misleading emphasis in the answer. Rules: rank the list worst first by how much each item would cost a sale; for every item, name the page on [website] that should state the true fact in plain text a machine can quote; do not soften anything. Output: a ranked table with three columns: what the answer got wrong, the truth, the page to fix. The answer: [paste the AI answer]. The facts: [paste 5 to 10 true facts: what you do, pricing, who you serve, key features].

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• Day 4 Set up Bing Webmaster Tools and IndexNow. If Google Search Console is not already verified, start there: the plan assumes it, and Bing imports from it in minutes. Then verify the site in Bing Webmaster Tools, submit the sitemap, wire IndexNow so new and changed pages reach the index in days rather than weeks, and open the AI Performance report, in public preview since February 2026: free, first-party data on where Microsoft Copilot and Bing's AI experiences cite you. Last, find the referral report in Google Analytics: AI-assistant visits arrive as referrals from hosts like chatgpt.com and perplexity.ai, and that report is where this plan's traffic will show up.

  Role: you are a technical SEO walking a marketer through a setup. Assume nothing is set up yet. Context: my site is [website], running on [CMS or framework, e.g. WordPress]. Task: give one checklist covering 1) verifying the site in Google Search Console if it is not already, sitemap submitted; 2) verifying in Bing Webmaster Tools, importing from Google Search Console where possible; 3) submitting the sitemap to Bing; 4) enabling IndexNow the easiest supported way on my stack; 5) finding the AI Performance report once data arrives; 6) finding where Google Analytics 4 shows referral visits from AI assistants such as chatgpt.com and perplexity.ai. Rules: number every step; mark any step that needs a developer with the word DEV; where a step differs by platform, give only the [CMS or framework] path; 20 steps maximum. Output: the numbered checklist, then one line on what to check back on in 7 days.

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• Day 5 Fix your entity records. One canonical name, spelled one way, everywhere: site, schema, profiles, third-party listings. Check Wikidata for a wrong or missing label. Publish an Organization node whose sameAs links point at profiles that genuinely exist, because a wrong sameAs actively misresolves you, which is worse than none.

  Role: you are an entity SEO auditor. Context: my brand is [brand] in the [category] market. The name currently appears here: [list your website, LinkedIn, G2 or Capterra, Crunchbase, X, Wikidata and any others]. Task: 1) check whether the name is spelled identically everywhere and flag every variant; 2) list which profiles are missing, unclaimed or stale; 3) describe what a machine resolving [brand] into one entity would get wrong today; 4) write one canonical positioning sentence for [brand]. Rules: the sentence must be under 25 words, name what it is, who it serves and the category [category], and contain no superlatives, so it can be pasted verbatim on every profile. Output: four short sections in that order, the sentence last on its own line.

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• Days 6 to 7 List the pages AI already cites in your category. Your tracking shows which domains and which specific articles the engines keep quoting when they answer your category questions. That list, usually dominated by best-of roundups and review sites, is week 4's outreach plan. File it now.

  Role: you are a digital PR researcher building an outreach list. Context: when AI assistants answered my [category] questions this week, they cited these sources: [paste the URLs or domains from your tracking]. My brand is [brand]. Task: 1) group the sources into best-of listicles, review sites, community threads, publications and vendor sites; 2) rank the individual pages by how often they appear; 3) shortlist the ten most-cited pages a [category] vendor could realistically be added to. Rules: for each shortlisted page, add one line naming who runs it and what they would need from me; exclude pages that clearly only cover a segment [brand] does not serve. Output: the five groups, then the shortlist as a numbered list of ten.

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You end week 1 holding a per-engine baseline, a list of what AI gets wrong, and the exact pages to be on.

Week 1 in full: timings, worked examples and what goes wrong

• Day 8 Run the view-source test on every money page. Right-click, view source, search for a full sentence of your key copy. If the sentence is not in the served HTML, it does not exist for most AI retrieval. Content that only appears after JavaScript runs, or sits inside an embedded player, fails this test while looking perfect in a browser.

  Role: you are simulating an AI crawler, which reads only the served HTML of a page and runs no JavaScript. Context: below is raw view-source HTML from [website]. Task: 1) list the important claims that exist as plain text in this HTML; 2) list what is missing because it only arrives through scripts, embeds or images; 3) rank the missing pieces by how much each matters to a buyer deciding. Rules: quote the exact missing sentence or fact where you can; ignore styling and boilerplate; if the page holds under 200 words of real text, say so first in one blunt sentence. Output: three sections in that order, the ranking as a numbered list. HTML: [paste the page source, or its first few hundred lines].

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• Days 9 to 10 Restructure the answers to come first. Put the answer in the first sentence of the page and of every section, phrase headings as the questions buyers ask, keep one h1 and a clean heading outline, and name the entity instead of writing pronouns: a section beginning with your brand name survives being quoted alone, and one beginning with a bare pronoun does not.

