Gymshark: Scaling Headless Commerce Search for Global Growth With Algolia

How Gymshark used headless commerce and Algolia to transform their website & increase revenue

“We chose Algolia because its APIs are super-easy to work with and very well documented. Also, our goal is very scalable; Black Friday last year we had 450,000 concurrent users on side during the busiest period. Having somebody that can scale up with us is really, really important. And we like how Algolia innovates. We’re always getting new features.”
— Ben Pusey Former Software Product Owner @ Gymshark

Introduction

For Gymshark, the UK's fastest-growing fashion brand, the effects of its remarkable growth drove it to move from its Magento storefront and adopt headless commerce built on Shopify and incorporating best-in-class components. It now has 15 stores worldwide and 64 million customers online, and in 2020 broke $500 million in revenues.

The Black Friday that Broke Gymshark’s Store

Gymshark’s urgent need to adopt headless commerce came after Black Friday 2015. The surge in customers during this prime sales event overwhelmed its existing self-hosted store, resulting in a crash that not only cost Gymshark revenue but also a hard-won reputation.

A decision was made to move to a headless commerce architecture, prioritizing best-in-class, scalable, API-first technologies. From its monolithic store, Gymshark selected Shopify as the foundation on which it built a new storefront. It then adopted leading solutions for search and navigation capabilities (Algolia), front-end interface (ReactJS), and customer relationship management (Contentful), all supported with additional AWS services and tools.

A Serious Need to Improve Product Discovery

Peak customer loads weren’t the only driver for Gymshark’s transformation. The store’s search and discovery functionality had “big problems,” according to Ben Pusey, Software Product Owner at Gymshark responsible for the company’s e-commerce stack.

Poor search results meant best sellers were being buried at the bottom of the page, while out-of-stock products would appear at the top. Low relevance led to poor conversions from search. And the shopping experience lacked any of the personalization that consumers have come to expect. In addition, manual merchandising was intensive and unscalable for the merchandising team. A lack of localization resulted in slow load times for shoppers outside the U.K. and poor search results based on language nuances.

The Solution: Improving Relevance through Algolia

The company turned to Algolia to improve site search, browsing capabilities on its product category pages, to reduce the heavy workflow demands on merchandising, and more. Pusey says Gymshark chose Algolia based on four factors:

Ease of development: Well-documented and easy-to-use APIs.
Scalability: Ability to handle peak traffic during sales events.
Innovation: Continuous introduction of new features and capabilities.
Collaboration: Working closely through beta tests and the development of new features.

Implementation was fast: Gymshark moved from its monolithic self-hosted store to headless in a matter of months rather than years, and it deployed in-house personalization using Algolia in just a quarter. The company is now using Algolia’s entire relevance stack to meet several business goals and looking at how it can take further advantage of it.

Boosting Relevance for a Superb Search Experience

Today Gymshark uses Algolia for site search to unlock incremental revenue by customizing the user experience to their specific needs, audience, and catalog. But its first priority was to improve relevance. They added criteria to their ranking such as product availability, preventing out-of-stock items from showing up first on search pages. They used other business data to fine-tune relevance on a query basis, for example, improving the average click position of products based on its most search keyword “camo”, which drove higher click-through rates. In addition, Gymshark used Algolia’s AI-generated synonyms to show customers relevant products previously being missed. Algolia’s AI detected nuances in language and corrected them.

Algolia’s AI-based merchandising capabilities were used to promote best-selling products based on each individual search query, resulting in an estimated extra £2 million per year in sales.

Increasing Customer Engagement with Algolia Powering Collection Page

The team saw such an impact from Algolia on its site search that it looked to apply its benefits to the customer navigation experience. It now powers its product listing pages (PLPs) and collection pages with the Algolia relevance stack, bringing the benefits of custom ranking, Rules, Dynamic Re-Ranking, and the ability to combine human and AI-led merchandising capabilities.

Gymshark’s merchandising team now uses Algolia’s Visual Editor to define precise merchandising rules based on the company’s business data, improving the customer experience through speed and relevance.

Pusey estimates the company has generated £4.5m a year in extra sales due to these improvements.

Reducing manual workloads with AI Merchandising

Adding AI merchandising capabilities provided a boon for Gymshark internally. By automating merchandising, Algolia’s visual merchandising tool reduced manual labor and helped Gymshark overcome reliability issues that were causing system crashes.

Merchandising has gone from a heavily manual process to “basically set and forget.” It allows the trading team to avoid scrambles and focus attention on other activities to provide value, while scalable and reliable technology prevents the crashes and resulting customer loss it once experienced.

Personalization and Beyond

Gymshark is using Algolia to personalize the shopping experience and build brand loyalty. Initially implementing it across search and collection pages, the company plans to incorporate personalization around specific events, pricing tiers, and more—using deeper data and analytics.

Increased Revenue is Only the Start

Pusey estimates that adopting Algolia for search, customer navigation, and AI-based merchandising has resulted in an astounding $20M incremental annual revenue. By taking a headless and microservice approach, Gymshark is ready to evolve its e-commerce capabilities incrementally. Its next steps include to test KPI-driven merchandising algorithms and implement product recommendations.