Retail & E-Commerce
Our client operates five distinct apparel brands, each acquired or launched over the past decade and each running on a different e-commerce platform two on Shopify Plus, one on Magento, one on a custom-built legacy system, and one on a platform that was being sunset by its vendor entirely.
This fragmentation meant five separate teams maintaining five separate codebases, no shared component library, inconsistent checkout experiences, and critically no shared customer data layer. A customer who shopped two of the brands was treated as two completely separate people, with no ability to build cross-brand loyalty programmes or share inventory intelligently.
The brief was ambitious: consolidate onto a single headless commerce architecture that could serve all five brand storefronts with distinct visual identities, while sharing a unified product catalogue, customer data platform, and the feature leadership was most excited about a shared AI recommendation engine that could learn from behaviour across all five brands.
Each brand had its own peak season (swimwear in spring, outerwear in fall). Migrations needed to be scheduled around each brand's calendar no single cutover date could work for all five.
Despite sharing infrastructure, each storefront needed to look and feel completely distinct shared backend, fully independent frontend design systems.
With five previously siloed data sources, building a unified recommendation model required reconciling inconsistent product taxonomies before any meaningful training could begin.
We adopted a headless commerce architecture: a single commerce backend (catalogue, inventory, customer data, checkout) decoupled from five independent Next.js storefronts one per brand each consuming the same APIs but with completely independent design systems.
We built a shared product catalogue with a reconciled taxonomy across all five brands, and a customer data platform that unified identity across brands so a customer's purchase history with one brand could inform recommendations on another.
We built the first Next.js storefront for the brand with the most urgent need (the platform being sunset), establishing patterns and a shared component library that subsequent storefronts could adapt rather than rebuild from scratch.
The remaining four storefronts were built and migrated on a rolling schedule, each timed to avoid that brand's peak season with the shared component library accelerating each subsequent build significantly.
Once the unified catalogue and customer data platform were live, we trained a recommendation model on the combined cross-brand interaction data the "cold start" problem resolved itself as soon as the data was unified, since each brand's existing traffic provided enough signal once combined.
"We went into this expecting a long, painful migration. Instead, by the time we got to our third storefront, the team was moving faster than our internal teams ever had on a single brand. The shared recommendation engine alone has paid for the entire programme."