  Role: you are an editor restructuring a page for AI retrieval, where citations concentrate in the first third of a page. Context: the page below is from [website]; the buyer is [audience]; the brand is [brand]. Task: 1) produce a new heading outline with one h1 and every section heading phrased as a question that buyer would ask; 2) write a 40 to 60 word direct answer for the page and for each section, with the answer in the first sentence. Rules: start sections with the name [brand], never a pronoun; keep every true fact and cut nothing that matters; add no claims I did not give you; plain language over marketing tone. Output: the outline first, then the rewritten openings under each heading. Page copy: [paste].

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• Days 11 to 12 Add facts a machine can check. Real numbers with dates, named sources, first-hand results, quotations from people who exist. Extractive answers lift specific sentences that stand alone, and specificity is the one property a competitor cannot generate with the same model you fear.

  Role: you are a fact editor hunting vague claims. Context: below is a page from [website]; the brand is [brand]. Task: find every vague claim (words like fast, leading, powerful, seamless, trusted) and propose a checkable replacement for each: a real number with a date, a named source, a first-hand result, or a quotation from a named person at [brand]. Rules: never invent a figure; where no real number exists yet, write FILL followed by the exact internal question I must answer to get it; keep each replacement to one sentence. Output: a two-column list, vague claim then replacement, followed by the FILL questions as a separate numbered list. Page: [paste].

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• Day 13 Refresh the pages you want cited, honestly. Update the content itself, then the visible date, the schema date and the sitemap together. Never restamp an unchanged page: a wrong revision date costs the whole site's freshness signal, not one page's.

  Role: you are a content editor planning genuine updates, knowing AI answers favour recently maintained pages. Context: these pages on [website] matter most for [category] questions: [paste up to ten URLs]. Task: for each page, say what a genuine update would look like this quarter: a statistic to refresh, a section the market has moved past, or a new question the page should answer. Rules: say plainly which pages need no update at all; no cosmetic suggestions, because I only change a visible date when content genuinely changed; one to three lines per page. Output: one entry per page in the order given, each starting with the word UPDATE or the word LEAVE.

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• Day 14 Run a readiness audit and file the fix list. Close the week by auditing the site the way a machine reads it: rendering, structure, extractability, entity clarity. RankX AI's readiness check is free and takes a URL; whichever tool you use, the output of day 14 is a ranked fix list for the weeks ahead.

  Role: you are an AI retrieval system scoring a page. Context: read [URL or pasted HTML of your most important page]; the brand is [brand]; the market is [category]. Task: score the page 1 to 10 on five axes: text visible in the served HTML, answer-first structure, extractable facts, entity clarity around [brand], heading quality. Rules: justify every score in one sentence; for every score under 8, give the single highest-impact fix, concrete enough to act on today; end with the three [category] questions this page could plausibly be cited for and the one it answers best now. Output: a five-row score table, the fixes as a numbered list, then the three questions.

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You end week 2 with pages a crawler can quote, answers at the top, and a fix list ranked by impact.

Week 2 in full: timings, worked examples and what goes wrong

• Days 15 to 16 Read the fan-out queries and align your titles. When an assistant answers, it first writes its own sub-queries: variations, modifiers, adjacent questions nobody typed. Your tracking records them. Rewrite titles, descriptions and section headings to use those exact phrasings, and give each sub-question its own section rather than sending the engine elsewhere for it.

  Role: you are a search engine generating fan-out queries. Context: a buyer asked an AI assistant: [paste one of your category questions]. The market is [category]. My current page titles and headings: [paste them from your site]. Task: 1) write the 15 sub-queries the assistant would most plausibly search before answering: variants, comparisons, modifiers and adjacent questions, phrased the way a search engine expects; 2) match each sub-query against my titles and headings. Rules: no duplicate sub-queries; for every unmatched one, say whether an existing page should absorb it or a new page is needed, and name the page. Output: the 15 sub-queries numbered, each followed by the word MATCHED plus the title, or the word GAP plus the recommendation.

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• Days 17 to 19 Build comparison and alternatives pages worth citing. Name six or more tools including your rivals, corroborate every claim with something checkable, and say plainly when a competitor is the better choice, because a page that wins everything is a page nothing cites. These are the bottom-of-funnel questions assistants answer daily.

  Role: you are an honest industry analyst drafting a comparison page. Context: the page is titled [competitor 1] alternatives, for [audience]. It includes [brand] and at least six real tools: [list them]. Task: outline the page. For each tool: who it fits best, one genuine strength, one genuine limit, and public pricing if known. Add a section on when [competitor 1] remains the right choice, and one on when [brand] is the wrong choice. Rules: every claim must be checkable; mark anything uncertain with the word VERIFY rather than asserting it; no superlatives; no tool wins everything, because a page where one does is a page nothing cites. Output: the full outline with headings phrased as buyer questions, roughly 60 words of notes per tool.

