Generative Engine Optimization (GEO) Case Study: 8,337% Growth

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TL;DR (Executive Summary)

1️⃣ We relaunched The Rank Masters (TRM) site ~3 months ago and executed an integrated SEO + GEO program customized for AI answer engines.

2️⃣ In the most recent 90 days (Jun 11–Sep 8, 2025), ChatGPT referrals surged versus the prior period (Mar 12–Jun 9, 2025):

Metric	Jun 11–Sep 8, 2025	Change vs. prior period	Mar 12–Jun 9, 2025 (prior)
Views	675	+8,337.5%	8
Views per active user	48.21	+502.68%	8.00
Avg. engagement time per active user	5m 41s	+2,527.47%	0m 13s
Event count	1,176	+5,500%	21

3️⃣ We published 42 pages in 3 months (12 core pages + 30 long‑tail blogs) using semantic SEO, a modular content system, and a query fan‑out method to deliberately trigger AI recommendations.

4️⃣ Early conversion signals appeared (book‑a‑call/CTA touches), and engagement quality indicates genuine buying‑intent traffic.

💡 Want the same playbook rolled out on your domain? Book a call.

Background & Goals

Context 👉 TRM relaunched its website ~3 months ago with the objective of standing out in AI‑mediated discovery.

Our hypothesis: Generative Engine Optimization (AEO) + semantic content systems would not only sustain traditional organic search, but also unlock referrals from AI assistants (ChatGPT first), with high engagement and bottom‑funnel impact.

Primary Goals

1️⃣ Win incremental sessions via ChatGPT referrals by being recommended in AI answers. 🏅 We got a +8,337.5% increase.

2️⃣ Increase engagement depth (time on page, multi‑page sessions) from AI traffic. 🏅 We got a +2,527.47% increase.

3️⃣ Generate qualified pipeline signals (CTA clicks, book‑a‑call events). 🏅 We got a 48% increase (in the past 28-day comparison).

Measurement Window

Analysis period: Last 90 days (Jun 11–Sep 8, 2025) vs previous 90 days (Mar 12–Jun 9, 2025).
Source/medium filter: chatgpt.com / referral (GA4 Pages & Screens).

Strategy: Built for Humans, Structured for AI

We combined classic SEO with GEO—designing content to be consumable by reasoning models and useful for humans.

1) Semantic SEO System

Topic mapping around entities, attributes, intents rather than keywords alone.
Long‑tail fan‑out from core topics to capture specific questions that AI models surface. (check Google documentation on “AI in Search”).
Internal linking to pass topical authority and help assistants assemble answers.

2) Modular Content Architecture

Each page assembled from reusable blocks:

Problem → Framework → Steps → Proof → CTA modules.
Skimmable summaries (Executive takeaways, TL;DR) for LLM chunking.
FAQ/Q&A sections targeting direct answerability.
Evidence elements (checklists, data points, case snippets) for E‑E‑A‑T.

3) GEO Enhancements (Generative Engine Optimization)

Structured data (FAQ, HowTo, Article, Organization) to clarify role and claims.
Author & byline patterns to support experience/credibility.
Prompt‑native formatting (headings that map to likely Q→A spans).
Clear CTAs embedded in summary zones where AI often excerpts.

4) Query Fan‑Out to Trigger AI Recommendations

For every core concept, we produced a cluster of long‑tail pages that a model could cite or recommend in different reasoning paths.
Pages were designed to be quotable (explicit definitions, numbered steps), verifiable (data references), and combinable (consistent terminology across pages).

Execution: 12 Core Pages + 30 Blogs (42 Total)

Timeline (12 weeks):

Weeks 0–2: Relaunch, IA cleanup, schema, performance baselines, analytics tagging.
Weeks 2–8: Publish core pages (services/solutions, AEO/AI‑search pillars).
Weeks 4–12: Publish 30 long‑tail blog posts; interlink to cores; add FAQs & proof blocks.

Core Page Themes (examples)

answer‑engine‑optimisation
saas‑seo‑agency, saas‑content‑marketing‑services
programmatic‑seo, cro-product-led-content

Long‑Tail Blog Themes (examples)

AI Overview / AEO guides & checklists
Content audit workflows, cannibalization fixes, clustering
SaaS blog ROI and cadence strategy
Brand visibility in AI search and frameworks for CEOs
And more…

Measurement Framework (GA4)

Report: Pages & Screens.
Segment: session source/medium = chatgpt.com / referral.
Comparison: last 90 days vs previous 90 days.
KPIs: Views, Views per Active User, Avg. Engagement Time per Active User, Event Count.

Results (Last 90 Days vs Previous 90 Days)

Views: 675 (+8,337.5%)
Views per active user: 48.21 (+502.68%)
Average engagement time per active user: 5m 41s (+2,527.47%)
Event count: 1,176 (+5,500%)

What This Means

High depth per user from ChatGPT referrals: almost 50 pageviews per active user suggests strong topical exploration and effective internal linking.
5m+ engaged time indicates content quality and intent match.
Event activity shows users are interacting with CTAs, downloads, and navigational elements; initial conversions appeared in the period.

Top Destinations from ChatGPT (examples)

/ (homepage), /blog, /contact‑us
/saas‑seo‑agency, /saas‑content‑marketing‑services, /answer‑engine‑optimisation
Blog slugs touching GEO, content audits, ROI, and AI‑era frameworks (e.g., ai‑overview‑seo‑bofu‑case‑study, content‑audit‑checklist‑b2b‑saas, optimizing‑for‑google‑ai‑overview, best‑tools‑tracking‑brand‑visibility‑ai‑search).

⚠️ Note: Exact page‑level counts are available in GA4; above list reflects destinations visible in the filtered Pages & Screens table for chatgpt.com / referral.

Why It Worked

Entity‑first coverage: We targeted the concept graph assistants traverse, not just keywords—so models could reliably select our pages to support varied answers.
Answerable page design: TL;DRs, FAQs, numbered frameworks, and outcome‑focused intros created model‑friendly chunks that are easily recommended.
Consistency across a cluster: Terminology, definitions, and framing were uniform across 42 pages, improving perceived authority and intra‑site reasoning.
CTA placement in excerpt zones: Modules adjacent to likely snippet regions improved engagement and event count without harming readability.
Technical cleanliness: Schema, performance, and IA reduced friction for both humans and crawlers/assistants.

Lessons & Optimization Opportunities

Scale the fan‑out: Keep expanding long‑tails where we see assistant traction; double‑down on How/What/Framework intents.
Surface proof earlier: Insert micro‑case proofs in top sections to increase lead intent from AI traffic.
Richer author signals: Add author expertise profiles, speaker clips, and outbound citations to strengthen E‑E‑A‑T.
Event mapping: Ensure book‑a‑call and micro‑conversions (guide downloads, email clicks) are tracked with explicit UTM parameters for AI channels.

Want To Get The Same Type of Results on Your Site?

Want to unlock AI‑assisted referrals and compound organic growth the way TRM did? We’ll deploy the same SEO + GEO + semantic system on your domain, build your entity graph, fan‑out queries, and ship modular, answerable content that assistants love to recommend.

👉 Book a free 30‑minute strategy call.

Let’s map your opportunity and show you what 90 days can do.