The essential GEO KPI checklist for marketing leaders

How to audit for AI visibility, mentions, and brand citations

In our recent study, the B2B CMO Pulse 2025, 46% of CMOs cited unclear KPIs and measurement gaps as their biggest challenge in adapting from SEO to Generative Engine Optimization (GEO). The second-most named challenge — “lack of clarity” — underscores a growing truth: you can’t manage what you can’t measure.

The problem isn’t that marketers don’t believe in GEO. It’s that the traditional KPIs and metrics of what success looks like have changed.

If marketing budgets go where attribution is clearest, then GEO measurement is the next frontier of growth clarity.

Since marketing budgets gravitate toward tactics with clear attribution, GEO measurement represents the next significant opportunity for enhanced growth clarity.

Below is a GEO KPI checklist — a practical framework your team can use to start quantifying visibility, trust, and performance in the AI search era.

1. Start with relevant prompts

Before you can measure visibility, you need to understand which questions your audience is asking.
✅ Generate a list of 30–50 prompts relevant to your business or category (e.g., “best enterprise CRM tools for B2B companies”).
✅ Include brand, competitor, and category prompts.
✅ Use these prompts consistently when testing visibility in AI engines like:

• ChatGPT
• Google’s AI Overviews
• Perplexity
• Grok
• Claude
• Gemini
• DeepSeek

Goal: Establish a baseline for how your brand (and competitors) appear in generative results.

2. Track your AI ranking positions

For each prompt, note how — and where — your brand appears in the AI-generated summary.
✅ Is your brand cited by name?
✅ Is your site linked or referenced?
✅ Are you summarized neutrally or favorably?

Metric: Average AI position (ranking in summary hierarchy).
Goal: Improve your average position and ensure accuracy in how your brand is represented.

3. Measure “Share of Voice” vs. your competition

Just as traditional SEO tracks keyword share, GEO tracks answer share.
✅ Measure how often your brand appears in AI responses compared to competitors.
✅ Assess context quality — not just quantity.
✅ Track sentiment (positive, neutral, or negative summaries).

Metric: % of prompts where your brand appears vs. total prompts analyzed.
Goal: Grow your “share of answer” — the new share of voice.

4. Track Share of Voice over time

Visibility in AI results fluctuates as models evolve.
✅ Measure changes in share of voice weekly or monthly.
✅ Identify when visibility spikes or drops — and correlate it to new content releases or technical changes.

Goal: Identify leading indicators of performance — not just lagging ones.

5. Identify brand strengths & weaknesses

Conduct a qualitative audit of AI summaries to reveal what machines think your brand stands for.
✅ Review AI-generated summaries for accuracy and tone.
✅ Compare against internal positioning and messaging.
✅ Highlight discrepancies or missed strengths.

Goal: Align machine perception with brand reality.

6. Find opportunities to improve AI visibility

Create, test, and track performance of AI content optimization tactics.

✅ Identify content gaps where your brand doesn’t appear in summaries.
✅ Strengthen structured data and author signals around underperforming topics.
✅ Create “answer-first” content that addresses AI prompts directly.

Goal: Improve entity clarity and increase inclusion in AI-generated results.

7. Create a GEO dashboard

Bring it all together with a measurement framework that teams and strategies can align around.
✅ Centralize prompt rankings, share of voice, and sentiment tracking in one view.
✅ Add internal KPIs — like traffic from AI citations or branded mention velocity.
✅ Tie these metrics to content or campaign initiatives for ROI visibility.

Goal: Make GEO performance measurable, repeatable, and reportable.

The takeaway

Measuring GEO isn’t about replacing SEO metrics — it’s about expanding what visibility means in an AI-driven world. Brands that master this early will own the first generation of AI trust signals: visibility, accuracy, and authority.

In short: what gets measured gets cited.