Discussion Reporting Metrics

What metrics should be in an AI visibility report? Building our dashboard

RE
ReportBuilder · Marketing Analytics Manager
· · 152 upvotes · 11 comments
R
ReportBuilder
Marketing Analytics Manager · January 9, 2026

Building our first AI visibility dashboard. Need help on what to include.

Current SEO reporting:

  • Keyword rankings
  • Organic traffic
  • Backlinks
  • Page performance

What I think we need for AI:

  • Brand mentions in AI responses
  • Citation tracking
  • Competitive comparison
  • Platform breakdown

Questions:

  • What metrics actually matter?
  • How do you benchmark AI visibility?
  • What reporting cadence works?
  • How do you present to leadership?
11 comments

11 Comments

AM
AI_Metrics_Expert Expert Head of Analytics · January 9, 2026

I’ve built AI visibility dashboards for multiple enterprises. Here’s the framework.

Core Metrics (Must Have):

MetricDefinitionBenchmark
Brand Visibility Score% of relevant AI responses mentioning you70%+ exceptional
Citation FrequencyHow often your URLs appear as sourcesTrack trend
Share of VoiceYour mentions vs competitors15% top, 25-30% leader
Average Rank PositionPosition in multi-brand responsesLower is better
Cross-Platform CoveragePresence across 4+ platforms2.8x citation likelihood

Supporting Metrics (Should Have):

MetricDefinitionWhy It Matters
Sentiment ScorePositive/negative/neutral contextRecommendation likelihood
Citation DriftMonthly volatility40-60% normal
Content Recency Impact% from recent content65% from past year
Source AnalysisWhich of your pages citedContent strategy

The executive summary:

Your report should answer: “How visible is our brand in AI search?”

DS
Dashboard_Structure · January 9, 2026
Replying to AI_Metrics_Expert

Dashboard layout that works.

Page 1: Executive Summary

  • Overall visibility score (big number)
  • Month-over-month trend
  • Competitive position
  • Key wins/concerns

Page 2: Platform Breakdown

PlatformVisibilityTrendTop Cited Content
ChatGPT42%+5%[Link]
Perplexity38%+8%[Link]
Google AI55%+2%[Link]
Claude28%New[Link]

Page 3: Competitive Analysis

CompetitorTheir SOVOur SOVGap
Competitor A28%22%-6%
Competitor B18%22%+4%
Competitor C15%22%+7%

Page 4: Content Performance

Which pages get cited most Which topics we’re winning Content gaps to address

Page 5: Recommendations

Prioritized action items based on data

QP
Query_Panel_Setup GEO Strategist · January 9, 2026

The foundation of good AI reporting is the query panel.

What’s a query panel:

25-30 tracked prompts that represent your target audience’s questions.

How to select queries:

CategoryExampleWhy Include
Awareness“What is [category]?”Top of funnel
Consideration“Best [category] for [use case]”Comparison stage
Decision“[Your brand] vs [competitor]”Bottom of funnel
Feature-specific“How to [specific task]”Detailed queries

Query selection criteria:

  • High intent for your business
  • Span different journey stages
  • Include branded and non-branded
  • Match how your audience actually asks

Warning:

If your query panel doesn’t match real user behavior, your metrics are meaningless.

How to build:

  1. Analyze sales call transcripts
  2. Review customer support queries
  3. Check search console question queries
  4. Interview sales team
CA
Cadence_Advice · January 8, 2026

Reporting cadence that works.

Weekly (internal team):

  • Quick visibility check
  • Anomaly detection
  • Not for trend analysis

Monthly (stakeholder report):

  • Full metrics review
  • Trend analysis
  • Competitive movement
  • Action item updates

Quarterly (strategic review):

  • Deep-dive analysis
  • Strategy assessment
  • Resource allocation review
  • Goal setting

Why monthly is the baseline:

IssueWhy Not Weekly
AI volatility40-60% variation normal
Overreaction riskChanges might reverse
Resource drainToo much reporting time

The 30-day minimum rule:

First month data is establishing baseline. Don’t panic about fluctuations.