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• Days 20 to 21 Turn your expertise into crawlable LinkedIn text. Publish the week's strongest material as native LinkedIn posts and articles under named authors. For B2B it is the text channel the engines actually read, and it compounds with the mention work in week 4.

  Role: you are a LinkedIn ghostwriter for [named person, role at brand]. Context: the audience is [audience]. Source material: [paste the piece or its key points]. Task: write three native posts from it, three different angles: one contrarian take, one how-we-did-it, one surprising data point. Rules: a specific number or first-hand finding in the first two lines of each; 120 to 200 words per post; plain text; at most three hashtags; end each on a genuine question; mention [brand] at most once per post; no engagement-bait phrasing and no emoji walls. Output: the three posts, each under its angle as a one-word label.

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You end week 3 speaking the retrieval layer's own language, with comparison pages built to be quoted.

Week 3 in full: timings, worked examples and what goes wrong

• Days 22 to 24 Pitch the pages AI already cites. Take the citation list from week 1 and ask for inclusion in the specific best-of articles and directories the engines keep quoting. Lead with a fresh, checkable number they cannot get elsewhere, and go to the outlets that get reprinted first.

  Role: you are writing outreach asking for inclusion in an article AI assistants keep citing. Context: the writer runs [article title and URL], cited for [category] questions. My template: [paste the outreach template from the workbook]. My brand: [brand]. Positioning sentence: [paste it]. Two checkable numbers they cannot get elsewhere: [number one, with its date] and [number two, with its date]. Task: personalise the template into a ready-to-send email, plus two subject line options. Rules: under 140 words; exactly one specific, true sentence about their piece and no other flattery; make declining feel easy; no follow-up threats; the numbers do the selling. Output: the two subject options first, then the email body.

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• Days 25 to 26 Push review volume on G2 and Capterra. Ask happy customers for reviews this week, not eventually. Complete the profiles, use your one canonical positioning sentence, and aim for steady volume rather than a perfect score.

  Role: you are a customer marketing lead writing a review-request sequence. Context: brand [brand]; platform [G2 or Capterra]; sender [named person]; trigger moment [a success moment in your product, e.g. their first report ships]. Task: write three messages: 1) a 60-word ask sent at the trigger moment; 2) one gentle follow-up a week later; 3) a short thank-you once a review appears. Rules: ask for an honest review, never a positive one; no incentives beyond what the platform allows; suggest the reviewer mention their specific use case, because specific reviews are what AI answers quote; no guilt in the follow-up. Output: the three messages, labelled 1 to 3, nothing else.

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• Days 27 to 28 Show up where the threads are, by hand. Answer real questions in the Reddit and community threads the engines cite, as a named person, without automation, because bought or botted placements die with the accounts. Pitch one or two pieces of digital PR for plain-text mentions: the mention is the asset even when no link comes with it.

  Role: you are a community strategist who despises spam. Context: AI assistants cite these threads and questions in the [category] market: [paste them]. The person who would participate: [named person] from [brand]. A finding we can publish: [paste one number or result]. Task: 1) for each thread, judge whether [named person] can add real value as a named participant, yes or no with one reason; 2) for each yes, draft a two-sentence opening that answers the actual question before any product is mentioned; 3) turn the finding into two digital PR angles. Rules: each PR angle is a single plain sentence a journalist could quote with [brand] named in it; never draft anything that opens with a product pitch. Output: the thread judgements, the openings, then the two angles.

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• Days 29 to 30 Re-run the baseline and set the rhythm. Repeat the week 1 measurement, report only the questions that never contained your name, and put the deltas beside the before screenshots. Then set the weekly rhythm: one fan-out review, one content refresh, one outreach ask, every week. The plan ends; the operating cadence does not.

  Role: you are an analyst writing the honest day 30 report. Context: day 1 and day 30 AI visibility baselines for [brand] are pasted below. Stable citations typically take 8 to 16 weeks or longer, so day 30 measures direction, not victory. Task: report 1) share of voice change per platform, counting only the questions that never contained our name; 2) which question groups moved; 3) which competitors gained; 4) what did not move at all; 5) the three highest-leverage actions for the next 30 days. Rules: flat numbers get reported flat, no spin; tie every claim to a number in the data; where the data cannot support a conclusion, say so instead of concluding. Output: five numbered sections, under 500 words. Data: [paste both tracking sheets].

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You end day 30 with a measured delta, a working rhythm, and the honest expectation set below.

Week 4 in full: timings, worked examples and what goes wrong