5 Companies That Increased AI Citations by 300%+ (Case Studies)

TL;DR

These five B2B companies increased their AI search citations by 300%+ using proven GEO strategies: (1) A CRM challenger increased Share of Model from 4% to 18% by publishing original research; (2) A cybersecurity startup went from 0% to 12% SoM through schema implementation and PR; (3) A marketing platform achieved 340% citation growth via answer-first content; (4) An HR tech company gained 15% SoM through entity optimization; (5) A data analytics firm tripled visibility with industry benchmarks.

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Introduction

"Can AI search visibility really be improved, or is it just luck?"

This is the most common question we hear from B2B marketers. The answer: AI visibility is absolutely improvable—and the results can be dramatic.

This article presents five real case studies of B2B companies that significantly increased their AI search visibility using systematic GEO (Generative Engine Optimization) strategies.

What you'll learn:

• Starting points and baselines
• Specific strategies implemented
• Timeline to results
• Measurable outcomes
• Key lessons for your own efforts

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Case Study 1: CRM Challenger Breaks Into the Conversation

Company Profile

• Industry: CRM Software
• Company Size: 200 employees, $40M ARR
• Market Position: Challenger (not in top 10 by market share)
• Starting AI Visibility: 4% Share of Model

The Challenge

This CRM company had strong Google rankings for long-tail keywords but was virtually invisible in AI search. When users asked ChatGPT or Perplexity "What's the best CRM?", Salesforce and HubSpot dominated. They weren't even mentioned.

Initial Assessment:

• Share of Model: 4%
• ChatGPT mentions: 2% of relevant queries
• Perplexity citations: 6% of queries
• Competitor (market leader): 28% SoM

The Strategy

Phase 1: Original Research (Month 1-2)

Published "The 2026 State of CRM Report" featuring:

• Survey of 1,500 CRM users
• Unique statistics on CRM adoption, challenges, and ROI
• Industry-specific breakdowns
• Quotable data points throughout

Phase 2: Content Restructuring (Month 2-3)

• Reformatted key pages with answer-first structure
• Added TL;DR sections to all guides
• Created dedicated "CRM for [Industry]" pages
• Implemented FAQ schema on all pages

Phase 3: Authority Building (Month 3-6)

• Pitched research findings to 15 industry publications
• Secured coverage in MarTech Today, SaaS Mag, TechCrunch
• Got quoted in 8 articles about CRM trends
• Launched a podcast with CRM industry experts

The Results

Key Lessons

Original research is the great equalizer—a challenger can out-cite a market leader with unique data

PR amplification multiplies research impact—the coverage drove more citations than the research alone

Niche queries are entry points—they gained visibility on "best CRM for [specific industry]" before general queries

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Case Study 2: Cybersecurity Startup Goes From Zero to Hero

Company Profile

• Industry: Cybersecurity (endpoint protection)
• Company Size: 50 employees, $8M ARR
• Market Position: Early-stage startup
• Starting AI Visibility: 0% Share of Model

The Challenge

As a startup competing against CrowdStrike and SentinelOne, they had zero AI visibility. AI never mentioned them, even for specific use cases they excelled at.

Initial Assessment:

• Share of Model: 0%
• Not mentioned in ANY of 200 tracked queries
• Competitor mentions: 24% (CrowdStrike), 11% (SentinelOne)
• Google rankings: Page 3-4 for key terms

The Strategy

Phase 1: Technical Foundation (Month 1)

• Implemented comprehensive schema markup

- Organization schema with security certifications

- Product schema with detailed features

- FAQ schema on all support content

• Fixed site speed issues (FCP: 2.1s → 0.6s)
• Created robots.txt explicitly allowing AI crawlers

Phase 2: Entity Establishment (Month 1-2)

• Created detailed founder/team pages with credentials
• Submitted to Crunchbase, G2, Capterra with consistent information
• Built out LinkedIn presence for all executives
• Applied for and completed SOC 2 certification

Phase 3: Targeted Content (Month 2-4)

• Created "Alternative to [Competitor]" pages
• Published comparison content for every major competitor
• Developed use-case specific landing pages
• Built a comprehensive threat intelligence blog

Phase 4: Validation Building (Month 3-6)

• Pursued analyst briefings (Gartner, Forrester)
• Commissioned independent security tests
• Pitched to cybersecurity publications
• Got mentioned in 12 industry round-ups

The Results

Key Lessons

Zero to something is achievable in 6 months with focused effort

"Alternative to [Leader]" content is highly effective for startups

Schema markup and technical foundation must come first

Third-party validation accelerates trust faster than owned content

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Case Study 3: Marketing Platform's Content Transformation

Company Profile

• Industry: Marketing Automation
• Company Size: 500 employees, $75M ARR
• Market Position: Mid-market player
• Starting AI Visibility: 7% Share of Model

The Challenge

Despite strong content marketing (200+ blog posts), their AI visibility was mediocre. The problem: content was optimized for SEO, not AI citability.

