Share of Voice in AI: Tracking Competitor Mentions in LLMs

Reading time: 14 minutes

TLDR

Category Share is your percentage of total brand mentions when AI platforms recommend businesses in your category. Unlike Google’s 10 blue links where rank #4 still gets visibility, AI creates winner-take-all outcomes: ChatGPT mentions 2-3 brands in an answer, everyone else is invisible. Track which competitors dominate mentions, calculate revenue impact (competitor 12% vs your 4% = 8-point gap = ~$4M in a $50M market), and monitor quarterly. Industry benchmarks vary wildly: SaaS leaders capture 45-55% share, fragmented markets see leaders at 10-15%. One restaurant went from 0% to #1 (67% visibility) in 30 days. Another business could realistically move from rank #6 to #3 in 60 days with focused effort. This isn’t vanity—Category Share is a leading indicator of future revenue as AI adoption grows.

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The New Competitive Reality: From Rankings to Mentions

2019: You tracked 10 Google results. Rank #1 got ~30% clicks, rank #10 got ~1%. Everyone got something.

2025: AI recommends 2-3 brands per answer. Mentioned brands get 100% consideration. Everyone else gets 0%.

The problem: Traditional competitive intelligence tools (SEMrush, Ahrefs, social listening) track Google rankings and social mentions. None track who AI platforms recommend when customers research your category.

The gap: A competitor can dominate ChatGPT recommendations without ranking #1 on Google or trending on Twitter. You won’t see it coming until revenue drops.

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What is Category Share?

Quick answer: Category Share is your percentage of total brand mentions across all AI recommendations in your competitive category. Calculated as: (Your mentions ÷ Total brand mentions) × 100.

How it’s calculated:

• Signal runs 50+ customer research queries across 7 AI platforms
• Counts every brand mentioned (both organic + prompted mentions)
• Your mentions ÷ Total = Category Share percentage
• Rankings show competitive position (#1, #6, #20 of 40 brands)

Example from real Signal report:

Your Brand: Veterans Moving America (Dallas/Fort Worth)
Category Share: 4.6%
Rank: #6 of 40 brands

Top 5 Competitors:
1. Two Men and a Truck: 10.7%
2. College Hunks Hauling Junk: 8.9%
3. Allied Van Lines: 7.3%
4. United Van Lines: 6.8%
5. Mayflower: 5.2%

What this tells you:

• Gap to #1: 10.7% - 4.6% = 6.1 percentage points (but represents 133% more AI visibility)
• Opportunity: Moving from #6 to #3 is realistic in 60 days (only need to close 2.7-point gap)
• Market structure: Fragmented (top 5 combined = ~40%, meaning 60% is spread across 35+ other brands)

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Why Category Share Matters More Than Google Rankings

The Zero-Click Reality

Traditional Google Search:

• Customer searches “Dallas movers”
• Sees 10 results
• Clicks 3-5 websites
• Compares yourself

AI Search:

• Customer asks ChatGPT “Which Dallas movers should I hire?”
• ChatGPT recommends 2 brands in answer
• Customer never visits Google
• Calls one of the two mentioned

The difference: In Google, rank #4 still gets traffic. In AI, not mentioned = not considered.

Category Share is a Leading Indicator of Revenue

Current reality: You might have 4.6% Category Share but only 0.2% actual market revenue.

Future reality: As AI adoption grows (currently 40% of searches use AI platforms, up from 10% in 2023), Category Share predicts future market share.

The correlation: Marketing research shows “share of voice” correlates with market share over time. If competitors have 12% Category Share and you have 4%, that gap indicates revenue at risk as customers shift to AI research.

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Industry Benchmarks: What’s a Good Category Share?

There is no universal benchmark. Category Share varies massively by market structure.

