Primary KPIs (Highest Value)
These are the North Star metrics that are consistently tracked and valued most:
1. Brand Presence
What it measures: Percentage of relevant AI-generated answers where your brand is mentioned
Why it's valuable:
Most widely tracked metric across all use cases
Easy to understand and explain to stakeholders
Establishes a clear trend line over time
Core indicator of overall AI visibility
Best practices:
Pull regularly to establish baseline and track progress
Use as the foundation for quarterly reviews
Compare against previous periods to show momentum
2. Citations & Citation Share
What it measures:
Total number of brand citations in AI-generated answers
Citation consistency across topics, prompts, stages, or personas
Competitive citation share (your brand vs. competitors)
Why it's valuable:
Tangible proof of brand mentions
Direct indicator of "share of voice" in AI search
Competitive intelligence (e.g., "We're cited 49% vs. Competitor at 83%")
Often reported as the #1 tracking metric
Best practices:
Break down by topic or customer journey stage
Identify third-party sites mentioning competitors but not you = outreach opportunities
Track citation sources to identify high-value domains
Insight: "Citations are the most important KPI to track when brand presence might be skewed by prompt selection or geography."
3. AI Referral Traffic
What it measures: Website sessions driven by specific AI models (ChatGPT, Perplexity, Gemini, etc.) via Google Analytics integration
Why it's valuable:
Direct business impact metric
Connects AI visibility to actual website traffic
Easier for executives to understand than abstract visibility metrics
Demonstrates ROI of AI search optimization efforts
Best practices:
Track by individual AI platform to identify which are driving most value
Monitor conversion rates from AI traffic vs. other channels
Use as proof point for expanding AI search investment
Secondary KPIs (Still Important)
These metrics provide additional context and competitive intelligence:
4. Competitive Presence
What it measures: Brand performance compared to key competitors across the same prompts
Why it's valuable:
Identifies gaps and opportunities
Shows relative market position in AI search
Helps prioritize optimization efforts
Best practices:
Focus on 2-4 key competitors rather than tracking too many
Calculate competitive substitution rate
Filter prompts by competitor to find specific gaps
5. Position Share
What it measures: Distribution of brand mentions by position (top, middle, bottom) within AI responses
Why it's valuable:
Similar to traditional SEO rankings
Indicates prominence within responses
Useful for tracking improvement over time
Important context:
Monitor but don't over-index on this metric
Similar to SEO: drop-off is high after "page 2"
Question whether users read entire lengthy LLM responses
Watch for unusual trends rather than focusing heavily on absolute position
6. Sentiment
What it measures: Breakdown of positive, mixed, and negative brand mentions
Why it's valuable:
Quality of mentions, not just quantity
Early warning system for negative narratives
Helps identify messaging opportunities
Important context:
Mixed sentiment is typically fine (pro/con lists, comparisons)
Only investigate if negative sentiment becomes significant
Use Insights tab to flag concerning trends
7. Agent/Bot Traffic
What it measures: Total visits from AI agents/bots to your properties
Why it's valuable:
Leading indicator of content being consumed by AI
Shows which pages are being crawled most frequently
Helps prioritize optimization efforts
Best practices:
Track crawl frequency by page
Monitor for drops that might indicate technical issues
Use to identify high-value content for optimization