ecommerce_ops · ecommerce · workflow
Gymshark adds Algolia Recommend to boost Black Friday order rates by 150%
Gymshark's previous recommendation solution required extensive manual configuration and constant upkeep, creating operational burden and limiting the team's ability to improve recommendation quality efficiently.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Customer visits product page
Customers land on product detail pages where Algolia Recommend surfaces related products.
Tools used
AlgoliaAlgolia RecommendShopify · partnerContentful · partnerReactAWS
Outcome
Algolia Recommend delivered a 150% increase in order rate and 32% increase in add-to-cart rate with new users on Black Friday, a 13% higher order rate for returning customers, and reduced manual configuration burden on the IT team.
What failed first
The prior recommendation solution demanded so much manual configuration and constant upkeep when making changes that the team spent significant effort on maintenance rather than improving recommendation quality.
Results
Time saved150%
Volume32%
Running since2021
Grounding & classification
Source type: vendor customer story
31 fields verified against source quotes.
personalizationrecommendation systemproduct catalogfailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedretailconversion increaseemployee productivityvendor customer storyecommerce opsdata sync enrichment