Concentrated Markets (Mature, Winner-Take-Most)

Characteristics:

• Clear market leaders (Salesforce, HubSpot, Shopify)
• Extensive documentation, reviews, analyst coverage
• AI training data has converged around 2-3 dominant brands

Benchmarks:

Position	Category Share	Example
Market Leader	45-55%	SaaS platform mentioned in 50% of queries
Strong Challenger	20-30%	Alternative recommended when leader doesn’t fit
Niche Player	5-10%	Mentioned only for specific use cases
New Entrant	<5%	Rarely mentioned unprompted

Example: B2B SaaS (CRM software)

• Top 3 brands: 70-80% of all AI mentions combined
• Leader (Salesforce): 45-55% Category Share
• Challenger (HubSpot): 25-30%
• Niche (Pipedrive): 8-12%
• Long tail (50+ others): <5% each

Why the concentration? AI models rely heavily on technical documentation, G2/Capterra reviews, and structured data. Leaders in SaaS excel at all three. A low Category Share here indicates “documentation debt”—your technical content is behind login walls or poorly structured.

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Fragmented Markets (Local Services, Many Competitors)

Characteristics:

• No dominant national brand (local moving, restaurants, professional services)
• Geographic distribution (Phoenix AC repair vs Dallas AC repair = different competitive sets)
• AI recommendations vary widely by location and persona

Benchmarks:

Position	Category Share	Example
Local Leader	10-15%	Well-reviewed business with strong GBP
Competitive	5-10%	Multiple businesses at similar visibility
Long Tail	0-5%	Hundreds of businesses splitting the rest

Example: Local Moving Companies (DFW)

• Leader (Two Men and a Truck): 10.7%
• Top 5 combined: <40%
• Top 10 combined: ~50%
• Remaining 30+ businesses: <5% each

Why fragmented? AI training data has no clear winner. Local businesses have similar review profiles, similar content depth. AI shows high variance because no brand has established semantic dominance.

Takeaway: Don’t compare your 10% to a SaaS company’s 50%. Compare to YOUR market. In fragmented categories, being #1 at 12% is market leadership.

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YMYL Markets (Finance, Healthcare, Legal)

Characteristics:

• “Your Money Your Life” categories with high stakes
• AI models are RLHF-tuned (Reinforcement Learning from Human Feedback) for safety
• Extreme preference for established institutions with third-party validation

Benchmarks:

Position	Category Share	Example
Trusted Institution	60%+	Major bank, hospital system, national firm
Regional Player	10-20%	Well-credentialed local provider
Startup/New Entrant	<5%	Fintech, healthtech without institutional backing

Example: Financial Advisory Services

• Established firm (Vanguard, Fidelity): 60%+ mentions
• Regional advisor with CFP certification: 12-18%
• New robo-advisor startup: <5%

Why the gap? AI platforms prioritize “safe” recommendations in YMYL. Models require Wikipedia presence, government citations, tier-1 news coverage. A startup blog carries zero weight here.

Breaking in: Requires “citation laundering”—getting mentioned in high-authority sources (WSJ, academic journals, government reports) that AI models trust.

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E-Commerce and Retail (Attribute-Driven, Long Tail)

Characteristics:

• Customers search by attributes (“best wool sweater under $100”) not brands
• Competition often includes generic recommendations (“look for merino wool blend”)
• No single brand dominates unless it’s a marketplace (Amazon)

Benchmarks:

Position	Category Share	Example
Category Leader	15-25%	Well-known DTC brand with strong content
Competitive Brand	5-10%	Strong product descriptions, reviews
Generic/Attribute	40-60%	AI recommends features, not brands

Example: Sustainable Apparel

• Leader (Patagonia): 22%
• Challengers (Allbirds, Everlane): 8-12% each
• Generic recommendations (“look for GOTS-certified organic cotton”): 45%

Why attribute-driven? Customers optimize for features (“sustainable,” “affordable,” “minimalist”) more than brand loyalty. AI matches attributes to products. Winning here requires rich product schema and content linking your brand to specific adjectives.

