From SEO to AEO: The New Metrics That Matter

Whether you think AI search is just SEO rebranded or something entirely new, one thing is clear: the performance metrics have changed.

Traditional SEO metrics—keyword rankings, organic traffic, click-through rate—developed for a world where search engines provide links and users click through to websites. AI search alters this dynamic.

According to Scrunch research, the core difference is simple: “AI bots perform the discovery work for users, whereas traditional search requires users to evaluate multiple results.”

This shift demands new approaches to measurement.

The Fundamental Shift: From Traffic to Visibility

In traditional SEO, success means driving traffic. Rank higher, get more clicks, generate more conversions.

In AI search, users often get what they need without clicking anywhere. Zero-click results—where AI provides complete answers directly—are the default, not the exception. Research suggests over 60% of Google searches already end without a click, and AI search amplifies this trend.

Success now depends on “being findable by the AI that finds everyone else.”

Your brand appearing in AI recommendations—even without a click—shapes perception and consideration. Visibility becomes the outcome, not just a step toward traffic.

The Four Core Metrics of AI Search

According to Scrunch, four metrics matter most for understanding AI search performance.

1. Brand Presence

Definition: How frequently your brand appears in AI-generated answers across tracked prompts.

Key measurements:

Share of voice (%) across relevant prompts
Brand placement within results (mentioned first, middle, or last)
Consistency of appearance across runs

Critical questions:

Do I show up in answers that matter for my category?
How does my visibility compare to competitors?
Which prompts generate the most mentions?

Visibility likelihood: High. Brand mentions appear directly in the AI’s response narrative, visible to every user who asks.

This is your primary indicator of whether AI considers you relevant. A brand never mentioned is a brand never considered.

2. Citations

Definition: External sources that AI models reference when generating answers.

Key measurements:

Citation share across your target prompts
Citation count specifically from your owned pages
Which content types earn citations

Critical questions:

Is my content being cited as a source by AI models?
Which domains dominate citations in my industry?
What can I create to earn more citations?

Visibility likelihood: Medium. Citations typically appear below the main answer, less prominent than direct mentions. But citations indicate trust—AI considers your content authoritative enough to reference.

Citations also drive traffic. Unlike mentions, citations include links users can follow.

3. LLM Referral Traffic

Definition: Human website visits originating from AI platforms like ChatGPT, Perplexity, or Bing Copilot.

Key measurements:

Session count from AI domains
Conversion rate from AI referrals
Engagement metrics (time on site, pages per session)

Critical questions:

How much traffic actually comes from AI search?
Which prompts drive the most visits?
Do AI visitors convert better than organic traffic?

Visibility likelihood: Low volume but potentially high intent. Users clicking through from AI responses have been pre-qualified by the model—they saw your recommendation and chose to learn more.

Volume will typically be lower than traditional search due to AI’s lower click-through rates. But quality may be higher.

4. Agent Traffic

Definition: Visits from AI retrieval bots crawling your site to index content for future responses.

Key measurements:

Bot visit frequency from AI crawlers
Bot diversity (which AI systems are crawling)
Most-crawled pages

Critical questions:

How often is my site being considered for AI responses?
Which AI models are accessing my content?
Is my site technically optimized for AI crawling?

Visibility likelihood: Forward-looking. Agent traffic doesn’t directly indicate current visibility—it indicates potential future visibility. Sites frequently crawled by AI bots are being evaluated for inclusion in responses.

Old Metrics vs. New Metrics

The translation from SEO to AEO metrics:

Traditional SEO	AI Search Equivalent	Key Difference
Keyword rankings	Brand mentions	No positions, just present/absent
Organic traffic	LLM referral traffic	Lower volume, higher intent
Click-through rate	Citation rate	Clicks come from citations, not rankings
Backlinks	Third-party mentions	Source authority matters for AI inclusion
Domain authority	Brand presence score	Authority shown through AI recommendations
SERP features	AI overview inclusion	Visibility in AI-generated summaries

The metrics aren’t direct translations—they represent different success models.

