Share of Voice in AI: The New Competitive Metric That Matters

Introduction

For decades, SEO professionals measured success with keyword rankings. “We rank #1 for our most valuable keyword” was a statement of victory. But what does #1 mean when 40% of users ask ChatGPT instead of Googling? What does traffic matter when AI synthesizes answers without users clicking through?

Welcome to the era of Share of Voice in AI—the metric that defines competitive visibility in the age of AI-powered search.

Share of Voice (SOV) isn’t new to marketing. It’s long been used to measure advertising presence and brand mentions in traditional media. But its application to AI search creates a fundamentally new competitive dynamic.

This article explains:

• What Share of Voice in AI means and why it matters
• How to calculate it systematically
• How to benchmark against competitors
• What “good” SOV looks like by industry
• How to improve your Share of Voice
• Why this metric is becoming more important than traditional SEO rankings

What is Share of Voice in AI?

Share of Voice (SOV) in AI is the percentage of relevant AI-generated responses that mention your brand compared to the total mentions of all brands in your category.

The Formula

Your Share of Voice = (Your brand mentions / Total category mentions) × 100

Practical Example

You’re a project management software company. You test 100 queries across 5 major LLMs (ChatGPT, Claude, Perplexity, Gemini, Copilot) for a total of 500 test responses.

Results across all 500 responses:

• Asana mentioned: 387 times
• Monday.com mentioned: 352 times
• ClickUp mentioned: 298 times
• Your brand mentioned: 143 times
• Others mentioned: 85 times

Total category mentions: 1,265

Your Share of Voice: (143 / 1,265) × 100 = 11.3%

Interpretation: Your brand captures 11.3% of total mindshare in AI recommendations for project management queries. Asana captures 30.6%, Monday.com captures 27.8%, and ClickUp captures 23.6%.

Why Share of Voice Matters More Than Rankings

Traditional SEO rankings measure position in a list. Share of Voice measures mindshare in a conversation.

The Fundamental Difference

Traditional SEO:

• Finite positions (1-10 organic results)
• Zero-sum competition (if you’re #1, competitor is #2)
• Binary visibility (you either rank or don’t for a specific keyword)
• User sees all options and chooses

AI Share of Voice:

• Variable mentions (LLM might recommend 0, 3, 5, or 7 brands)
• Not strictly zero-sum (LLM can expand recommendations)
• Probabilistic visibility (you might appear 60% of the time)
• User often receives only synthesized recommendations

Why SOV Better Reflects AI Reality

1. AI Responses Aren’t Ranked Lists

ChatGPT doesn’t return “10 blue links” ranked from best to worst. It synthesizes conversational answers that might mention multiple brands in various orders and contexts.

Traditional ranking:

1. Asana
2. Monday.com
3. ClickUp
4. Your brand

AI recommendation:

"For project management, consider Asana for marketing teams needing
strong collaboration features, Monday.com for enterprises requiring
extensive customization, or ClickUp for small teams seeking an
all-in-one solution."

Your brand doesn’t appear. Your “ranking” is irrelevant because you have 0% visibility, while three competitors have roughly equal mindshare.

2. Visibility Varies by Query Context

You might appear frequently for queries about “project management for remote teams” but rarely for “enterprise project management.” Share of Voice can be measured overall or segmented by query type.

3. Multiple Brands Can Win

Unlike traditional search where ranking #1 captures disproportionate clicks, AI often presents 3-5 options relatively equally. Increasing your SOV from 10% to 25% doesn’t require displacing a #1 competitor—you can both appear frequently.

How to Measure Share of Voice in AI

Calculating SOV requires systematic querying and analysis.

Step 1: Define Your Query Universe

Identify 20-50+ queries where you want visibility:

Category queries:

• “best [category]”
• “top [category] providers”
• “leading [category] companies”

Use case queries:

• “[category] for [specific use case]”
• “[category] for [industry]”
• “[category] for [team size]”

Competitive queries:

• “[your brand] vs [competitor]”
• “alternatives to [competitor]”
• “compare [A] and [B]”

Example for CRM:

1. "best CRM software"
2. "top CRM platforms"
3. "CRM for small business"
4. "enterprise CRM solutions"
5. "CRM for sales teams"
...
25. "Salesforce alternatives"

Step 2: Test Systematically

For each query:

1. Test across major LLMs (ChatGPT, Claude, Perplexity, Gemini, Copilot)
2. Use consistent testing protocol:
   • Clean browser session (or API)
   • Same query phrasing
   • Document all brand mentions
3. Run multiple times (different days/times) for statistical validity

What to document:

• Query tested
• LLM platform
• All brands mentioned
• Your brand mentioned? (Yes/No)
• Position if mentioned (1st, 2nd, 3rd, etc.)
• Context and framing

Step 3: Calculate Overall and Segmented SOV

Overall Share of Voice:

• Total your brand mentions across all tests
• Total all brand mentions across all tests
• Calculate percentage

Segmented Share of Voice:

Calculate SOV for different dimensions:

By query type:

• Category queries SOV: 8%
• Use case queries SOV: 15%
• Competitive queries SOV: 35%

By LLM platform:

• ChatGPT SOV: 12%
• Claude SOV: 9%
• Perplexity SOV: 14%
• Gemini SOV: 10%

By competitor:

• SOV vs. Competitor A: 45% (you) vs. 55% (them)
• SOV vs. Competitor B: 62% (you) vs. 38% (them)

Step 4: Establish Benchmarks

Track SOV monthly or quarterly to identify trends:

Q1 2025: 11.3% SOV
Q2 2025: 14.7% SOV (+3.4 points)
Q3 2025: 18.2% SOV (+3.5 points)
Q4 2025: 23.1% SOV (+4.9 points)

What “Good” Share of Voice Looks Like

SOV targets vary by market position and competitive dynamics.

Market Leader Benchmarks

Dominant Market Leader (Salesforce in CRM, Asana in PM):

• Target SOV: 30-40%
• Should appear in most relevant queries
• Multiple use cases and contexts

Strong #2 or #3 Player:

• Target SOV: 20-30%
• Appear frequently, sometimes as first choice
• Strong in specific use cases

Challenger Brand Benchmarks

Established Challenger:

• Target SOV: 10-20%
• Appear in significant minority of queries
• Own specific niches or use cases

Emerging Player:

• Target SOV: 5-10%
• Building initial visibility
• Focus on specific high-value segments

Startup or New Entrant:

• Target SOV: 1-5% initially
• Any visibility is progress
• Target niche queries where you can win

Industry Variations

B2B SaaS (Highly Competitive):

• Top 3 players: 25-35% SOV each
• Next 3 players: 10-20% SOV each
• Long tail: 1-10% SOV

E-commerce (Fragmented):

• Market leader: 20-30% SOV
• Major brands: 10-20% SOV
• Niche players: 5-15% SOV in their niche

Professional Services (Localized):

• National leaders: 15-25% SOV
• Regional players: 10-20% SOV in their region
• Specialists: 20-40% SOV in their specialty

Improving Your Share of Voice

SOV improvement requires systematic optimization.

Strategy 1: Increase Mention Rate

Objective: Appear more frequently across queries.

Tactics:

• Optimize content for RAG retrieval (rank well in search)
• Build authoritative citations in sources LLMs reference
• Establish clear entity presence (Wikipedia, Wikidata)
• Create content explicitly addressing target queries

Impact: Appearing in 30% of queries → 50% increases SOV significantly.

Strategy 2: Improve Positioning

Objective: When mentioned, be framed as first choice or strong contender.

Tactics:

• Build positive sentiment through customer success stories
• Earn enthusiastic press coverage
• Create comparison content positioning you favorably
• Address and resolve negative mentions

Impact: Being 3rd choice → 1st choice increases mindshare and conversions.

Strategy 3: Expand into New Query Segments

Objective: Win visibility in query categories where you currently don’t appear.

Tactics:

• Identify query gaps (competitors appear, you don’t)
• Create targeted content for those queries
• Build citations specifically addressing those use cases
• Optimize for niche terms with less competition

Impact: Adds entirely new visibility and expands total addressable mindshare.

Strategy 4: Competitive Displacement

Objective: Appear alongside or instead of key competitors.

Tactics:

• Create “[Your Brand] vs [Competitor]” content
• Appear in “alternatives to [Competitor]” queries
• Build citations in same sources that mention competitors
• Position yourself for same use cases

Impact: Directly compete for same mindshare as major competitors.

Share of Voice as a Leading Indicator

SOV is not just a vanity metric—it’s a leading indicator of business outcomes.