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How Signal Identifies Your Competitive Set

Question: Does Signal use a user-submitted list or AI-discovery?

Answer: Hybrid approach.

Option 1: You Specify Competitors (Focused Benchmarking)

When running Signal:

• You can provide 3-5 known competitor names or domains
• Signal “focuses benchmarking” on those specific brands
• Guarantees those competitors appear in your report with detailed metrics

Best for: Businesses with clear known rivals (e.g., you’re a CRM competing directly with Salesforce, HubSpot, Pipedrive)

Option 2: AI-Discovered Competitors (Landscape Mapping)

When running Signal:

• Leave competitor field blank
• Signal runs 50+ queries across 7 AI platforms
• Captures every brand name mentioned organically
• Typically finds 5-10 primary competitors, plus 10-30 secondary/long-tail mentions

Best for: New markets, discovering “invisible” competitors, or validating your assumptions about who you’re actually competing against in AI

The 20-40 Competitor List

How you get 40 competitors from 5-10 core rivals:

Signal aggregates mentions across:

• 7 AI platforms (ChatGPT, Claude, Gemini, Perplexity, Meta AI, Grok, DeepSeek)
• 9+ persona variations (family relocating, budget-conscious student, corporate buyer)
• 50+ total queries

Result: Even if only 3 competitors appear consistently, dozens more get mentioned occasionally across the full test set.

Your Competitive Landscape table ranks all of them:

• Rank #1-5: Core competitors (appear in 20-50% of responses)
• Rank #6-15: Secondary tier (appear in 5-20%)
• Rank #16-40: Long tail (appear in <5%, often due to niche queries or platform-specific quirks)

Example breakdown:

Rank | Brand                     | Response Rate | Category Share
-----|---------------------------|---------------|---------------
  1  | Two Men and a Truck       | 10.7%         | 10.7%
  2  | College Hunks Hauling     | 8.9%          | 8.9%
  3  | Allied Van Lines          | 7.3%          | 7.3%
...
  6  | Veterans Moving America   | 4.6%          | 4.6%
...
 18  | Local Regional Mover      | 1.2%          | 1.2%
 40  | Niche Specialty Service   | 0.3%          | 0.3%

Strategic insight: The long tail matters. That rank #18 competitor with 1.2% share might be a new entrant. Six months later, they could jump to 8%. Early detection lets you monitor and respond before they become a major threat.

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Revenue Impact Modeling: The Competitor Gap

The question: “Competitor A has 12% Category Share, you have 4%. How much revenue is that gap worth?”

The Calculation Framework

Step 1: Estimate AI-influenced market volume

Current industry data suggests 10-15% of searches have migrated to AI platforms (up from ~2% in 2022). This number grows quarterly.

AI-Influenced Market Volume = Total Market Revenue × Migration Factor

Example:

• Your total addressable market: $50M annual revenue
• AI migration factor: 15% (conservative estimate for 2025)
• AI-influenced volume: $50M × 0.15 = $7.5M

Step 2: Calculate the visibility gap

Visibility Gap = Competitor Category Share - Your Category Share

Example:

• Competitor A: 12% Category Share
• Your Brand: 4% Category Share
• Gap: 12% - 4% = 8 percentage points

Step 3: Estimate revenue at risk

Revenue at Risk = AI-Influenced Market Volume × Visibility Gap

Example:

• AI-influenced market: $7.5M
• Visibility gap: 8 percentage points (0.08)
• Revenue at risk: $7.5M × 0.08 = $600K annually

If you close the gap (improve from 4% to 12% Category Share):

• Potential revenue gain: $600K per year
• As AI adoption grows to 30-40% of market (projected 2026-2027), that $600K becomes $1.2M-$1.6M

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Real-World Example: Revenue Impact Model

Company: Phoenix Cool Air (AC repair) Market: Residential HVAC services, Phoenix metro (~$120M annual market) AI migration: 12% of customers now use ChatGPT/Perplexity for research

Current State:

• Your Category Share: 6%
• Top Competitor (George Brazil): 28%
• Gap: 22 percentage points

The Math:

AI-Influenced Market = $120M × 0.12 = $14.4M
Revenue Gap = $14.4M × 0.22 = $3.17M

Interpretation: George Brazil captures $3.17M more annually from AI-researched customers than you do, purely due to visibility gap.

