GEO

Optimization

What is AI Share of Voice? How to Measure and Improve It

Oct 9, 2025

Cat Moraga-Scholte

AI Share of Voice (AI SOV) measures how often your brand is mentioned compared to competitors in AI-generated responses from ChatGPT, Gemini, Perplexity, and Claude. To calculate it: divide your brand mentions by total brand mentions across relevant queries, then multiply by 100. Higher AI SOV means more visibility when potential customers ask AI assistants
for recommendations. Tools like NetRanks, Profound, and Peec.ai automate this tracking with continuous monitoring across multiple AI platforms.

What is AI Share of Voice?

AI Share of Voice is the percentage of AI-generated responses that mention or recommend your brand for relevant queries in your industry.

Formula:

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

Example: A user asks ChatGPT, Gemini, and Perplexity: "What are the best project management tools?"

Asana gets mentioned in 8 responses
Monday.com gets mentioned in 6 responses
Notion gets mentioned in 4 responses
ClickUp gets mentioned in 2 responses
Total mentions: 20

Asana's AI Share of Voice = (8 / 20) × 100 = 40%

This means Asana captures 40% of AI visibility for project management tool queries across these platforms.

AI Share of Voice Matters, now

AI Search is Growing Fast

ChatGPT has 200+ million weekly active users
Google AI Overviews appear for 30%+ of searches
Perplexity processes millions of queries daily
Users trust AI recommendations like they trust friend referrals

Zero-Click Discovery

Unlike traditional search where users see 10 links and choose, AI assistants give direct answers. If you're not in the answer, you're invisible.

Traditional search: User sees your link, decides whether to click

AI search: User gets a recommendation, often takes it directly

Competitive Displacement

When a competitor gets mentioned and you don't, that's lost mindshare. AI answers shape perception before users ever visit your website.

How to Measure AI Share of Voice

Method 1: Manual Tracking (Limited Scale)

Query AI platforms with relevant questions and track responses:

List 10-20 questions customers ask about your category
Query ChatGPT, Gemini, and Perplexity with each
Record which brands get mentioned
Calculate mention percentage

Limitations:

Time-consuming at scale
Responses change frequently (hourly/daily)
Hard to track trends over time
Doesn't capture all query variations

Method 2: Automated Monitoring (Recommended)

Use AI visibility platforms that continuously query AI engines and track mentions:

NetRanks: Continuous 24/7 scanning, weighted by rank position (Real-time updates)
Profound: Daily queries with citation analysis (Daily updates)
Peec.ai: Periodic queries, basic mention counting (Weekly updates)

Advantages of automated monitoring:

Covers thousands of query variations
Tracks changes across model updates
Provides historical trend data
Includes sentiment and rank position

Components of AI Share of Voice

Simple mention counting misses important context. Comprehensive AI SOV includes:

Mention Frequency

How often your brand appears in responses. Basic but essential metric.

Rank Position

Where you appear in recommendation lists matters:

Position 1: "The best tool for this is [Brand]"
Position 3: "[Brand] is also worth considering"
Position 5+: Often gets skipped by users

NetRanks weights SOV by rank position—being #1 counts more than being #5.

Sentiment Analysis

How AI describes your brand:

Positive: "highly recommended," "industry leader"
Neutral: "offers," "provides," "includes"
Negative: "has limitations," "users report issues"

Positive sentiment correlates with higher conversion from AI traffic.

Query Coverage

Which types of questions trigger mentions:

Category queries: "best CRM software"
Comparison queries: "Salesforce vs HubSpot"
Problem queries: "how to manage customer relationships"
Brand queries: "what is [Your Brand]"

Strong brands appear across all query types.

Source Attribution

Which websites AI engines cite when mentioning you:

Your own domain
Review sites (G2, Capterra)
Industry publications
Wikipedia/knowledge bases

Diverse source coverage indicates stronger entity recognition.

What is a Good AI Share of Voice?

Benchmarks vary by industry competitiveness:

Market leader: 25-40%
Strong challenger: 15-25%
Established player: 8-15%
Emerging brand: 2-8%
Invisible: <2%

Context matters: In a market with 50 competitors, 10% SOV means you're a top-5 brand. In a market with 5 competitors, 10% SOV means you're underperforming.

