A Prime Opportunity to Improve Recommendations
Gymshark’s transformation didn’t stop there, continuously innovating and improving their storefronts. In 2021, the e-commerce retailer added Algolia Recommend to the mix. The results have been remarkable, especially during the critical Black Friday period.
The company had challenges with its previous recommendation solution. Having already had great success using Algolia for a full range of e-commerce capabilities — search, analytics, rules, personalization, dynamic reranking, and more — Ben Pusey, the Software Product Owner at Gymshark responsible for the company’s e-commerce stack, saw an opportunity to evaluate its recommendation capabilities.
Algolia Recommend is a simple, flexible API used to build AI-powered recommendations using as little as six lines of code. It results in, as Gymshark would experience better conversion rates, increased order rate, and improved customer engagement measured, especially on returning users.
In August 2021, Gymshark initially tested Algolia Recommend in its Netherland store. During a two-week period, Gymshark tested Algolia against its previous solution to get a complete picture of how it could improve performance. Through A/B testing on related products on product detail pages (PDPs), the company saw impressive results in this first round of testing:
- A 5.5% increase in order rate for customers who clicked on a product recommended by Algolia Recommend.
- 13% higher order rate from returning customers when Algolia was recommending products.
- 10% higher “add to cart rate” for returning customers with Algolia Recommend.
- Users seeing Algolia recommendations were clicking on more products: 1.4 clicks per user compared to 1.1 with the previous solution.
- A higher conversion rate with Algolia for Gymshark’s top 10 products.
For this test, Gymshark did not test filtering options, such as showing only products of a specific color or products in stock, which would further improve the recommendation quality. Gymshark expects even better as they unlock more of the full potential of Algolia Recommend, such as the customization of recommendations.
Moving testing to all its markets, Gymshark saw similar results, with especially strong increases around mobile customers. Despite lower site performance due to seasonality, the retailer saw significant improvement from users interacting with the recommendations, with order rates on mobile increasing by an astounding 150 percent with the addition of Algolia Recommend.
“We’re confident that Algolia is recommending more relevant results and we’re seeing performance increases because of that,” Christova added.