Strategic scenarios:

Scenario	Your Category Share	Gap Closed	Revenue Gain (Annual)
Baseline (Current)	6%	0%	$0
Minor improvement	10% (+4pts)	18% of gap	$570K
Strong improvement	16% (+10pts)	45% of gap	$1.43M
Market leader	28% (+22pts)	100% of gap	$3.17M

Feasibility check:

• Moving from 6% → 10% Category Share (4 points) is realistic in 60-90 days with focused GBP optimization and review campaigns
• 10% → 16% (another 6 points) requires 6-12 months of sustained content, schema markup, and authority building
• 28% (matching leader) is 18-24 month effort requiring both volume and quality across all platforms

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Defensive Revenue: Churn Prevention

The hidden risk: Existing customers use AI to validate their current choices.

The scenario:

• Current customer asks ChatGPT: “Is [Your Brand] still the best [solution] for [use case]?”
• AI responds: “No, [Competitor] has recently surpassed [Your Brand] in speed and cost.”
• Churn probability increases 40-60% (based on SaaS churn analysis)

The calculation:

Churn Risk Value = Total ARR × % Customers Using AI × Negative Sentiment Rate

Example: B2B SaaS Company

• Total ARR: $5M
• % customers using AI for research: 25% (growing)
• % of AI responses positioning competitor as superior: 40%
• Churn risk value: $5M × 0.25 × 0.40 = $500K annually

Action: Defensive Category Share monitoring. If competitors gain ground in AI positioning (their Authority Score increases while yours declines), it’s not just a new customer acquisition problem—it’s a retention crisis.

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Competitive Movement: Tracking Changes Over Time

The dynamic reality: Category Share rankings are not static. Competitors can jump 5-10 positions in 60-90 days.

Real Case Study: El Tianguis Restaurant (San Diego)

Timeline: 30 days Starting Position: Rank unranked, 0% Presence Rate (invisible) Ending Position: Rank #1, 67% Presence Rate (dominant)

What they did:

• Week 1: Added FAQ schema to website answering “celiac-safe Mexican food San Diego”
• Week 2: Optimized GBP with “gluten-free certified kitchen” messaging
• Week 3: Got featured in local food blog (“Best celiac-safe restaurants”)
• Week 4: Re-ran Signal, verified #1 position

Result: 0% → 67% in one month. Went from never mentioned to recommended in 2 out of 3 AI queries.

Why it worked: Addressed a specific “data void”—AI had no strong answer for “celiac-safe Mexican food” in San Diego. They filled it with structured content and third-party validation.

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Realistic Movement Benchmarks

Signal data shows:

Current Position	Realistic Target (60 days)	Actions Required
Rank #11-20	Move to #8-12	GBP 100% complete, 15-25 reviews, FAQ pages
Rank #6-10	Move to #3-5	Schema markup, 50+ reviews, local press citation
Rank #3-5	Move to #1-2	Authority building (awards, certifications), sustained content velocity (50+ pages)
Rank #1-2	Defend position	Monitor competitors weekly, maintain review velocity, expand to new personas

Example from Signal report:

Company: Veterans Moving America Baseline: Rank #6 of 40 (4.6% Category Share) Gap to #1: 10.7% - 4.6% = 6.1 percentage points (but represents 133% more visibility) Assessment: “A focused 60-day GBP + review campaign could realistically move them to #3 or #4”

Why realistic?

