How Gymshark Found Influencers Nobody Else Was Looking For

In 2012, Ben Francis was 19, working at Pizza Hut, and making fitness apparel in his parents’ garage in Solihull. He had no money for celebrity endorsements and no brand equity to trade on. Gymshark was competing against established names with real marketing budgets.

He did have a spreadsheet and a metric that the established brands were not paying attention to.

The Problem With How Everyone Was Choosing Influencers

At the time, the standard logic for influencer selection was simple: bigger audience = more reach = better result. Brands chased follower counts. A fitness account with 500,000 followers commanded a significant fee. An account with 50,000 followers did not.

This logic has a flaw. Follower count tells you how many people could theoretically see a post. It says nothing about how many people actually engage with the content, trust the creator, or act on what they recommend. A 500,000-follower account where 3,000 people comment and interact is functionally different from a 50,000-follower account where 4,000 people do the same thing, and the second account is cheaper.

The metric Francis focused on was engagement rate: the ratio of likes, comments, and shares to follower count. He was looking for accounts where the audience was genuinely active, not just large.

The Filtering Process

Francis and the small Gymshark team identified fitness YouTubers and Instagram accounts and calculated engagement rates across their posts. They were looking for accounts with high engagement rates, often in the 5-10% range, rather than the 1-2% rates common on accounts with large followings that had grown too fast or relied on paid promotion to build their base.

They also looked at the type of engagement. Comments that asked specific questions about the products shown, or referenced the creator’s programming or advice, indicated an audience that trusted the creator’s recommendations and paid attention to what they posted about. Generic comments or emoji responses indicated passive audiences that followed but did not engage meaningfully.

The shortlist they built from this filtering was not full of the biggest names in fitness. It was full of mid-size accounts whose audiences behaved like members of a community rather than passive subscribers.

What They Did With the List

Gymshark sent free product to the people on their list. In exchange, creators would post about the gear if they liked it. There was no guaranteed post, no minimum exposure, and no large upfront fee. The arrangement only made sense if the product was good enough that the creator would organically feature it.

This was also a data point. If a creator received free product and did not post about it, that told Gymshark something about how the creator perceived the brand, and about whether the product-audience fit was actually there.

The creators who did post drove measurable website traffic. Francis tracked which posts generated actual sales, not just impressions. Early data from specific creator posts would directly correlate with spikes in orders. This feedback loop let the team double down on the creators who converted, not just the ones who posted.

The Outcome and What It Tells You About the Metric

By 2016, Gymshark had reached £41 million in revenue. By 2020, the brand was valued at £1 billion, making Francis one of the youngest self-made billionaires in the UK. The influencer network built in those early years was a core part of that trajectory.

The specific numbers are not the point. The more transferable insight is this: the competitive advantage was not access to influencers, everyone had access to influencers. The advantage was using a better filter. Engagement rate versus follower count sounds like a small distinction, but it produces a completely different shortlist. The better shortlist is worth more per pound of influencer spend.

The Broader Pattern

Gymshark’s early influencer strategy is a useful illustration of what data advantage looks like in practice. It was not a sophisticated model or expensive software. It was a spreadsheet, a publicly visible metric (engagement rate), and the discipline to filter by it instead of by the more obvious proxy (follower count).

This pattern applies across marketing channels. The metric that is easiest to measure, reach, impressions, total revenue, is usually not the metric that tells you what you actually need to know. The more useful metric is typically one step removed: revenue per activated customer, engagement per follower, gross margin per acquisition channel.

The brands competing against Gymshark in 2012-2015 were not incompetent. They were using the standard metric. Ben Francis used a better one. That gap closed over time as engagement rate became a standard consideration. But Gymshark built the relationships and brand equity before that happened.

Data advantage has a shelf life. The window for acting on a better metric closes as the market catches up. The value comes from identifying and using it early.