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Hat Club is an e-commerce retailer specializing in headwear and apparel, known for deep category expertise, product-led merchandising, and a loyal customer base.
As more shoppers turn to AI-powered answer engines to discover products and evaluate brands, Hat Club made an early call. AI search would not be treated as a side project. It would be treated as a real discovery channel.
This case study looks at how Hat Club increased AI search visibility from single digits at onboarding to more than 50% on a consistent basis, with peaks as high as 73%. Along the way, the team began attributing meaningful weekly revenue to AI-driven discovery.
Hat Club entered AI search with intent, not infrastructure.
Leadership wanted to be early to AI-powered discovery, but the team lacked a clear way to measure where the brand appeared, what influenced visibility, or how to improve it. At the same time, confidence in traditional SEO was fading. Organic performance felt uneven, and attribution was unclear. Reporting often blurred the line between paid and organic results.
AI search introduced new stakes. These systems were increasingly summarizing brands for customers. At the same time, the field was still open enough for an advantage to be built.
Hat Club needed clarity more than experimentation.
The team did not see AI platforms as a novelty. They saw them as an early-stage commerce interface. A place where customers form opinions, compare options, and decide what to explore before clicking through to a product page.
That perspective reshaped the goal. Success was not about appearing everywhere. It was about showing up consistently on high-intent prompts, making sure AI responses reflected what Hat Club actually sells, and building visibility that the team could explain with confidence.
Doing that required working at the level AI search actually operates. Prompts, not keywords.
Hat Club evaluated several vendors in a crowded and fast-moving market. Many spoke about AEO but relied on familiar SEO abstractions that made AI search hard to act on.
Cognizo took a different approach. It made AI visibility visible.
The platform showed where Hat Club appeared across AI systems, how that visibility changed over time, and which actions influenced it.
“The dashboard gives us a tool that helps us measure whether the actions we take actually have a relevant outcome.”
— Hat Club Team
That clarity changed the pace of work. AI search stopped being something to watch from a distance and became something the team could manage week to week. Cognizo served as a decision layer by establishing a baseline, tracking visibility at the prompt level, and offering a competitive context that the team could use.
Hat Club approached AI visibility the same way it approaches merchandising. Start with a portfolio. Refine it continuously.
Instead of relying on a static list, the team treated prompts as a living system. They focused on prompts tied to real product categories, seasonal demand, and practical questions customers ask before buying.
When new opportunities surfaced, choices were deliberate. Prompts that aligned with the assortment were activated. Others were rewritten to better reflect how customers shop. Prompts that could not be credibly supported were set aside. This kept visibility grounded in business reality, not theoretical traffic.
The defining feature of Hat Club’s methodology was how they responded to visibility changes.
Rather than treating AI rankings as something to monitor passively, the team used visibility data as a decision engine. Each review cycle focused on three questions:
Prompts with declining or zero visibility weren’t seen as failures. They were treated as diagnostic signals—indicators that something needed attention.
“Those are kind of like the check engine light. If something has dipped or hit zero, we decide whether it’s something we need to act on or something we should move away from.”
— Chris Hayes, E-commerce Specialist
If a prompt mattered strategically, the response was reinforcement:
If a prompt no longer made sense, it was removed and replaced with something higher value.
Over time, this created a clean, high-signal tracking set where nearly every prompt had measurable visibility. As the cadence settled in, a pattern emerged. Consistent publishing led to consistent gains. The system began to compound.
What mattered most was not just the increase itself, but what it enabled.
“Before we started, we were pretty blind. Now we can discuss the topic and present the data.”
— Chris Hayes, E-commerce Specialist
Prompt-level movement gave the team something concrete to show. Executive conversations shifted. AI search stopped feeling abstract and began to earn credibility as a measurable initiative that the team could explain and defend.
Hat Club’s progress did not come from novelty. It came from discipline. The team focused on the prompts that mattered, monitored them closely, and built visibility through steady, deliberate content.
Prompt-level thinking replaced keyword abstraction. Portfolio management reduced wasted effort. A consistent review loop turned data into action.
AI search became operational.
Hat Club does not see today’s AI-driven revenue as an endpoint. The real value has been learning how the channel works before it becomes crowded.
By the time AI-driven commerce reaches maturity, the team will already understand how visibility is earned, which prompts matter, and how content decisions affect outcomes. That understanding is the advantage.
Hat Club built a repeatable system for AI visibility and began seeing early revenue impact as a result. The first step was understanding where the brand appeared and which prompts mattered most.
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