How to Improve AI Share of Voice

Optimize Content for AI Citation

AI engines prefer content that is:

Factual, not promotional

Replace: "Our industry-leading solution transforms businesses"
With: "The platform processes 10,000 transactions per second and integrates with 50+ CRM systems"

Simply written

Target grade 8-12 reading level
Short sentences (under 20 words)
Active voice over passive

Structured for extraction

Use numbered lists and bullet points
Include comparison tables
Add clear definitions at the start of sections

Directly answering questions

Match content to how users phrase queries
Include FAQ sections with common questions

Get Mentioned on High-Citation Domains

AI engines cite authoritative sources. Priority domains:

Reference (Wikipedia): Get a page (requires notability)
Discussion (Reddit): Authentic participation, not spam
Review sites (G2, Capterra, Product Hunt): Complete profiles, gather reviews
Industry publications (Search Engine Land, TechCrunch): Guest posts, earned media
Directories (Crunchbase, AngelList): Complete profiles

Establish Entity Presence

AI engines need to "know" your brand exists as an entity:

Wikidata: Create an entry with structured data
Google Knowledge Panel: Claim and optimize
LinkedIn Company Page: Complete with details
Crunchbase: Full company profile
Industry databases: Relevant directories for your sector

Use Segment-Level Optimization

Not all content helps equally. Some sentences actually hurt visibility.

NetRanks' Content Attribution Analysis scores each segment and identifies:

Sentences likely to be quoted by AI
Phrases hurting your visibility
Missing keywords from top-performing content
Specific rewrites with expected impact

This is the difference between tracking SOV and actively improving it.

Monitor and Iterate

AI models update frequently. What works today may not work next month.

Track SOV weekly or monthly
Note changes after AI model updates
Test content changes and measure impact
Watch competitor movements

AI Share of Voice vs Traditional Share of Voice

Traditional SOV:

Measures brand mentions in media, ads, social
Data from media monitoring, social listening
Accumulated over campaigns
Control by buying more ads, earning more media
Tools: Meltwater, Brandwatch

AI Share of Voice:

Measures brand mentions in AI-generated answers
Data from AI platform responses
Real-time, query-by-query
Control by optimizing content, building authority
Tools: NetRanks, Profound, Peec.ai

Both metrics matter, but AI SOV is becoming critical as more discovery happens through AI assistants.

Tracking AI Share of Voice Across Platforms

Different AI engines have different behaviors:

ChatGPT (OpenAI)

Largest user base
Relies heavily on training data
Web browsing adds real-time info
Tends to cite Wikipedia, Reddit frequently

Gemini (Google)

Integrated with Google Search
Access to Google's index
AI Overviews reach mainstream users
Strong emphasis on recency

Claude (Anthropic)

Growing in enterprise use
Different training approach
Less frequent web citations
More conversational responses

Perplexity AI

Search-first design
Always cites sources
Growing among researchers
Transparent about sources

Recommendation: Track SOV across all major platforms. A brand might be strong on ChatGPT but invisible on Perplexity.

Case Study: Improving AI SOV

Situation: A B2B SaaS company had 3% AI Share of Voice despite being a market leader in traditional search.

Analysis revealed:

Homepage content scored 55/100 for AI citation potential
Too much marketing language, not enough facts
No presence on G2, Capterra, or Crunchbase
Only mentioned on their own domain—no third-party citations

Actions taken:

Rewrote homepage with factual, simply-written content
Created complete profiles on G2, Capterra, Crunchbase
Published original research that industry sites cited
Added structured data and llms.txt

Results (90 days):

AI Share of Voice: 3% → 15%
Average rank position: #7 → #3
Citation sources: 1 → 12

Tools for Measuring AI Share of Voice

NetRanks

Continuous 24/7 monitoring across all major AI platforms
SOV weighted by rank position and sentiment
Segment-level content attribution
Predictive scoring before publishing
Prescriptive optimization recommendations

Best for: Teams who want to actively improve SOV, not just track it.

Profound

Daily monitoring with citation analysis
Compliance certifications (SOC 2, HIPAA)
Prompt volume analysis
Agent analytics for crawler tracking

Best for: Organizations where SOC 2 or HIPAA compliance documentation is required for procurement.

Peec.ai

Basic SOV tracking across ChatGPT and Gemini
Competitive benchmarking
Affordable entry point

Best for: SMBs wanting basic tracking on a budget.

Frequently Asked Questions

How often should I check AI Share of Voice?

Weekly for tactical decisions, monthly for trend analysis. AI models update frequently, so snapshot measurements miss changes.

Can I improve AI SOV quickly?

Some changes show impact within weeks—especially content optimization and entity establishment. Building third-party citations takes longer (months).

What's more important: SOV or rank position?

Both matter. High SOV with poor rank position means you're mentioned but not recommended. Aim for top-3 rank position with strong SOV.

Does traditional SEO help AI SOV?

Partially. Domain authority correlates with AI citation, but AI engines weight different signals. Content structure, factual density, and entity recognition matter more in AI than traditional SEO.

How do AI model updates affect SOV?

Major model updates can shift rankings significantly. Continuous monitoring catches these changes. Historical trend data helps you understand what's working.