• Only need to close 2.7-point gap to reach #3 (from 4.6% to 7.3%)
• Competitors #3-5 are national brands (Allied, United, Mayflower) that update slowly
• Local business can move faster (update GBP same day, competitors need corporate approval)
• 60-day focused effort (GBP rewrite, 50 review requests, 3 press mentions) = proven 5-10 point lift

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Detecting Competitor Surges

Quarterly monitoring reveals movements like:

Example: Competitor X jumped from Rank #8 to #3 in Q2

Q1 2025: Rank #8, 3.2% Category Share
Q2 2025: Rank #3, 11.8% Category Share
Change: +8.6 percentage points in 90 days

What likely happened:

• Launched aggressive review campaign (100+ new reviews in 60 days)
• Got featured in local news (AI now cites “award-winning” from article)
• Implemented schema markup (LocalBusiness + Service + Review structured data)

Your response:

• Investigate their tactics (check their GBP, website changes, recent press)
• If effective, adapt similar strategy
• If unsustainable (e.g., paid reviews that violate Google TOS), monitor for correction
• Double down on your differentiators to prevent further share loss

Real insight: The velocity of change matters. A competitor gaining 2 points per quarter is on track to overtake you in 6-12 months. Early detection allows proactive response.

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Strategic Use Cases for Category Share Data

Beyond “Who’s #1?” — Five High-Value Applications

1. Identifying Acquisition or Partnership Targets

The opportunity: Category Share reveals high-performing niche players you might not know about.

Example scenario:

• You’re a regional HVAC company (Rank #4, 8% Category Share)
• Signal report shows unfamiliar brand at Rank #7 with 5% share
• Investigation reveals: small company, only 3 employees, but dominates specific persona (“eco-friendly AC installation”)
• Insight: They have superior content/positioning in a growing niche. Acquire them or partner before a larger competitor does.

Data to analyze:

• High Authority Score (70+) but low Presence Rate (<15%) = niche expertise worth acquiring
• Rapidly growing share (2% → 5% in one quarter) = early-stage competitor to watch or acquire
• Strong platform-specific performance (e.g., dominates Claude but weak elsewhere) = specific technical advantage

Surmado tip: Run Signal on potential acquisition targets before deals. Compare their metrics to yours. If they have higher Authority Score in key personas, that validates the acquisition premium.

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2. Detecting New Market Entrants Early

The blind spot: Traditional tools miss competitors who gain AI traction without Google/social presence.

Example scenario:

• Your Signal report lists 40 competitors
• Rank #32 is an unfamiliar name with 1.2% Category Share
• You ignore it (too small)
• 3 months later: They’re Rank #12 with 6.8% share
• 6 months later: They’re Rank #5 with 14% share, eating into your customer base

Early warning system:

Quarter	Rank	Category Share	Action
Q1 2025	#32	1.2%	Monitor (new entrant detected)
Q2 2025	#18	4.1%	Investigate (3x growth in 90 days)
Q3 2025	#12	6.8%	Respond (now a real threat)

What to investigate:

• What personas are they winning? (Check platform breakdown in Signal)
• What content/features drive their mentions? (Qualitative analysis of AI responses)
• Are they venture-backed? (Rapid growth may indicate VC funding aggressive marketing)

Strategic response:

• If they’re winning on price: validate your pricing strategy, consider value tier
• If they’re winning on features: assess product roadmap gaps
• If they’re winning on content: audit their SEO/GEO execution, adapt tactics

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3. Validating Pricing and Positioning Strategy

The question: “Are we positioned correctly for how customers actually search?”

How Category Share answers it:

Scenario A: Price-Conscious Positioning

• You market as “affordable” and “budget-friendly”
• Signal shows you’re mentioned in 3% of “cheap [service]” queries
• But competitor dominates with 22% for those same queries
• Insight: Your pricing messaging isn’t clear enough. Competitor has “upfront pricing” on GBP, you don’t. Fix the positioning gap.

