ChatGPT
Claude
Gemini
GrokA brand can be named by one AI model and absent from another. It can appear in a category answer but be described in a way that misses its audience or strengths. That is why “get more mentions” is too shallow a target. The useful goal is to become easier to identify, describe accurately, and recommend for the right buyer questions.
A 30-day GEO plan gives a team enough time to establish a baseline, fix a few real gaps, and check what changed. It cannot force ChatGPT, Gemini, Claude, or Google Search to mention a brand. Their systems differ, and a prompt test is a snapshot. The value of a month is the repeatable learning loop, not a promise of a ranking jump.
Define what a better mention looks like
Before changing pages, agree on what “better” means. Otherwise, one person celebrates any brand appearance while another sees an inaccurate description or a competitor taking the recommendation.
Track quality alongside frequency
Use a compact scorecard for each fixed prompt. Score each dimension from 1 to 5, where 1 means weak or missing and 5 means clear and accurate. Keep notes or answer excerpts beside the score so the team can explain why it moved.
Dimension | What to check | Example signal |
Presence | Does the brand appear on relevant questions? | Included in a useful category answer |
Accuracy | Is the offer and audience described correctly? | Product category and target buyer are right |
Specificity | Does the answer name a real differentiator? | A concrete use case or capability appears |
Recommendation fit | Does the reason to choose the brand match the prompt? | The brand is tied to a relevant buyer need |
Competitive context | Which alternatives appear, and how are they framed? | A competitor owns a topic your brand should address |
Set the baseline before Day 1
A fair comparison needs the same questions and conditions over time. Choose a manageable prompt set that reflects buyer intent, then keep the wording stable for this first month.
Use prompts that represent a buying journey
Include branded questions, category searches, comparisons, and use cases. For example, test “What does [brand] do?”, “What are the best tools for [need]?”, “How does [brand] compare with [competitor]?”, and “How can a team solve [specific problem]?” Replace placeholders with real commercial priorities, not every possible phrase variation.
For each run, record the exact prompt, platform or model, date, country or language when relevant, whether search or browsing was enabled, the answer, citations, and competitors named. Keep separate runs separate. Do not assume one model’s answer represents all users, or that a small prompt set measures total market share.
Check what you can measure
Google’s Search Console now offers generative AI performance reporting for its own Search and Discover experiences, including impressions, pages, countries, devices, and dates. That can complement your baseline, but it is not a report of every answer in ChatGPT or Claude. See Google’s Search Generative AI performance report announcement for its scope.
Bing Webmaster Tools also provides an AI Performance report for citations across Copilot, Bing AI summaries, and select partner experiences. It aggregates citation activity and grounding phrases, rather than revealing every exact user prompt or why a particular page appeared. Read Microsoft’s AI Performance report documentation to understand what the data can and cannot tell you.
The 30-day improvement checklist
Work through these phases in order. Each phase should produce a small decision or change that the next phase can build on. You can adjust the pace to fit your team, but keep the baseline and comparison prompts stable.
Move from diagnosis to a measured retest
Days 1 to 4: Capture the baseline. Run the chosen prompts across the models that matter to your audience. Save answers and citations. Score presence, accuracy, specificity, recommendation fit, and competitive context. Note repeated gaps, not one surprising result.
Days 5 to 8: Audit your most important pages. Review the homepage, product or service pages, about page, use-case pages, and high-intent comparisons. Ask whether a new visitor can quickly understand what the brand does, who it helps, and why its claims are credible.
Days 9 to 14: Tighten the brand explanation. Update confusing or inconsistent descriptions. Use plain words for the category, customer, problem, and outcome. Replace broad claims with concrete workflows, supported proof, examples, or customer evidence that you are allowed to share.
Days 15 to 20: Strengthen the pages behind priority prompts. Improve the page that best answers a recurring buyer question. Add useful detail and internal links to related evidence. Check that the page is accessible to search crawlers. Google says good SEO foundations remain relevant to its generative search features, while OpenAI says publishers should allow OAI-SearchBot if they want content eligible for ChatGPT search summaries and links.
