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Founded in 2012, Gymshark is the fastest-growing fitness fashion brand in the UK, selling apparel and accessories internationally through 15 online stores. Gymshark crossed the $500 million revenue mark in 2020 and saw 64 million online shoppers, who visited more than 1 billion pages.
Ben Pusey, Gymshark product manager responsible for the entire commerce stack, shared with us the company’s digital transformation journey to MACH architecture and how it has fueled incredible growth.
It all started with Black Friday 2015. Gymshark was running on a self-hosted Magento store, which was failing under the load. During this event, Gymshark lost money and damaged its reputation. Management was pushed to completely rethink the technology from the ground up with a critical philosophy in mind: “We realized that our tech was a core driver of our business success,” says Ben. “We needed to refocus the tech to grow with the business.”
With this in mind, they chose to adopt four core principles in creating their future tech stack:
Popularized by the MACH Alliance, these four principles are designed around creating modular, scalable, and flexible products that can be adapted to reflect changing business priorities over time.
Gymshark chose a MACH stack comprised of composable elements
Search was the first component of the Shopify foundation that Gymshark replaced with a best-of-breed technology.
Before Algolia, Gymshark was encountering various limitations with its search and navigation experience:
Search and navigation were clearly components of the shopping experience that could quickly unlock significant additional revenue, so Gymshark decided to look for a solution that would reflect its four core principles. We are honored that Gymshark chose Algolia, and are proud to have helped Ben and his team build their new search and navigation experience in just a few months.
One element Ben and his team like about their headless search and navigation solution is that it has delivered results from day one while allowing them to unlock incremental revenue by customizing the user experience to their specific needs, audience, and catalog.
Gymshark’s first area for optimization was its search experience. The first priority was to improve relevance. The team added product availability and other business data to the ranking logic and improved the average click position and click-through rate for one of their most-used search keywords, “camo.”
Gymshark also leveraged Algolia’s AI-generated synonyms to show customers products relevant to their search words. For instance, US customers searching for “sweatpants” (as opposed to the UK version, “joggers”) were getting no results. Algolia AI detected this glitch and suggested synonyms.
Finally, they used built-in machine-learning automated merchandising to adapt the order of results for each search query and boost the highest-performing products. By doing this, they generated £2M a year in extra sales.
Collection pages were also among the first site elements Gymshark moved off of Shopify templates to a React app. The transfer was fueled by content delivered and managed with Algolia.The rationale was that Ben and his team were enjoying the search capabilities and wanted to apply them to the navigation experience. They wanted the merchandising team to be able to leverage Algolia’s visual merchandising tool on collection pages to define precise merchandising rules, while relying on Algolia AI and broader business rules (using Gymshark’s business data) to automatically merchandise across the entire store.
For instance, by prioritizing items with the most sizes in stock, hiding out-of-stock products, and using priority scoring to rank products on collection pages, Gymshark generated £4.5m a year in extra sales.
This strategy also produced significant benefits for the merchandising team. With the former solution, they had needed to manually rerank products and hide out-of-stock items on the fly, which was cumbersome (and impossible during large events such as Black Friday). Today, everything is automated.
Gymshark also uses Algolia to personalize the shopping experience across the entire website, starting with search and collection pages and expanding to the home page, then encompassing outbound email and product recommendations, as well as its planned mobile app.
In the course of a year, Ben and his team were able to bring all of these optimizations to Gymshark’s online experience. They followed a lean process that comprised several iterations, with the goal of delivering business value in each stage and measuring improvement in order to reprioritize the road map.
Using a headless search and navigation solution has allowed Gymshark to add significant revenue over the course of several iterations, each optimizing the shopping experience and differentiating it from those of competitors. A critical aspect was being able to hold down the search and navigation fort even during massive traffic and transaction spikes such as those resulting from Black Friday — without any burden to their engineering team.
Gymshark’s MACH-empowered approach to search and navigation has brought it:
But Gymshark isn’t stopping with these numbers. For search and navigation, its next steps are to test KPI-driven merchandising algorithms, apply machine learning reranking to collection pages, test new personalization strategies, and implement product recommendations, all while launching a mobile app benefiting from all those capabilities. For the rest of the stack, Gymshark is gearing up to implement a new product information management system.