Key takeaways
- Looker Studio is free and works well for AI visibility reporting, but you need a data source that exports the right metrics (coverage %, share of voice, citation counts, mention rank)
- Several AI visibility tools now offer native Looker Studio connectors or APIs -- the fastest path to a working dashboard is picking one that does
- The most useful dashboards combine AI visibility data with web analytics (Google Analytics or GSC) so you can connect AI citations to actual traffic
- If you want to go beyond reporting and actually fix your visibility gaps, you need a platform that does more than export data
AI search is generating real traffic now. ChatGPT, Perplexity, Google AI Overviews, and Gemini are sending visitors to websites every day, and brands are starting to ask the same question: how do we track this properly?
The problem is that most AI visibility tools give you data inside their own dashboards. That's fine for day-to-day monitoring, but it doesn't work for stakeholder reporting. Executives want a monthly trend line. Clients want a branded dashboard. Marketing leads want a weekly snapshot they can glance at in 30 seconds. None of those people are going to log into yet another SaaS tool.
That's where Looker Studio comes in. It's free, it's flexible, and most people already use it for other marketing reporting. This guide walks through exactly how to build an AI visibility report in Looker Studio -- from connecting your data source to designing a dashboard that's actually useful.
What you'll need before you start
Before touching Looker Studio, you need to sort out three things:
A Google account. Looker Studio is a Google product, so you'll need a Google account to access it at lookerstudio.google.com. If your team uses Google Workspace, that works fine.
An AI visibility data source. This is the most important decision. Looker Studio can't query ChatGPT or Perplexity directly -- it needs a tool that tracks AI visibility and can push that data into Looker Studio via a connector or API. More on this below.
A clear sense of what you're reporting on. Are you tracking brand mentions across AI engines? Citation share vs. competitors? Visibility by prompt topic? Decide this before you start building, or you'll end up with a cluttered dashboard that nobody reads.
Choosing your AI visibility data source
This is where most guides skip over the hard part. Looker Studio is just a visualization layer -- the quality of your dashboard depends entirely on what data you're feeding into it.
Here's a quick comparison of the main options:
| Tool | Looker Studio integration | Key metrics available | Best for |
|---|---|---|---|
| Otterly.AI | Native connector + prebuilt template | Coverage %, mentions, share of voice, avg rank | Fast setup, agency reporting |
| Peec AI | Community connector | Brand coverage, domain ranking | Teams wanting documented field schemas |
| Profound | REST API + SDKs | Custom fields, deep citation data | BI pipelines, data engineering teams |
| Promptwatch | API + Looker Studio integration | Visibility scores, citations, traffic attribution | Full-funnel reporting with traffic data |
| Promptmonitor | CSV export | Basic visibility scoring | Budget-conscious monitoring |
Promptwatch is worth calling out here because it's one of the few platforms that connects AI visibility to actual traffic. Most tools tell you how often you're cited -- Promptwatch can tell you whether those citations are driving sessions. That matters if you're trying to justify the work to a CFO.
For the rest of this guide, the steps apply regardless of which tool you use. The connector setup varies, but the Looker Studio workflow is the same.
Step 1: Connect your data source
Open Looker Studio and click "Create" > "Report." You'll be prompted to add a data source.
If your tool has a native Looker Studio connector (like Otterly.AI), you'll find it by searching the connector gallery. Click "Connect," authenticate with your account, and select which workspace or brand report you want to pull from.
Otterly.AI
If your tool uses a community connector (like Peec AI), the process is similar -- search for the connector by name, authorize it, and map your fields.
If you're working with an API (like Profound or Promptwatch), you have two options:
- Use Google Sheets as a middleman: pull data from the API into a Google Sheet via a script or automation tool like Zapier, then connect that Sheet to Looker Studio
- Use BigQuery: push data from the API into BigQuery, then connect BigQuery to Looker Studio for more scalable, automated reporting
The Google Sheets route is faster to set up. BigQuery is better if you're reporting on large datasets or want automated daily refreshes without manual intervention.
Connecting via Google Sheets (the quick path)
- Set up a Google Sheet with columns matching your AI visibility metrics (date, prompt, AI engine, coverage %, mention rank, citation URL, competitor share, etc.)
- Use your tool's API or export function to populate this sheet -- either manually or via an automation
- In Looker Studio, add a data source and select "Google Sheets"
- Point it at your sheet and select the correct tab
- Looker Studio will auto-detect your column types -- review these and correct any that are wrong (dates especially)
Step 2: Set up your date dimension
Every useful dashboard needs a working date filter. Without it, you can't show trends over time, and trends are the whole point.
Make sure your data source has a date column formatted as YYYY-MM-DD. Looker Studio is picky about date formats -- if it doesn't recognize your dates, it'll treat them as plain text and your time-series charts won't work.
Once connected, add a date range control to your report. Go to "Add a control" > "Date range control." This lets anyone viewing the report filter the data to any time window without editing the report itself.
Step 3: Build your core metrics
A good AI visibility dashboard doesn't need to be complicated. Here are the metrics that actually matter, and how to visualize them:
Overall visibility score / coverage rate
This is the percentage of tracked prompts where your brand appears in the AI response. It's your headline number.
Use a "Scorecard" widget. Set the metric to your coverage percentage field. Add a comparison date range (e.g., previous period) so viewers can see if it's going up or down.
Visibility trend over time
Use a "Time series" chart with date on the X axis and coverage % on the Y axis. If your data includes multiple AI engines, add a dimension breakdown so you can see separate lines for ChatGPT, Perplexity, Google AI Overviews, etc.
