Generative Engine Optimization (GEO) Case Study: 3X’ing Leads

This case study demonstrates how Go Fish Digital applied Generative Engine Optimization (GEO) to increase both visibility and conversions inside AI-driven search environments like ChatGPT, Copilot, Google’s AI Overviews and AI Mode. Over the course of just three months, we executed a focused strategy designed to align with how large language models (LLMs) retrieve, ground, and cite content.

The work centered on four key levers:

The results were substantial:

This case study confirms that AI search is not a passing trend—it is already functioning as a new type of search engine and sales channel. By aligning content with the way answer engines process and ground information, we proved that Generative Engine Optimization (GEO) can deliver measurable, bottom-line impact. Of course, using ourselves as the first client to dog-food most of our own defined strategies that can be passed onto clients.

Key Takeaways

Generative Engine Optimization (GEO) Case Study Numbers

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Target Persona Cluster and Prompt Mapping

To succeed in generative search, we began by aligning with the highest-value personas: business buyers researching Generative Engine Optimization (GEO) agencies. These are decision-makers evaluating services like Go Fish Digital’s generative search optimization and answer engine optimization.

The strategy was not just to identify the persona, but to map the full set of prompts and query fan-out variations that buyers would naturally use. Large language models (LLMs) like ChatGPT expand queries into adjacent questions (as described in US11769017B1), so appearing in those expansions is just as important as the head term.

We built a prompt map that included:

Why it worked: By pre-emptively aligning with semantic query clusters rather than individual keywords, we ensured visibility across the entire research journey, not just the first question.

Generative Engine Content Production

Within three months, we produced 5–8 cornerstone assets engineered for AI visibility. These weren’t ordinary landing pages — they were designed to be retrievable, re-rankable, and reference-worthy, directly aligning with how answer engines filter and ground responses.

Each page was structured to maximize:

Why it worked: Answer engines prioritize fact-rich, semantically aligned content over keyword repetition. By designing content that was “too authoritative to ignore,” we increased the likelihood of being cited as grounding material inside AI responses.

Referral Traffic Growth

With our GEO content in place, we saw a +43% increase in monthly visitors from ChatGPT and AI referral sources. This was not traditional SEO growth—it reflected AI-driven discovery, where users were encountering our content through AI Overviews, ChatGPT conversations, and Copilot answers.

The growth is tied to how AI systems expand queries through fan-out and rerank passages. By covering adjacent prompts and semantically related clusters, our content surfaced more frequently in these answer experiences, driving referral traffic that traditional search analytics often overlook.

Why it worked: AI referrals are not random—they’re grounded in semantic mapping and passage-level reranking (e.g., ret-rr-skysight-v3). By ensuring our content matched these signals, we secured consistent AI-driven visibility.

Conversation to Conversion Lift

The most compelling outcome was a measurable +83.33% increase in monthly conversions attributed to ChatGPT and AI referrals. Beyond traffic, this proved that AI search is operating as a sales agent, pre-qualifying users before they even reach our site.

Our data showed that leads from AI referrals converted at a 25X higher rate than leads from traditional search. This is because users arriving through AI citations had already been exposed to our authority inside the AI-generated answer—creating trust before the click.

Why it worked: GEO focuses on semantic authority and fact-density, which makes a brand appear repeatedly in AI answers. This repetition builds trust and credibility within the answer engine itself, resulting in higher-quality leads and stronger conversion rates once users visit the site.

How We Win in Answer Engines: A Case Study Playbook

Here’s how we won in less than 90-days with Generative Engine Optimization (GEO):

Step 1: Prompt Mapping

We started by identifying exactly where we wanted to appear inside AI-driven search journeys. Our target was clear: business-buying personas looking for generative engine optimization services. From there, we built a prompt map that captured not just the core keyword, but the full set of research questions and query fan-out terms buyers naturally use when evaluating agencies.
Impact: This ensured we weren’t just competing for one phrase but were aligned with the entire research funnel of generative search buyers.

Step 2: Setting Initial Benchmarks

To measure real growth, we first defined the baseline. We used:

Impact: This gave us a clear snapshot of AI referral performance at the start and allowed us to measure lift with precision.

Step 3: Developing Highly Authoritative Content

Instead of producing generic pages, we built 5–8 cornerstone assets engineered for LLM knowledge capture. Each page was:

Impact: By creating content that was too authoritative to ignore, we increased the likelihood that LLMs like ChatGPT and Copilot would not just crawl, but cite and ground their answers in our material.

You can see some of those cornerstone assets here:

Step 4: Building Query Fan-Out Pages

With the foundation in place, we expanded into query fan-out. Using proprietary tools, we identified topically adjacent questions that buyers ask after the initial query—everything from follow-up research prompts to competitor comparisons. We then built pages with the same level of insight and structure as our cornerstone assets.

The fan-out pages ended up being topics like:

Impact: This positioned us to capture citations across the entire AI-assisted research journey, increasing not only traffic but also the rate of brand mentions in generative answers.

Related: LLM SEO and how to influence the machine

Why We Didn’t Need Certain Generative Engine Optimization (GEO) Strategies

Not every business starts from the same foundation when entering Generative Engine Optimization (GEO). In our case, we had several advantages that allowed us to bypass strategies that are often critical for brands trying to win in answer engines.

1. We Have Strong Reputation

Our agency already had a solid online reputation, with strong standings across platforms like Google Business Profiles, Yelp, Quora, and Reddit. Because of this, we didn’t need to invest in reputation management or review-building efforts.

Why it matters for others: Since generative search is increasingly neural rather than lexical in the way that it searches, user reviews and community discussions can shape how brands are represented in training data. For businesses without this foundation, reputation-building is a necessary step.

2. We Have Established Authority

Go Fish Digital entered this effort with a long history of recognized expertise in SEO. Our semantic footprint and domain authority were already strong, meaning we didn’t have to spend time building a baseline of trust signals before launching GEO-specific content.

Why it matters for others: For newer or less-established brands, authority-building efforts—such as digital PR, high-value references about your brand, and externally referenced expert-driven content—may be essential before Generative Engine Optimization (GEO) strategies can scale effectively.

3. There Are Existing External Signals

Because our agency is widely discussed, cited, and recognized as an award-winning SEO agency, there was already significant training data surrounding our brand entity. This meant we could go straight into executing Generative Engine Optimization (GEO) levers and see results.

Why it matters for others: For most businesses, external credibility signals—citations, mentions, and third-party validation—are critical inputs for LLMs deciding which brands to cite. Without these, visibility in AI-generated answers may lag.

What This Generative Engine Optimization (GEO) Case Study Tells Us

Here’s what this case study confirms for us:

1. AI Search is the New Search Engine

Generative and AI-driven search behave like any other search engine: once we understand how they retrieve, filter, and rank information, they can be optimized against. The rules are new, but the principle is the same—visibility can be engineered.

2. Higher-Intent Personas Drive Results

Our analysis shows that traffic from AI referrals converts at a 25X higher rate than traditional search. This demonstrates that AI search is already functioning as a sales agent, pre-qualifying and influencing users before they ever click through.

3. Quality Execution + Technology Is the Differentiator

Generative Engine Optimization (GEO) is far less about lexical keyword systems and far more about semantic mapping, fact-density, and audience alignment. The brands that understand their audiences at a deep level—and can deliver the insights those audiences are searching for—will win the most citations and visibility in AI-generated answers.