Real-time Assortment for Fashion & Apparel.
Assortment Planning at scale. Ward handles it across every location.
The Assortment capability built for Fashion & Apparel
Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
What changes for your team
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace opportunity detection
- Planogram optimization inputs
Why assortment matters
in fashion retail.
Ward doesn't replace the buyer's eye — it sharpens the math behind the buy. Which store clusters need wider assortment with shallow depth? Which need narrow-deep buys with full size runs? Ward analyzes sell-through by cluster, customer segment, and style attribute to recommend architecture that matches how customers actually shop each location.
Assortment architecture, denim category
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.
What a Ward card looks like.
Cluster B stores (urban, high-traffic) underperforming on premium snacks vs Cluster A by 34%. Assortment gap: 12 SKUs missing.
Fashion assortment:
the shift.
- ×Markdown timing
- ×Size curve misallocation
- ×Style velocity prediction
- ✓Store cluster segmentation
- ✓SKU rationalization recommendations
- ✓Whitespace opportunity detection
Fashion KPI impact.
Ward requires at least 2 full selling cycles to baseline style velocity and markdown timing. Results vary between basics and trend-driven categories.
Questions about assortment.
TLS 1.3, AES-256 at rest. SOC 2 Type II in progress. On-prem available.
Based on store count and data volume. POC engagements at a fixed fee.
SAP, Oracle Retail, Shopify, BigQuery, Snowflake, flat files, and any system with a REST API.
Fashion assortment
by data source.
More Fashion insight cards.
Insights surface
Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action—not just a chart to interpret.
Insights become actions
Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel—mobile push, text, or email. Not every insight needs a ticket. But when one does, it has an owner.
Your team responds
Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal—Ward learns what worked, what missed, and why.
Outcomes measured
Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.
Agents get sharper
Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.
Fashion retailers: see what Assortment problems Ward catches.
Root causes, not just alerts. See it on your data.
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches — and the ones your current tools miss.