Share of voice vs. competitors
This is the metric that gets executives' attention. If you're tracking competitor visibility alongside your own, a "Bar chart" or "Stacked bar chart" works well here -- one bar per brand, showing their share of total AI mentions across your tracked prompts.
Top cited pages
A simple "Table" widget showing your URLs alongside citation counts tells you which content is actually getting picked up by AI engines. Sort by citation count descending. This is also useful for identifying pages that used to get cited but have dropped off.
Visibility by AI engine
Different AI models behave differently. You might be strong on Perplexity but weak on Google AI Overviews. A "Pie chart" or "Bar chart" broken down by AI engine shows this at a glance.
Prompt-level breakdown
A table showing individual prompts, your visibility for each, and competitor visibility helps teams prioritize where to focus. Sort by prompts where competitors are visible but you're not -- those are your gaps.
Step 4: Add web analytics data (optional but valuable)
This is where most AI visibility dashboards stop short. Showing that you're cited 40% of the time is interesting. Showing that those citations drove 3,200 sessions last month is convincing.
If your AI visibility tool supports traffic attribution (Promptwatch does this via a code snippet, GSC integration, or server log analysis), you can pull that data into the same Looker Studio report and add a "Sessions from AI referrals" scorecard alongside your visibility metrics.
Even without a dedicated integration, you can pull AI referral traffic from Google Analytics 4 by filtering for sessions where the source/medium includes known AI referrers (perplexity.ai, chatgpt.com, etc.).
To add GA4 as a second data source:
- In your Looker Studio report, go to "Resource" > "Manage added data sources"
- Add Google Analytics and select your GA4 property
- Create a new page or section in your report for traffic data
- Use a "Scorecard" for total AI referral sessions and a "Time series" for trend
You can blend the two data sources (AI visibility + GA4) if you have a common dimension like date, though blending in Looker Studio has some quirks -- test it before relying on it for client reports.
Step 5: Design for the audience
The biggest mistake people make with Looker Studio dashboards is designing for themselves instead of for the person who'll read it.
A few principles that actually work:
Put the headline number first. Your overall visibility score or share of voice should be the first thing someone sees. Don't make them scroll to find it.
Use consistent colors. Pick one color for your brand and stick to it across all charts. Use a different color for competitors. This makes comparisons instantly readable.
Add text boxes to explain context. A chart showing a visibility drop is confusing without context. A one-line text box saying "Visibility dipped in March following a Google AI Overviews update" turns a confusing chart into a useful one.
Separate pages by audience. If you're reporting to both an exec team and an SEO team, consider separate pages. Execs want three numbers. SEO teams want the prompt-level breakdown.
Use the prebuilt template if one exists. Otterly.AI ships a prebuilt Looker Studio template when you connect their connector. It's not perfect for everyone, but it's a much faster starting point than building from scratch.
Step 6: Automate the data refresh
A dashboard that requires manual updates every week isn't a dashboard -- it's a spreadsheet with extra steps.
If you're using a native connector (Otterly.AI, Peec AI), data refreshes automatically on whatever schedule the connector supports. Check the connector documentation for refresh frequency.
If you're using Google Sheets as a middleman, set up an automation to update the sheet on a schedule. Options include:
- Google Apps Script with a time-based trigger
- Zapier connecting your AI visibility tool's API to Google Sheets
- n8n for more complex workflows with multiple data sources
If you're using BigQuery, schedule your API-to-BigQuery pipeline to run nightly. Looker Studio will always query the latest data.
Common mistakes to avoid
Tracking too many prompts. More prompts doesn't mean better data -- it means noisier data. Start with 20-50 prompts that represent your most important use cases and expand from there.
Ignoring competitor data. Your visibility score means nothing in isolation. A 35% coverage rate looks great until you see a competitor at 70%. Always include at least one competitor in your tracked prompts.
Not filtering by AI engine. Aggregating across all AI engines hides important differences. A brand might be invisible on ChatGPT but dominant on Perplexity. Keep these separate in your visualizations.
Forgetting mobile viewers. Looker Studio reports can be viewed on mobile. Check how your dashboard looks on a phone before sharing it with clients or executives.
Treating the dashboard as the end goal. A dashboard that shows you're losing visibility is only useful if it leads to action. The reporting is the start, not the finish.
Which tool to use for the full workflow
If you want to go beyond just reporting and actually improve your AI visibility, you need a platform that closes the loop -- find gaps, create content, track results.
Promptwatch is built around this workflow. It shows you which prompts competitors rank for that you don't, has a built-in AI writing agent to create content targeting those gaps, and then tracks whether that content gets cited. The Looker Studio integration means you can pipe all of that into the dashboard you've just built.
For teams that just need monitoring and reporting, Otterly.AI's native Looker Studio connector is the fastest path to a working dashboard.
Otterly.AI
For enterprise teams with existing BI infrastructure, Profound's REST API gives you the most flexibility to build exactly what you need.
Profound
Putting it together
Building an AI visibility report in Looker Studio isn't technically difficult. The hard part is choosing the right data source, deciding what metrics actually matter for your audience, and setting up automation so the dashboard stays current without manual effort.
Start simple: one data source, five to six core metrics, a date filter, and a competitor comparison. Get that working and shared with stakeholders before adding complexity. A clean, reliable dashboard that people actually read is worth more than an elaborate one that nobody trusts.
Once you have the reporting in place, the next question is what to do with the data -- and that's where the real work begins.