Monthly AI Visibility Report Template (With Looker Studio Dashboard)

A monthly AI visibility report shows, on one page, whether your brand is appearing more or less in AI answers, where, why, and what you're doing about it. It has six sections: headline rates, results by engine, results by topic, top cited sources, accuracy issues, and AI traffic and conversions, plus a method box. Build it in Looker Studio from your scan sheet and GA4 so it updates when new data lands.

On this page

• What are the six sections?
• How should the scan sheet be structured?
• From MentionX data: what the numbers in each section look like
• How do you build it in Looker Studio?
• How do you write the "what changed" box?
• How do you explain a drop?
• Worked example: Ledgerline's October report
• Which metrics should leadership see, and which shouldn't they?
• What does a good report avoid?
• How do you present a report nobody asked for?
• Template checklist
• How long does it take each month?
• What does an example report look like?
• What does the monthly workflow look like?
• How do you set targets?
• What belongs in the method box?
• How do you adapt the template for different audiences?
• How do you connect the data sources?
• How do you track AI traffic for section 6?
• Common questions
• Sources

Product documentation checked on 30 October 2026. "Our view" marks MentionX opinion. Ledgerline is fictional.

What are the six sections?

Plus a method box: prompt set version, prompt count, engines and modes, runs per prompt, scan dates, competitor set, formula version.

How should the scan sheet be structured?

Looker Studio works best with one tidy table. One row per prompt × engine × run × brand:

Keep a separate tab for cited URLs (one row per URL per answer) and one for accuracy issues (issue, source, owner, status, date fixed).

From MentionX data: what the numbers in each section look like

Method. Read-only aggregate queries over MentionX scans from 22 August to 29 October 2026 (UTC): 2,217 engine runs (2,154 completed, 63 failed) across 54 scans, 18 anonymised projects and six engines, via APIs. Citation counts cover 9,750 cited URLs.

These figures show why each report section needs its own denominator and notes:

Limits. 18 projects is a small, non-random sample of B2B software brands. API answers can differ from consumer apps. URL page types come from rule-based URL patterns, so some pages are unclassified. Perplexity, Copilot and DeepSeek figures come from a single day (26 October), so treat them as a snapshot.

Our view: the source table is the most useful section for the people doing the work, and the one most often wrong because of uncleaned URLs.

How do you build it in Looker Studio?

1. Create a report and add two data sources: the Google Sheet (scan tab, URLs tab, issues tab) and your GA4 property.
2. Add calculated fields on the scan source:

- Mention rate = SUM(mentioned) / COUNT(prompt_id) filtered to your brand - Recommendation rate and Citation rate the same way

1. Page 1, leadership view (8 elements):

- Scorecards: mention rate, recommendation rate, citation rate, SOV, each with comparison to previous period - Line chart: mention rate by month, your brand vs competitors - Bar chart: mention rate by engine - Table: top 10 cited domains with a "mentions us" column - Scorecard + table from GA4: AI assistant sessions and key events; top landing pages

1. Page 2, team view: topic breakdown, full domain list, issue tracker, raw answer links.
2. Add a text box for the method and another for "What changed and why".

How do you write the "what changed" box?

Three bullets, every month:

• What moved: "Mention rate up 6 points on Perplexity; flat elsewhere."
• Likely why: "Our comparison page was cited in 9 of 30 runs after the rewrite on 12 Oct."
• What's next: "Apply the same rewrite to the pricing guide; pitch the two review sites that still omit us."

Use "likely" for causes; with variable answers you rarely know for sure.

How do you explain a drop?

Before reporting a drop as a problem, check:

1. Did the method change? New prompts, new engine mode, fewer runs.
2. Did an engine change? A model or product update can shift answers for everyone. Check whether competitors dropped too.
3. Is it within normal variation? Compare with the spread you saw in earlier months.
4. Is it concentrated? One topic or engine usually explains most of it.

Then report it plainly with the likely cause and the action.

Worked example: Ledgerline's October report

Ledgerline is a fictional accounts-payable tool. Figures are illustrative. The scan covered 40 unbranded prompts, four engines and three runs: 480 runs, of which 471 completed.

The CMO read the headline and "Next" rows only, which is fine: those two rows should stand on their own.

Which metrics should leadership see, and which shouldn't they?

What does a good report avoid?

• A single score with no components.
• Screenshots of individual answers as evidence.
• Unlabelled estimates. If a number is projected, say so.
• Mixing branded prompts into headline rates.
• Changing the competitor set without noting it.

How do you present a report nobody asked for?