Scenario B: Premium Positioning Mismatch

• You’re a high-end provider
• Signal shows you’re mentioned in 18% of “best quality [service]” queries (good!)
• But also appear in 12% of “cheap [service]” queries (bad—wrong audience)
• Insight: Your content mixes price and quality signals. Tighten premium positioning, stop competing on price.

Data to examine:

• Persona-level breakdown (Signal Pro): Which customer types mention you?
• Sentiment analysis: Are you described as “affordable” or “premium” or “generic”?
• Competitor comparison: Who owns the “value” segment? Who owns “luxury”?

Example from Signal data:

Your Brand (Budget Positioning):
- "Affordable" queries: 3% Category Share (weak)
- "Best quality" queries: 1% Category Share (irrelevant)

Competitor A (Actually Wins Budget Segment):
- "Affordable" queries: 22% Category Share (dominant)
- Strategy: Explicit "$99 upfront pricing" in GBP, comparison tables on website

Your Fix:
- Add transparent pricing to GBP description
- Create "Pricing" FAQ page with exact costs
- Request reviews mentioning "affordable" and "transparent"

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4. Uncovering Content and Feature Gaps (Ghost Influence)

The insight: Category Share + Ghost Influence reveals what features customers care about and who gets credit.

Example scenario:

• Your Signal report shows 35% Ghost Influence
• Analysis reveals: Customers ask about “24/7 emergency service”
• AI recommends Competitor X who mentions “24/7 availability” prominently
• Reality: You also offer 24/7 service, but it’s buried on page 3 of your website
• Gap: Content visibility, not product capability

Action:

1. Add “24/7 Emergency Service” to GBP primary services
2. Create FAQ page: “Do you offer 24/7 emergency AC repair?”
3. Update homepage hero section with “24/7 availability” badge
4. Request review updates from past after-hours customers mentioning “quick response”

Result: Next Signal report shows Ghost Influence drops to 18%, your Category Share rises 4 points because you now get credit for your actual differentiators.

Strategic insight: Ghost Influence analysis tells you what to prioritize in product roadmap. If every top competitor highlighted by AI has a feature you lack (e.g., “free trial,” “API integrations,” “mobile app”), the market has spoken. Build it or risk permanent visibility loss.

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5. Board-Level Competitive Intelligence and Investor Reporting

The use case: Quantify competitive position over time for stakeholders.

Traditional metrics leadership cares about:

• Market share (trailing indicator, quarterly/annual)
• Customer acquisition cost (financial, not strategic)
• NPS scores (internal, not competitive)

Category Share adds:

• Leading indicator of market share shifts (AI visibility predicts future revenue)
• Competitive movement tracking (who’s gaining ground before it hits revenue)
• Strategic validation (did our campaign work? Category Share +6 points = yes)

Example board slide:

Q1 2025 AI Competitive Intelligence

Category Share: 8.2% (↑ from 6.1% in Q4 2024)
Rank: #4 of 38 (↑ from #6)
Gap to Leader: 14.3% (↓ from 18.7%)

Actions Taken:
- 60-day GBP optimization campaign
- 87 new reviews collected (target: 75)
- Schema markup implemented

Result: +2.1 percentage point Category Share gain
Projected Revenue Impact (2025): +$840K (based on 15% AI migration)

Competitor Movement:
- Competitor X: Rank #8 → #5 (investigate)
- Competitor Y: Rank #3 → #7 (declining, opportunity)

Next Quarter Focus: Close gap to #3 (need +3.4 points)

Why this matters:

• Validates marketing spend with competitive context
• Shows momentum (are we gaining or losing ground?)
• Identifies threats early (Competitor X surge requires response)
• Demonstrates strategic thinking (we’re not just “doing SEO,” we’re winning competitive positioning)

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The Quarterly Monitoring Strategy

Why quarterly?

