The ROI of Generative Engine Optimization: Cybersecurity Case Study Compendium

Published February 2026 · 8 Case Studies · ROI Data Across 6 AI Platforms · Pipeline Attribution Models Included

Aggregate Results Across All Case Studies

• 60% average increase in AI visibility score within 90 days of implementing GEO strategies
• 20–35% increase in inbound leads attributable to AI search visibility improvements
• 3–5× higher conversion rate from AI-referred traffic compared to traditional organic search
• 15–30% reduction in customer acquisition cost when AI search becomes a meaningful pipeline source
• $78K average annual GEO investment vs. $560K+ for equivalent traditional content team — 86% cost reduction

1. The ROI Framework for GEO

1.1 Why Traditional ROI Models Don't Work for GEO

Most cybersecurity marketing teams measure content ROI through a linear pipeline: content creates traffic → traffic generates leads → leads become opportunities → opportunities close. GEO disrupts this model because its highest-value impact — being recommended by AI when buyers ask for solutions — often happens before any website visit occurs.

A CISO who asks ChatGPT "What's the best cloud security platform for mid-market healthcare?" and receives a response recommending your product has been influenced by your GEO investment. But if they then Google your brand name directly and request a demo, your analytics attributes that lead to "branded search" — not to the AI recommendation that actually drove the decision.

1.2 The Three-Layer ROI Model

Key Insight: Companies that measure only Layer 1 (direct AI-referred traffic) typically see 10–20% of the true GEO ROI. The most sophisticated cybersecurity marketers in this study measured all three layers and found that total GEO-influenced pipeline was 5–7× higher than what direct attribution alone showed.

1.3 The Cost Comparison Baseline

2. Case Study: Mid-Market Endpoint Security Vendor

Company Profile

Category: Endpoint Detection & Response · Size: 200 employees · Stage: Series B · Pre-GEO AI Visibility: 8% (cited in fewer than 1 in 12 relevant AI responses)

The Challenge

Despite strong product-market fit and a growing customer base, this EDR vendor was invisible in AI search. When prospects asked ChatGPT or Perplexity about endpoint security solutions, CrowdStrike, SentinelOne, and Microsoft Defender dominated the recommendations. The company's existing SEO strategy generated steady organic traffic but zero AI citations.

GEO Investment

• Created 15 structured comparison pages ("[Company] vs [Competitor]" with feature matrices)
• Built a real-time CVE tracking portal covering 2,500+ security keywords
• Launched 8 "Best EDR for [industry]" listicle pages with balanced, vendor-neutral analysis
• Implemented direct-answer blocks and FAQ schema across all product pages
• Ungated 3 major technical whitepapers to serve as citation sources

Results (90 Days)

• 8% → 41% AI visibility score improvement — from invisible to appearing in 4 of 10 relevant AI responses
• +28% increase in inbound demo requests, with 18% of new demos self-reporting AI as discovery channel
• 4.2× higher conversion rate from AI-referred visitors vs. traditional organic traffic
• $340K estimated pipeline influenced by AI visibility in first 90 days (all three attribution layers)

ROI Calculation

3. Case Study: Enterprise SIEM Platform

Company Profile

Category: SIEM / Security Analytics · Size: 800 employees · Stage: Late-stage / pre-IPO · Pre-GEO AI Visibility: 22%

The Challenge

Competing against Splunk and Microsoft Sentinel in AI recommendations. Despite strong analyst coverage (Gartner Magic Quadrant leader), the company's AI visibility lagged behind competitors whose content was better structured for AI consumption. Their extensive but poorly structured documentation wasn't earning the citations their market position warranted.

GEO Investment

• Restructured 40+ existing product documentation pages with direct-answer blocks and comparison tables
• Created a comprehensive integration directory with detailed pages for 150+ integrations
• Built a compliance mapping portal (NIST CSF, SOC 2, HIPAA, PCI-DSS) with product-specific control coverage
• Launched 12 industry-specific use case pages ("SIEM for healthcare," "SIEM for financial services," etc.)
• Updated pricing transparency — published pricing tiers publicly for the first time

Results (6 Months)

• 22% → 54% AI visibility score — from mid-pack to #2 position in SIEM category citations
• +35% increase in inbound qualified leads with 22% citing AI discovery
• 18% shorter sales cycles for AI-influenced deals vs. traditional pipeline
• $2.1M total pipeline attributed to AI visibility improvements over 6 months

Key Learnings

• Integration documentation was the biggest lever. Each detailed integration page became a citation source when buyers asked "Does [Vendor] integrate with [Platform]?" — a query type representing 8% of all cybersecurity AI prompts.
• Pricing transparency delivered outsized results. Publishing pricing increased citations on Perplexity and Google AI Overviews by 40%+ for pricing-related queries.
• Sales cycle compression was unexpected ROI. AI-influenced buyers arrived pre-educated, reducing average sales cycle by 18% and freeing sales capacity.

4. Case Study: Cloud Security Startup

Company Profile

Category: Cloud Security / CNAPP · Size: 45 employees · Stage: Series A · Pre-GEO AI Visibility: 2%

The Challenge

As a well-funded but early-stage startup, the company had minimal web presence and zero AI visibility. Their product was technically strong but unknown to AI platforms. With limited marketing budget and a 2-person marketing team, they needed an approach that could compete with Wiz, Palo Alto Prisma Cloud, and Orca Security in AI recommendations.

