I’m an analytics person trying to measure something that doesn’t fit traditional frameworks.
What I have for SEO:
What I don’t have for AI:
Google Analytics shows some referrals from chat.openai.com and perplexity.ai, but that’s maybe 200 visits/month. That can’t be the whole story.
My questions:
Feeling like I’m missing a major channel because I don’t know how to measure it.
You’re right that traditional metrics don’t capture AI impact. Here’s the measurement framework I use:
AI Visibility Metrics (Primary):
| Metric | What It Measures | Why It Matters |
|---|---|---|
| Citation Frequency | How often you’re mentioned | Volume of AI visibility |
| Citation Position | First mention vs later | Quality/prominence of visibility |
| Share of Voice | Your mentions / total mentions | Market position in AI |
| Citation Quality | Context and sentiment | How positively you’re portrayed |
| Coverage Rate | % of relevant queries where you appear | Breadth of visibility |
Downstream Impact Metrics (Secondary):
| Metric | What It Measures | Why It Matters |
|---|---|---|
| Branded Search Lift | Correlation between citations and branded searches | Awareness impact |
| AI Referral Traffic | Direct visits from AI platforms | Immediate traffic value |
| Survey Attribution | % of customers who researched via AI | Decision influence |
| Competitive Displacement | Share of voice change vs competitors | Market dynamics |
The key insight: AI visibility is primarily an awareness metric. It influences decisions upstream of your website. Traditional metrics capture what happens after someone decides to visit you - AI metrics capture what influences that decision.
On the attribution problem - here’s how we’ve approached it:
The challenge: User asks AI, then might:
None of these clearly attribute back to AI.
Our solution: Multi-signal approach
Branded search correlation
Customer surveys
AI referral tracking
Not perfect attribution, but directionally accurate.
On tools - here’s what’s available:
Dedicated AI Visibility Tools:
Am I Cited
Other options:
Manual approach:
What I’d recommend: Start with manual tracking for 30 days to understand what to measure. Then invest in tooling once you know what metrics matter for your business.
Am I Cited is our primary tool - the automated tracking and alerting saves hours weekly.
This framework is helpful. Let me translate to a dashboard structure:
Primary Dashboard: AI Visibility
Secondary Dashboard: Impact
Reporting frequency:
Question: What’s a “good” citation frequency? Any benchmarks for what we should aim for?
Benchmarks vary significantly by industry and competitive set. Here’s what I’ve observed:
Citation Frequency Benchmarks (% of relevant queries):
| Category | Low | Average | High |
|---|---|---|---|
| Market Leader | 30% | 50% | 70%+ |
| Established Player | 15% | 30% | 45% |
| Challenger/Growing | 5% | 15% | 25% |
| New Entrant | <5% | 5-10% | 15% |
Position Benchmarks:
Share of Voice:
The real benchmark: Your competitors. If they’re at 40% and you’re at 10%, that’s your gap to close - regardless of “industry average.”
On reporting to leadership - here’s what resonates:
What executives care about:
Executive dashboard (one page):
Section 1: Market Position
Section 2: Business Impact
Section 3: Key Wins & Gaps
Section 4: Next Quarter Focus
Keep it simple. Executives don’t need citation position distributions - they need “are we winning or losing, and what are we doing about it.”
For the analytically-minded, here’s how to get granular:
Query-level analysis:
Citation quality scoring:
Temporal analysis:
Content-level attribution:
Am I Cited provides much of this granular data. The query-level and content-level views have been most actionable for us.
Perfect. Here’s my measurement implementation plan:
Week 1: Manual Baseline
Week 2: Tool Implementation
Week 3-4: Dashboard Build
Ongoing:
Metrics I’ll track:
This gives me the foundation to actually answer “how are we performing in AI search” instead of guessing.
Quick practical tips:
Start simple: Don’t build a 50-metric dashboard. Start with:
Expand once you prove value.
Connect to existing processes:
Don’t over-attribute:
Celebrate wins: When citations improve, share it. Leadership engagement grows when they see progress.
Worth noting: measurement will evolve.
What’s coming:
What to do now:
The brands that start measuring now will have the best historical data when better attribution tools emerge.
AI search measurement is where web analytics was in 2005. It’ll get better, but early data is still valuable.
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Get the data you need to measure AI search performance. Citation frequency, share of voice, position distribution, and competitive analysis.
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