Week 6-8 is when actionable patterns emerge.

PD
Platform_Differences Expert · January 8, 2026

Platform-specific reporting considerations.

ChatGPT reporting focus:

MetricWhy Important
Parametric mentionsWithout web search
Retrieved citationsWith web search
Topic coverageWhat you’re known for

Perplexity reporting focus:

MetricWhy Important
Real-time citationsFreshness signals
Source diversity8,000+ unique domains cited
Reddit correlation46.7% of top sources

Google AI Overviews focus:

MetricWhy Important
Traditional rank correlation93.67% link to top 10
Deep page discovery4.5% from Page 1 directly
YouTube integrationVideo content

Claude focus:

MetricWhy Important
Brave Search backendDifferent index
Constitutional AI preferencesTrustworthy sources

Report should break down by platform, not just aggregate.

AS
Attribution_Section Demand Gen Lead · January 8, 2026

Connecting visibility to business outcomes.

Attribution metrics to include:

MetricSourcePurpose
AI referral trafficGA4Direct attribution
Brand search growthGSCCorrelational
“Discovered via AI” responsesForm fieldSelf-reported
Sales-mentioned AICRMAnecdotal

How to set up GA4 for AI tracking:

Create segments for:

  • perplexity.ai referrals
  • chat.openai.com referrals
  • Other AI platform traffic

The attribution section should answer:

“What business impact is AI visibility having?”

SignalWhat It Suggests
AI traffic growingDirect discovery happening
Brand search correlates with visibilityIndirect discovery working
Sales hears AI mentionsAwareness impact
Form responses cite AIAttribution working

Honest caveat:

Full attribution is impossible. Show what you can measure and correlate the rest.

R
ReportBuilder OP Marketing Analytics Manager · January 7, 2026

This is exactly what I needed. Here’s my report structure.

Monthly AI Visibility Report:

Section 1: Executive Summary

  • Overall visibility score
  • MoM trend
  • Key wins
  • Priority actions

Section 2: Core Metrics

MetricCurrentMoMBenchmark
Visibility ScoreX%+/-70%+
Share of VoiceX%+/-15-25%
Citation FrequencyX+/-Trend
Cross-PlatformX/4+/-4+

Section 3: Platform Breakdown

  • ChatGPT performance
  • Perplexity performance
  • Google AI performance
  • Claude performance

Section 4: Competitive Analysis

  • SOV comparison
  • Topic-level gaps
  • Opportunities

Section 5: Content Performance

  • Most cited pages
  • Top queries we appear in
  • Content gaps

Section 6: Business Attribution

  • AI referral traffic
  • Brand search correlation
  • Form responses

Section 7: Recommendations

  • Prioritized actions
  • Resource requirements

Tool: Using Am I Cited for data collection.

Thanks everyone for the framework!

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Frequently Asked Questions

What metrics should be in an AI visibility report?
Core metrics include Brand Visibility Score (% of relevant responses mentioning you), Citation Frequency (how often your URLs appear), Share of Voice (your mentions vs competitors), Sentiment Analysis (how AI positions you), and Platform-Specific Performance across ChatGPT, Perplexity, Google AI, and Claude.
How often should AI visibility reports be generated?
Monthly reports work for trend analysis, as AI responses can vary 40-60% week-to-week normally. Quarterly deep-dives provide strategic insight. Weekly monitoring catches issues quickly but should not drive panic over normal fluctuations.
What benchmarks should AI visibility reports include?
Key benchmarks: 70%+ visibility score is exceptional, 15% share of voice for top brands, 25-30% for enterprise leaders. Citation drift of 40-60% is normal variation. Cross-platform coverage on 4+ platforms correlates with 2.8x higher citation likelihood.

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