SOV Predicts Brand Awareness

Brands with higher SOV in AI search see corresponding increases in:

• Brand search volume (people searching your name directly)
• Website traffic from brand searches
• Inbound inquiries mentioning “I saw you recommended…”

SOV Correlates with Consideration

When buyers ask AI for recommendations, appearing in that answer puts you in their consideration set—even if they don’t click through immediately.

The consideration funnel:

1. User asks AI for recommendations
2. Your brand is mentioned (you’re now “known”)
3. User researches further (visits your site or competitor sites)
4. User makes decision

Traditional analytics only capture step 3-4. SOV helps you understand step 1-2.

SOV Influences Pipeline

For B2B companies, SOV in AI directly correlates with:

• Pipeline velocity (faster deal cycles when buyers already know you)
• Win rates (higher close rates when you’re in initial consideration)
• Deal sizes (appearing in “enterprise” queries brings larger opportunities)

Example correlation:

Company A: 8% SOV → 15% win rate, 120-day sales cycle
Company B: 23% SOV → 28% win rate, 85-day sales cycle

Implication: Higher SOV correlated with better sales outcomes

Tools for Share of Voice Measurement

Measuring SOV manually doesn’t scale. Several tools automate this:

Manual Measurement (Free)

Approach:

• Create query spreadsheet
• Manually test each query across LLMs
• Track results in spreadsheet
• Calculate SOV manually

Pros: Free, full control, good for initial understanding Cons: Time-intensive, hard to scale, human error

Best for: Small businesses, initial exploration, limited budgets

Automated Monitoring Platforms

Otterly.ai:

• Automated query testing across LLMs
• Basic SOV calculation
• Competitor benchmarking
• Affordable for mid-size companies

GEO-Metric:

• Comprehensive SOV tracking
• Advanced segmentation (by query type, LLM, etc.)
• Competitive intelligence
• Active optimization recommendations
• Best-in-class for enterprises

Traditional SEO Tools (Semrush, Ahrefs):

• Adding basic AI visibility features
• SOV not yet primary focus
• Good for teams already using these platforms

Custom Solutions

For enterprises with data teams:

• Build custom scripts using LLM APIs
• Automate testing at scale
• Create custom dashboards and reporting
• Integrate with existing BI tools

The Future of Share of Voice

As AI search grows, SOV will become as important as traditional SEO metrics.

SOV Will Become Standard Reporting

In 2-3 years, expect SOV to appear in:

• Monthly marketing reports alongside SEO rankings
• Competitive intelligence briefings
• Board presentations on brand health
• Agency reporting and accountability

SOV Attribution Will Improve

As AI platforms mature, attribution will become more sophisticated:

• LLMs may provide referral data (like Google Search Console)
• Tracking pixels or parameters for LLM-driven traffic
• Surveys capturing “How did you hear about us?” with “AI recommendation” option

SOV Benchmarking Will Standardize

Industry benchmarks and standards will emerge:

• “Average SOV for B2B SaaS is 12%”
• “Market leaders average 30%+ SOV”
• “Emerging players target 5% SOV in first year”

Conclusion: The Metric That Defines AI Era Competition

Share of Voice in AI is not a replacement for traditional metrics—it’s a complement. SEO rankings still matter. Website traffic still matters. Conversion rates still matter.

But as search behavior shifts toward AI assistants, SOV captures something traditional metrics miss: mindshare in the moment of recommendation.

Key takeaways:

1. SOV measures competitive visibility in AI search—the percentage of relevant AI responses mentioning your brand

2. SOV better reflects AI reality than traditional rankings because AI responses aren’t ranked lists

3. Calculate SOV systematically by testing queries across LLMs and tracking all brand mentions

4. Target SOV depends on market position—leaders aim for 30%+, challengers for 10-20%, emerging players for 5-10%

5. Improve SOV through increased mention rate, better positioning, new query segments, and competitive displacement

6. SOV is a leading indicator of brand awareness, consideration, and ultimately business outcomes

7. SOV will become standard as AI search grows and tools automate measurement

Start measuring your Share of Voice today. Benchmark against competitors. Set targets. Optimize systematically. Track progress monthly.

The brands that win in AI-powered search won’t be those with the highest traditional rankings. They’ll be those with the highest Share of Voice—the brands that AI systems confidently recommend when users ask for help.

Ready to improve your Share of Voice?

• Learn what GEO is - Foundational optimization strategies
• Optimize for ChatGPT - Practical tactics for AI visibility
• Enterprise GEO Playbook - Systematic implementation at scale