Days 21 to 25: Add supporting evidence. Make sure important claims are consistent across your website and public profiles. Add a useful comparison, customer example, or explanation where a real gap exists. Anthropic distinguishes the crawler used for search from crawlers used for other purposes, so review its current crawler documentation before changing robots rules.
Days 26 to 30: Retest and decide what comes next. Repeat the same prompts under comparable conditions. Compare answers and scores with the baseline. Mark changes as improved, unchanged, or worse, and note where evidence is too thin. Keep the changes that improved clarity; investigate mixed results before expanding the plan.
Make changes that help people first
The easiest mistake is to turn the calendar into a page production quota. More pages do not automatically create stronger model mentions, and repetitive pages can make the site less useful.
Build proof, not a pile of near-duplicates
Google’s guidance for generative AI search emphasizes useful, distinctive content and warns against creating many pages mainly to target query variations. Its broader people-first content guidance asks whether a page serves an intended audience and provides a satisfying answer. Use that as a quality check: each change should make a buyer’s decision easier, even if no AI system ever cites the page.
Crawler access is a practical check, not a shortcut to a mention. OpenAI’s publisher and developer FAQ says content needs to be available to OAI-SearchBot to be included in ChatGPT summaries and snippets. Anthropic’s crawler guidance describes separate crawlers for model development, user-directed retrieval, and search. Check the current instructions with your technical team and avoid broad crawler changes you do not understand.
Use GeoSnake to keep the loop visible
The work becomes easier to manage when prompt results, competitor appearances, and narrative gaps are reviewed together. GeoSnake’s public AI visibility scan describes showing brand mentions, competitors, a visibility score, and possible next actions. Use that information as a working baseline, then pair it with the exact answers and sources you reviewed.
Turn the baseline into the next action
Start with the GeoSnake free business scan to see how AI models describe your brand and which competitors appear. Then explore GeoSnake’s AI visibility features for ongoing monitoring, and read the GeoSnake guide to how marketers track AI visibility weekly for a simple review cadence.
At the end of the month, assign one owner to each meaningful gap and set a date to review it again. Keep the scorecard short enough that the team will actually use it. If your prompt results improve while your page traffic or qualified inquiries do not, report those measures separately rather than claiming that one caused the other.
What success should look like after 30 days
A useful month-end result is not necessarily a dramatic increase in mentions. It may be a clearer product description, fewer repeated inaccuracies, better evidence on a priority page, a crawler issue resolved, or a more reliable baseline for future comparison. Those are meaningful improvements even if the models have not changed their answers yet.
Treat the next month as another learning cycle
Search and AI systems decide what to retrieve and display. Google says even pages that meet its requirements are not guaranteed to be crawled, indexed, or served. Treat a 30-day checklist as an operating rhythm: make a focused change, allow time for systems and users to encounter it, then check again. Do not promise executives that a single page edit will produce a specific mention rate.
If you want a starting point for that measurement cycle, run a free GeoSnake business scan. It can help surface where your brand and competitors appear, so the next 30 days begin with observed questions and gaps rather than guesswork.
FAQs
These are the three questions teams usually ask before they begin.
Can a brand guarantee more AI mentions in 30 days?
No. AI search and model responses depend on systems a company does not control. A 30-day plan can improve the clarity and quality of your public information and create a disciplined way to measure change, but it cannot guarantee a mention or recommendation.
How many prompts should a team track?
Start with a small set covering branded, category, comparison, and use-case intent. The right number is the number your team can repeat consistently and review carefully. Expand only after the first set produces useful decisions.
Does Google Search Console track ChatGPT or Claude mentions?
No. Google’s generative AI performance reporting covers Google Search and Discover features. For other platforms, use their public crawl guidance where relevant and track repeatable prompts directly or through a visibility tool.