Assortment Planning + Shopify + Fashion Retail

Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. For Fashion retail specifically, Ward monitors 15,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.

Ward tracks Sell-through rate, Markdown %, Return rate, Style velocity, Size accuracy at the store-category level. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.

OAuth-based connection. Ward reads via Shopify Admin GraphQL API. Real-time webhooks for order and inventory events. Data points include: Orders & line items, Product catalog, Inventory levels, Customer profiles, Discount usage, Fulfillment data.

Yes. Ward reads Shopify data and combines it with contextual signals (weather, events, demographics) to generate Fashion-specific insight cards. No custom development required.

Ward tracks assortment width vs depth by cluster, style attribute performance, size curve accuracy, and inter-style cannibalization. It measures revenue-per-option to identify when adding more styles dilutes overall performance.

A denim buyer has 200 styles to allocate across 90 stores. Ward reveals that urban flagships convert best with wide assortment at shallow depth, while suburban stores need fewer core styles with full size runs. The current uniform allocation starves variety in urban stores and creates size gaps in suburban ones. A cluster-specific matrix reduces markdown risk while lifting full-price sell-through.

First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish robust baselines for your specific operation.

No. Ward sits on top of your existing stack. It is the proactive intelligence layer that watches your data continuously and delivers insight cards — so your team acts on findings instead of hunting for them.