GEO Investment

• Built 5 highly detailed "[Competitor] alternatives" pages targeting Wiz, Prisma Cloud, Orca, Lacework, and Aqua Security
• Created a real-time cloud misconfiguration database tracking across AWS, Azure, and GCP
• Published 20 "Best cloud security tools for [specific use case]" pages with honest, balanced analysis
• Implemented llms.txt file and comprehensive schema markup across the entire site
• Launched technical blog with specific benchmarks, deployment guides, and architecture comparisons

Results (120 Days)

• 2% → 28% AI visibility score — from near-invisible to appearing in over 1 in 4 cloud security AI responses
• 5,200 monthly visitors from programmatic SEO portal within 60 days of launch
• 23% of pipeline originated from AI-influenced channels (self-reported + correlation analysis)
• $680K total pipeline generated from content + AI visibility in first 4 months, on $22K total investment

Notable Finding: This case demonstrates that AI-SoV isn't purely a function of company size or market presence. A 45-person startup achieved 28% AI visibility in cloud security by creating the right content structures — outperforming several larger competitors with higher domain authority but weaker content optimization.

5. Case Study Summaries: Five Additional Cybersecurity Companies

6. Cross-Case-Study Patterns

6.1 What Worked Across All Cases

• Comparison tables were the universal catalyst. Every company that created structured feature-by-feature comparison pages saw immediate citation improvements — typically within 2–4 weeks on Perplexity and Google AI Overviews.
• Real-time data portals outperformed static content. Companies that launched dynamic portals (CVE databases, misconfiguration trackers, compliance centers) achieved 3–5× higher citation rates than those relying solely on static blog content.
• Ungating content was essential. Every company that ungated previously gated research saw measurable citation improvements within 30 days.
• Monthly content updates maintained citation position. Companies that established regular update cadences maintained or improved their AI-SoV, while those that published and forgot saw citation decay within 60–90 days.

6.2 Common Challenges

• Attribution gaps frustrated leadership. Even the most sophisticated measurement approaches captured only 35–55% of true GEO-influenced pipeline in standard dashboards. Building executive confidence required a portfolio of evidence, not a single metric.
• Platform inconsistency. Visibility gains on one AI platform didn't automatically transfer to others. Companies needed platform-specific strategies.
• Content quality vs. quantity tension. Early in implementation, some companies prioritized volume over quality. Those that focused on 10–15 exceptionally well-structured pages outperformed those that created 50+ average-quality pages.

6.3 The Time-to-Impact Curve

7. Building the Business Case: Templates and Frameworks

7.1 The Conservative ROI Projection

For cybersecurity marketing leaders presenting GEO investment to CFOs, we recommend this conservative projection approach — using only Layer 1 and Layer 2 attribution (measurable data) and excluding Layer 3 (ambient influence):

7.2 The Competitive Risk Framing

For skeptical executives, the most effective framing isn't "here's what we gain" — it's "here's what we lose by not acting":

• Every quarter delayed = compounding disadvantage. AI platforms develop citation preferences over time. Competitors building AI-SoV now create flywheel effects that become increasingly difficult to overcome.
• The invisible pipeline drain. If 40% of your buyers use AI assistants and your brand isn't recommended, you're losing deals you never knew existed. These buyers don't appear in your "lost opportunity" reports — they never enter your funnel at all.
• The cost of inaction exceeds the cost of investment. At $78K/year for a GEO platform vs. potentially hundreds of thousands in invisible pipeline losses, the risk-adjusted case for investment is strong even with conservative assumptions.

8. Measurement Playbook

Recommended Metrics Dashboard for GEO ROI

9. Frequently Asked Questions

What's the minimum investment to see GEO ROI in cybersecurity?

Based on the case studies, the minimum viable GEO investment that produced measurable results was approximately $18K over 90 days (vulnerability management startup case). This covered platform costs and content creation for 25 optimized pages. Companies investing $30K+ over 90 days consistently saw stronger results across all metrics.

How long before we can demonstrate ROI to leadership?

Layer 1 (direct AI referral) data becomes available within 4–6 weeks. Layer 2 (branded search correlation) requires 8–12 weeks of data. Most companies in this study presented an initial ROI case at the 90-day mark, with comprehensive attribution analysis at 6 months.

Does GEO cannibalize our existing SEO traffic?

In no case study did GEO investment reduce existing organic traffic. In fact, 6 of 8 companies saw organic traffic increases alongside AI visibility improvements, because the content structures that earn AI citations (comparison tables, FAQ schema, structured data) also improve traditional SEO performance.

What's the ROI difference between startups and enterprise cybersecurity companies?

Startups consistently saw higher percentage ROI (due to lower baselines and lower investment levels), while enterprise companies saw higher absolute pipeline values. The Series A cloud security startup achieved 31× ROI on $22K investment; the pre-IPO SIEM platform achieved 12× ROI on $175K investment. Both were strong business cases.

Should we build GEO capability in-house or use a platform?

In-house GEO requires expertise in AI citation patterns, multi-platform monitoring, content structure optimization, and programmatic SEO — skills that are rare and expensive. The case studies show that platform-based approaches (using tools like GrackerAI) delivered faster time-to-value at 75–85% lower cost than in-house builds.

10. About This Research

GrackerAI is the pioneering AI-powered AEO and GEO platform built specifically for B2B SaaS companies. The platform helps businesses get discovered and cited by AI search engines including ChatGPT, Perplexity, Claude, Gemini, and Microsoft Copilot.

Methodology

This compendium documents outcomes from eight cybersecurity companies using GEO strategies during 2025–2026. Company names are anonymized at their request. Pipeline attribution uses the three-layer model described in Section 1. ROI calculations are based on company-reported investment data and pipeline attribution. Results are representative but not guaranteed — outcomes vary based on category, competition, and execution quality.