Implementing New Measurement

Transitioning measurement from SEO to AEO requires new tools and approaches.

What Your Analytics Can’t Tell You

Standard analytics tools weren’t built for AI search. Google Analytics and similar platforms can tell you:

Traffic from AI referral sources (with proper configuration)
Conversion performance of AI traffic
Engagement metrics for AI visitors

They cannot tell you:

How often AI mentions your brand
Your share of voice in AI responses
Which queries produce mentions
Sentiment of AI descriptions
Competitive positioning in AI recommendations

For those metrics, you need AI-specific monitoring.

Setting Up AI Traffic Tracking

Configure your analytics to capture AI referral traffic:

Direct referrals to track:

chatgpt.com
chat.openai.com
perplexity.ai
claude.ai
bing.com (includes Copilot traffic)

Create segments for AI traffic to analyze separately from organic. Compare:

Volume trends over time
Conversion rates vs. other channels
Engagement patterns

Setting Up AI Visibility Monitoring

For brand presence and citation tracking:

Define query set: 50-100 prompts representing your competitive landscape
Select platforms: ChatGPT, Perplexity, Google AI, Claude (at minimum)
Establish cadence: Weekly tracking of priority queries
Track systematically: Document mentions, citations, sentiment, competitors

Manual tracking works for initial assessment. Automated tools enable scale and consistency.

Connecting New Metrics to Business Outcomes

New metrics must ultimately connect to results that matter.

The Attribution Challenge

AI search creates attribution complexity. A user might:

Ask ChatGPT for recommendations
See your brand mentioned
Later search directly for your brand
Convert through that branded search

Standard attribution credits the branded search. The AI influence is invisible.

Correlation Patterns to Track

Look for correlations between AI visibility and business outcomes:

Does brand presence growth correlate with branded search increases?
Do citation rate improvements precede traffic changes?
Does AI share of voice correlate with consideration set inclusion?

Correlation isn’t causation, but patterns suggest influence worth investigating.

Leading vs. Lagging Indicators

Structure your metrics as a funnel:

Leading indicators (predict future performance):

Agent traffic (AI bots crawling your site)
Citation rate increases
Brand presence improvements

Current indicators (show present state):

Share of voice vs. competitors
LLM referral traffic volume
Mention sentiment

Lagging indicators (confirm business impact):

Revenue from AI-attributed traffic
Brand awareness lift
Conversion rate changes

Lead indicators help you optimize before results appear. Lagging indicators confirm whether visibility translates to value.

The Visibility-Traffic Tradeoff

Here’s an uncomfortable truth: AI search may reduce traffic while increasing visibility.

When ChatGPT fully answers a user’s question, they don’t need to visit your website. Your brand was mentioned, your information was used, but no click occurred.

This creates a measurement dilemma. If visibility is high but traffic is low, are you succeeding?

Consider:

Visibility shapes consideration sets even without clicks
Brand mentions build awareness at scale
Users who do click have higher intent
Not all value flows through website visits

Success metrics must account for visibility value, not just traffic value.

Practical Starting Points

If you’re transitioning from SEO-only measurement:

Week 1: Baseline Assessment

Run 20-30 AI queries in your competitive space
Document current brand presence
Note competitor visibility
Identify gaps and opportunities

Week 2: Configure Analytics

Set up AI referral tracking
Create AI traffic segments
Establish reporting baseline

Week 3: Establish Cadence

Define priority query set
Set up weekly tracking process
Create simple reporting dashboard

Ongoing: Build Correlation Data

Track AI metrics alongside business metrics
Look for correlation patterns
Refine what you measure based on insights

The brands ahead in AI search are measuring differently. Start your transition now.

RivalHound provides the AI visibility metrics that traditional analytics can’t. Start your free trial to measure what matters in AI search.