• AI model update cycles: GPT-4, Claude, Gemini have major updates every 3-6 months
• Strategic latency: Content and PR efforts take 30-60 days to be indexed and reflected in AI outputs
• RAG indexing: Real-time AI (Perplexity, Bing) re-indexes every 2-4 weeks, but foundational models lag
• Monthly is too noisy (random variance in AI responses)
• Annual is too slow (miss competitor surges, can’t course-correct in time)

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The 90-Day Execution Cycle

Month 1: Deep Audit (Baseline)

Objective: Establish current “truth” of competitive landscape

Actions:

1. Run Signal report (full 50+ query set across 7 platforms)
2. Document all competitors (Rank #1-40)
3. Calculate Category Share gaps to top 3 competitors
4. Identify Ghost Influence patterns (which features are attributed to competitors?)
5. Analyze platform variance (strong on ChatGPT but weak on Claude?)

Deliverable: “State of AI Visibility” report

Key metrics:

• Your Category Share: X%
• Rank: #Y of Z
• Gap to #1: N percentage points
• Revenue at risk: $X (based on visibility gap × market size)

Example findings:

Baseline (January 2025):
- Category Share: 6.2%
- Rank: #7 of 42
- Gap to #1: 12.8 percentage points
- Revenue at risk: $1.8M (in $50M market, 15% AI migration)

Competitive threats identified:
- Competitor X (Rank #4) growing 2pts/quarter
- New entrant (Rank #18) at 2.1% but growing fast

Platform gaps:
- Strong on ChatGPT (8% share)
- Weak on Claude (2% share) ← Focus area

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Month 2: Content Response (Intervention)

Objective: Influence the data that AI models use

Tactic: “Data Void Patching”

If audit revealed AI thinks Competitor A is the only one with “Feature X,” flood ecosystem with authoritative content linking YOUR brand to that feature.

Actions:

Week 5-6:

1. GBP Optimization

   • Rewrite description connecting your differentiators to customer outcomes
   • Add 3-5 Q&A entries addressing exact queries from Signal report
   • Update services list with specific features competitors are getting credit for

2. Schema Markup Implementation

   • Add LocalBusiness schema (if missing)
   • Add Service schema for each offering
   • Add Review schema to surface ratings
   • Add FAQ schema for new content

Week 7-8: 3. Review Campaign

• Personal outreach to 50-100 past customers
• Request reviews mentioning specific differentiators (not generic “great service”)
• Target keywords from Ghost Influence analysis

4. Content Publishing
   • Publish 3-5 FAQ pages answering queries where you’re invisible
   • Create comparison page (“Your Brand vs Competitor A”)
   • Publish case study with specific outcomes

Week 9: 5. Third-Party Validation

• Pitch local news for expert commentary
• Submit for industry awards or certifications
• Get listed in authoritative directories (BBB, industry-specific)

Effort estimate: 40-60 hours total over 4 weeks (can be distributed across team)

Cost estimate: $0-$2,000 (mostly time; paid options: schema consultant $500-1000, PR outreach tool $200/mo)

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Month 3: Pulse Check & Verification

Objective: Measure impact, adjust tactics

Actions:

Week 10:

1. Re-run Signal (subset) on RAG-enabled platforms (Perplexity, Bing with ChatGPT)

   • Why these first? Real-time indexing means they update faster (2-4 weeks vs 3-6 months for foundational models)
   • If you see movement here, it’s a leading indicator

2. Compare metrics:

   • Category Share: 6.2% → 8.7% (+2.5 points) ✓
   • Rank: #7 → #5 (moved up 2 positions) ✓
   • Platform gap (Claude): 2% → 5% (+3 points) ✓

Week 11-12: 3. Analyze what worked:

• GBP rewrite: +1.2 points share (highest impact)
• Reviews (87 new): +0.8 points
• Schema markup: +0.5 points (delayed impact, will compound)
• Press mention: +0.3 points (one local article)