Initial Assessment:

• Share of Model: 7%
• Content library: 200+ posts (keyword-optimized, not answer-optimized)
• Perplexity citations: 4% (despite high Google rankings)
• Lost 3 major deals where buyers cited AI research

The Strategy

Phase 1: Content Audit & Restructure (Month 1-2)

Audited all 200+ posts and restructured top 50:

• Added TL;DR sections (50-70 words, direct answer)
• Reformatted to answer-first structure
• Added specific statistics (researched or created)
• Implemented FAQ schema on all reformatted posts

Phase 2: New Content Framework (Month 2-4)

Created new content using "AI-First Content Template":

• Question-based H1 and H2s
• Direct answer in first paragraph
• Original data point within first 200 words
• Clear, quotable definitions
• Structured comparison tables

Phase 3: Author Authority (Month 3-5)

• Created detailed author pages for all content creators
• Added credentials and expertise to every post
• Linked authors to LinkedIn profiles
• Had CMO publish thought leadership on LinkedIn

The Results

Key Lessons

Existing content can be transformed—you don't need to start over

Answer-first structure is crucial—SEO-optimized intros hurt AI visibility

The first 50-70 words determine citability

Author authority signals compound across all content

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Case Study 4: HR Tech Company's Entity Optimization Win

Company Profile

• Industry: HR/HCM Software
• Company Size: 300 employees, $50M ARR
• Market Position: Established challenger
• Starting AI Visibility: 8% Share of Model

The Challenge

The company had inconsistent brand information across the web. AI systems couldn't reliably identify them as a distinct entity, leading to confusion and missed citations.

Initial Assessment:

• Share of Model: 8%
• Brand name spelled 3 different ways across directories
• Company description inconsistent across platforms
• No Wikipedia presence
• Schema markup: None

The Strategy

Phase 1: Entity Audit & Cleanup (Month 1)

• Identified all brand mentions across web
• Standardized brand name everywhere (fixed 47 inconsistencies)
• Created consistent company description (used across all platforms)
• Updated all directory listings

Phase 2: Entity Definition (Month 1-2)

• Implemented comprehensive Organization schema
• Created Wikidata entry
• Submitted to all major business databases
• Updated Crunchbase, LinkedIn, G2 with consistent information

Phase 3: Entity Reinforcement (Month 2-4)

• Published "About [Company]" press release
• Secured media coverage mentioning official brand description
• Had employees update LinkedIn titles consistently
• Created Knowledge Panel-optimized About page

Phase 4: Wikipedia Effort (Month 4-6)

• Met notability requirements through press coverage
• Created Wikipedia article (neutral tone, well-sourced)
• Monitored and maintained article quality

The Results

Key Lessons

Entity consistency is foundational—AI can't cite what it can't identify

Small inconsistencies have big impacts—name variations confused AI

Wikipedia significantly boosts ChatGPT visibility

Knowledge Panel achievement correlates with AI visibility

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Case Study 5: Data Analytics Firm's Benchmark Strategy

Company Profile

• Industry: Data Analytics / BI
• Company Size: 150 employees, $25M ARR
• Market Position: Specialist in healthcare analytics
• Starting AI Visibility: 5% Share of Model

The Challenge

As a specialist in a specific vertical (healthcare), they struggled to get mentioned alongside generalist players like Tableau and Looker in AI responses.

Initial Assessment:

• Share of Model: 5% overall
• Healthcare-specific queries: 12%
• General "data analytics" queries: 2%
• Competitor (Tableau): 29% SoM

The Strategy

Phase 1: Niche Dominance (Month 1-3)

• Decided to dominate healthcare analytics AI visibility before expanding
• Created "Healthcare Data Analytics" definitive guide (8,000 words)
• Published "Healthcare Analytics Benchmark Report" with industry-specific data
• Developed case studies from 10 healthcare clients

Phase 2: Industry-Specific Content (Month 2-4)

• Created content for every healthcare analytics use case
• Built comparison pages: "[Company] vs Tableau for Healthcare"
• Developed "Healthcare Analytics ROI Calculator" with original data
• Published compliance-specific guides (HIPAA analytics, etc.)

Phase 3: Vertical Authority (Month 3-6)

• Joined healthcare industry associations
• Spoke at 5 healthcare conferences
• Got quoted in Healthcare IT News, Modern Healthcare
• Published in peer-reviewed healthcare informatics journal

The Results

Key Lessons

Niche dominance beats broad mediocrity—own your vertical first

Industry-specific benchmarks are highly citable

Comparison content targeting "vs [Leader] for [Vertical]" is effective

Conference presence and publications build vertical authority

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Common Success Patterns

What All Five Companies Did

Timeline to Results

Investment Requirements

*Includes content creation, PR, tools, and internal team time*

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Your Turn: Getting Started

Quick Assessment

Ask yourself:

What's your current Share of Model?

Do you have original research/data to cite?

Is your schema markup complete?

Is your entity information consistent?

Do you have third-party validation?

First Steps

Measure your baseline with KnewSearch

Identify the biggest gap (technical, content, or authority)

Start with quick wins (schema, content restructuring)

Build toward research (highest impact, longer timeline)

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Key Takeaways

• 300%+ citation growth is achievable with systematic effort
• Original research is the highest-impact tactic (appeared in 4/5 cases)
• Technical foundation (schema) is prerequisite (all 5 companies)
• Third-party validation accelerates results (all 5 companies)
• Niche dominance beats broad mediocrity (Case 5)
• Timeline to significant results: 3-6 months

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