Category-Leading AI Visibility for the Biggest Global Online Supplements Retailer

The client sells more vitamins, minerals, and health products online than any other retailer on the planet. Catalogue depth has never been the problem. Visibility at the moment of choice has.

Supplement shopping has quietly become one of the most research-heavy purchases in retail. Before anyone commits to a creatine or a collagen, they want proof: a thread of real users comparing notes, an article that ranks the options, a second opinion from an AI assistant. Those three surfaces now sit between the buyer and the buy button.

Going into the campaign, the retailer was barely present on any of them. Its paid and organic search footprint was strong, but ChatGPT, Gemini, and Google's AI Overviews were naming rival brands when shoppers asked what to take. For a company that leads its category on selection and price, absence from the AI shortlist was a structural risk, not a cosmetic one.

The mandate we were given: get the brand into the conversations and the citations that AI engines draw from, and do it in a way that keeps paying off after the campaign closes.

We treat AI visibility as a downstream effect of source coverage, not a channel you can buy into directly. Models recommend the brands that appear, credibly and repeatedly, in the material they were trained on and continue to crawl. So the work began by reverse-engineering that material.

The first deliverable was not a piece of content. It was a map of the precise questions supplement buyers type into search bars and AI assistants, from blunt commercial queries like "best creatine for men" to the longer, conversational prompts people use when they want a recommendation rather than a list.

From there, two streams of content were built against the same map.

The first was community-led: discussions seeded inside the subreddits where supplement buyers actually gather, framed around genuine questions and written to read like the threads regulars already trust.

The second was editorial: long-form, comparison-driven articles placed on Yahoo Finance and Business Insider, two publishers whose authority AI engines tend to defer to when assembling product recommendations.

The logic connecting the two streams is straightforward. An AI engine that encounters a brand once may ignore it. An engine that encounters the same brand inside a high-traffic Reddit thread and a respected finance publication, both answering the same buyer question, reads it as a category authority. One placement is a mention. Coverage across both is a pattern, and patterns are what models reward.

Subreddit selection was deliberate. Rather than chase the biggest audiences, we prioritised communities with the highest concentration of active buyers: r/nutrition, r/Supplements, and r/Biohackers, all places where members arrive specifically to compare products and pressure-test claims.

Every thread was topic-led, not brand-led. Prompts such as "collagen supplements worth trying," "favorite omega 3," and "is collagen worth taking" mirrored the way buyers phrase their own uncertainty, which is why they drew real participation. More than 400 substantive comments accumulated across the seeded posts, and the threads kept pulling views long after they went live.

The editorial side ran to five placements, each anchored to a high-value buyer keyword such as "best creatine for men 2026" and "best probiotic for women 2026." Every article was structured the way AI systems prefer to consume information: clear comparisons, defined categories, and explicit reasoning a model can lift straight into an answer.

Nothing in the program was written for an algorithm alone. Each thread and article had to earn a human reader first, because content that people genuinely engage with is exactly the content AI engines learn to trust.

Four months after launch, the campaign was still generating visibility with no further input.

The Reddit program returned 468,000+ views and 400+ comments across the seeded discussions. Its strongest performer, a collagen thread, reached 245,000 views and was still adding roughly 35,000 a month a full four months after posting; a second collagen discussion passed 115,000 views, 12,000 of them landing in the most recent month alone. Engagement on that trajectory signals content that Reddit's own discovery systems and Google have picked up, no longer reliant on any promotion.

The editorial placements delivered 40,000+ direct reads and a further 32,000+ pickups from AI engines citing the articles in their responses, against a combined potential reach north of 557 million. On Google, campaign content claimed page-one positions across the full decision journey: discovery queries like "collagen reddit," validation queries like "collagen supplements reddit," and comparison queries like "best creatine for women 2026."

The shape of that growth matters as much as the totals. Visibility did not jump and fade. It accumulated, then kept rising once the campaign ended, because every indexed thread and cited article keeps feeding the system.

The headline outcome is share of voice. By March 2026, the brand was named in more than half of all qualifying Gemini answers about the category, while the rest of the field, a set of well-known supplement retailers, stayed somewhere between 10 and 25%. A lead that wide, in a market this crowded, functions as a defensive moat.

Every other metric moved the same way. Mentions in Google AI Mode passed 80,000, outdistancing every tracked rival and confirming that the campaign's content had been absorbed into the sources these models reach for first. AI impressions in Overviews cleared 20 million, 4× higher than the nearest competitor, with the steepest climbs landing exactly when articles went live and Reddit threads gained traction. Across the major LLMs combined, AI mentions of the brand rose 200% over the campaign window.

The citation data explains why. When we pulled the domains AI engines lean on most when answering questions about the brand, Reddit sat in second place, behind only Healthline and ahead of WebMD, YouTube, and Wikipedia. For a consumer health brand, that is close to an ideal result: the campaign's primary channel doubles as one of the most trusted inputs the models have. Paired with the Yahoo Finance and Business Insider articles, it produced exactly the source mix that earns AI recommendations.

Each of those cited URLs is now a standing reference, ready for an AI engine to draw on every time a relevant question comes up.

The most telling number in the campaign is the one recorded after it finished. Mentions, impressions, and share of voice all carried on climbing through the months that followed, with no additional spend behind them. That is how AI indexing tends to behave: each citation makes the next one likelier, and visibility begins to build on itself.

It runs on a different economic model from paid media. Paid channels only deliver while they are funded. A well-placed Reddit thread or a cited editorial article keeps surfacing for as long as people keep asking the question. As AI assistants settle in as the default first stop for product research, that durability stops being a bonus. It becomes the entire reason the approach works.