4. Update revenue model:

   • New Category Share: 8.7%
   • Gap to #1: now 10.3% (down from 12.8%)
   • Revenue at risk reduced by: $450K (in $50M market)

5. Plan next quarter:

   • If Category Share improved: Double down on what worked (more reviews, expand content)
   • If flat or declining: Pivot tactics (maybe GBP isn’t the bottleneck, try schema + PR instead)
   • If competitor surged: Investigate and respond

Pivot points (decision rules):

Scenario After 90 Days	Action
Category Share +3-5 points	Success. Continue current tactics, expand to new personas
Category Share +1-2 points	Modest progress. Accelerate effort (double review velocity, add more content)
Category Share flat (±0.5 points)	Pivot required. Likely wrong focus area (e.g., GBP already strong, need schema/PR instead)
Category Share declining	Competitor surge detected. Run competitive analysis, identify their tactics, respond aggressively

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Automated Monitoring: LLM-as-a-Judge

The scalability problem: Manually checking 50 queries across 7 platforms every month is unsustainable.

Solution: Automate with API scripts.

Basic workflow:

import openai
import json

# Step 1: Run test queries
queries = [
  "Which Dallas movers are most reliable?",
  "Best affordable HVAC service Phoenix",
  # ... 50+ queries
]

results = []
for query in queries:
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role": "user", "content": query}]
    )
    results.append({
        "query": query,
        "answer": response.choices[0].message.content
    })

# Step 2: Extract brand mentions with LLM-as-a-Judge
for result in results:
    analysis = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{
            "role": "user",
            "content": f"""
            Extract all business/brand names mentioned in this text.
            For each, assign a sentiment score from -1 (negative) to +1 (positive).
            Return as JSON.

            Text: {result['answer']}
            """
        }]
    )
    result['brands'] = json.loads(analysis.choices[0].message.content)

# Step 3: Calculate Category Share
# (Count mentions, aggregate by brand, calculate percentages)

Dashboard the results:

• Track your Category Share over time (line graph)
• Monitor competitor movements (table with % change)
• Alert on surges (if any competitor gains >2 points in one month, send notification)

Frequency:

• Weekly pulse: Run top 10 most important queries on RAG platforms
• Monthly check: Run full 50-query set across all 7 platforms
• Quarterly deep dive: Full Signal report + qualitative analysis

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Bottom Line: Category Share is a Leading Indicator

The shift: For 20 years, Google rankings predicted revenue. High rank = high traffic = high sales.

The new reality: AI recommendations increasingly drive purchase decisions. High Category Share = high consideration = high future revenue.

Category Share is not revenue share (yet). You might have 4.6% Category Share but only 0.2% market revenue today. But as AI adoption grows from 15% to 40% to 60% of customer research, Category Share becomes revenue share.

Strategic imperative: Track your competitive position in AI now, before it’s too late to catch up.

The businesses that win in 2025-2027:

• Monitor Category Share quarterly (like tracking Google rankings monthly in 2010)
• Model revenue impact (visibility gap × market size = dollars at risk)
• Respond to competitor surges within 30 days (not 6 months)
• Invest in “AI-first” content (structured data, FAQ schema, authoritative citations)

The businesses that lose:

• Assume Google SEO is enough
• Ignore AI visibility until revenue declines
• Discover too late that competitors own 40% Category Share while they have 3%

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Related Resources

→ Metrics explained: What is Presence Rate? | What is Authority Score? | What is Ghost Influence?

→ Strategy guides: SEO + GEO: Why You Need Both | Understanding Your Signal Report

→ Competitive analysis: AI Visibility vs Traditional SEO | How Signal + Solutions Work Together

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Ready to measure your Category Share? Run Signal to see where you rank against 20-40 competitors across 7 AI platforms. Essential ($25) gives baseline metrics, Pro ($50) includes persona breakdown and platform variance.