Instead of accepting the high transaction fees and rigid UX of standard e-commerce platforms, NextGirl was built on a headless WooCommerce architecture. It combines robust inventory management with a completely custom Next.js frontend, enabling a unique size recommendation engine.
The Context
Most D2C fashion brands are either built on Shopify (fast but expensive at scale, limited customisation) or on custom platforms (full control but slow to launch). The requirement was a brand that could launch quickly, handle Indian payment methods natively, support a custom size recommendation flow to reduce returns, and scale without compounding transaction fees.
The Approach
Built on a Next.js frontend with WooCommerce acting as the headless commerce backend. This provided the speed and custom UX of a bespoke build, while leveraging a mature engine for inventory, discount rules, and order management. The custom frontend allowed for the integration of a unique, measurement-based size recommendation tool directly on the product detail pages.
Consistent sizing across inconsistent supplier standards.
Size recommendation across a product catalogue sourced from multiple suppliers with differing size charts. The architecture had to map product-specific measurement tables to a unified customer profile, ensuring accurate recommendations even when a 'Medium' in one dress measured differently than a 'Medium' in another.
500+ orders fulfilled in the first operational year. The measurement-based size recommendation flow resulted in a 40% lower return rate compared to industry averages for women's apparel. Operating costs per order remain 60% lower than an equivalent volume store on Shopify.
Engagement Meta
Technology Context
Quick Navigation
FRONTEND
API LAYER
SERVICES
DATABASE
INFRA
Architectural Decisions
Why this specific stack was chosen over standard defaults for this build.
Size standardisation is a supply chain problem first.
No amount of engineering fixes inconsistent supplier sizing. The measurement-based recommender works because each product has its own measurement chart — not because the algorithm is clever.
Headless is right when the UX is the differentiator.
If the checkout and browse experience are standard — use Shopify directly. If the customer experience is a competitive advantage worth owning — headless pays for itself at 500+ orders a month.
Platform fee arithmetic matters from order one.
At 500 orders/month, Shopify's 2% transaction fee on a Rs.1,200 average order value is Rs.12,000/month. At 2,000 orders, it is Rs.48,000. Owning the platform eliminates that compounding cost permanently.
The Final Result
500+ orders fulfilled in the first operational year. The measurement-based size recommendation flow resulted in a 40% lower return rate compared to industry averages for women's apparel. Operating costs per order remain 60% lower than an equivalent volume store on Shopify.
Size standardisation is a supply chain problem first.
No amount of engineering fixes inconsistent supplier sizing. The measurement-based recommender works because each product has its own measurement chart — not because the algorithm is clever.
Headless is right when the UX is the differentiator.
If the checkout and browse experience are standard — use Shopify directly. If the customer experience is a competitive advantage worth owning — headless pays for itself at 500+ orders a month.
Platform fee arithmetic matters from order one.
At 500 orders/month, Shopify's 2% transaction fee on a Rs.1,200 average order value is Rs.12,000/month. At 2,000 orders, it is Rs.48,000. Owning the platform eliminates that compounding cost permanently.