If leadership hasn't requested AI visibility reporting yet, start small. Send the one-page view for three months before asking for anything. Lead with a fact they can check, such as how an engine describes your product today compared with how you'd describe it, rather than with a rate. Then show the actions taken and what moved. A short, steady record of cause and effect earns more attention than a large dashboard launched all at once.

Keep the tone plain. Say "likely" when you mean likely, give the sample size, and name what you don't know, such as AI-driven visits that arrive without a referrer. Readers trust a report that admits its limits.

Template checklist

• Scan sheet in tidy format, one row per prompt × engine × run × brand
• GA4 custom channel group for AI assistants
• Looker Studio report with two pages
• Scorecards with period comparison
• Engine and topic breakdowns
• Source table with presence flag
• Accuracy tracker
• Method box
• "What changed" box filled in each month
• Shared with view access for stakeholders

How long does it take each month?

Once the template is built, a monthly update is mostly running the scans (manual or automated) and writing the three-bullet summary. Teams that track manually spend most of their time on the scans themselves; see manual vs automated tracking.

What does an example report look like?

Illustrative example, not real data. A one-page monthly report for a mid-size software brand might read:

Notice that every line connects a number to a cause or an action. That's the habit to build.

What does the monthly workflow look like?

A fixed calendar keeps the report on time and the method stable:

Keep the prompt set version fixed for the quarter. If you must add prompts mid-quarter, report them in a separate row until the next quarter starts.

How do you set targets?

Targets make the report useful, but AI answers move for reasons outside your control. Set them on things you can influence and measure clearly:

• Rate targets by topic, not overall. "Mention rate on invoice-approval prompts from 30% to 40% by March" is clearer than a company-wide number.
• Source targets. "Listed with correct details on four of the top six cited review pages" is fully in your hands.
• Accuracy targets. "No open pricing errors older than 30 days."
• Traffic targets only once you have three months of identified AI sessions to compare against.

Our view: avoid targets on share of voice alone. It can rise because a competitor slipped, which isn't something your team did.

What belongs in the method box?

Five lines are enough: dates (with time zone), engines and modes, prompt set version and count, runs per prompt, and completed versus failed runs. Add the competitor set and the scoring weights if you report share of voice. If anything changed since last month, say so in the box, not in a footnote.

How do you adapt the template for different audiences?

• Executives: headline, trend chart and two actions. Nothing else.
• Content team: cited URLs, uncited competitor pages and topic gaps.
• Product marketing: accuracy errors, positioning language the engines repeat, and competitor framing.
• Agencies' clients: the same six sections plus a short "work completed" log, so effort and outcome are visible side by side.

Keep one underlying scan sheet and build views on top of it; don't maintain separate data for each audience.

How do you connect the data sources?

Looker Studio connects to Google Sheets and GA4 through Google's own connectors (Sheets, GA4). A few setup choices save time later:

• Use header rows and consistent types. Dates as dates, and mentioned and recommended as 0/1 numbers, so sums and averages work.
• Keep one tab per grain. The scan tab is one row per run and brand; the URL tab is one row per cited URL. Mixing grains doubles counts.
• Clean URLs in the sheet, not the chart. Add a column with the resolved domain, without tracking parameters. Our data shows why: most Gemini links are redirects, and almost all OpenAI links carry a UTM tag.
• Blend only on shared keys. Join scan data and GA4 on month or landing page, never on session.

How do you track AI traffic for section 6?

Set up a custom channel group in GA4 that matches AI assistant referrers (Google's guide). ChatGPT adds utm_source=openai to many links, which shows up in GA4's source dimension (UTM parameters). Plenty of AI-driven visits arrive with no referrer at all, so label the section "identified AI traffic" rather than "all AI traffic".

Common questions

Can I build it in Looker Studio for free? Yes. Looker Studio and the Sheets and GA4 connectors cost nothing to use.

How long should the report be? One page or one dashboard screen for leadership, with a second page of detail for the team.

Should we show a single AI visibility score? Only with its parts next to it. A score alone hides which engine or topic moved.

Who should own the report? One person, usually the SEO or content lead, with the analyst preparing data. Shared ownership tends to mean late reports.

Monthly or weekly? Monthly for leadership. Weekly runs help the team spot problems, but week-to-week moves are mostly noise.

Sources

• Google Analytics Help, Custom channel groups; URL builders and UTM parameters
• Looker Studio, Connect to Google Sheets; Connect to Google Analytics
• NIST/SEMATECH, Confidence intervals for a proportion
• Google Search Central, AI features and your website, on how AI feature traffic appears in Search Console