Boutique Retailer Reduces Overstock by 35% with Demand Analytics
The Problem
A women's boutique in Whitby was making buying decisions based on the owner's intuition and supplier recommendations — not data. The result: racks full of slow-moving SKUs that sat for months while popular items ran out mid-season. Cash was tied up in dead inventory. Markdowns were eating margins. And the owner had no visibility into which items were actually driving revenue.
Key Pain Points
- Cash tied up in slow-moving inventory while best-sellers went out of stock
- Buying decisions based on gut feel and supplier incentives, not data
- Seasonal items arriving too late or in the wrong quantities
- 15–20% of inventory sold at a markdown in every season
Our Approach
We built a demand analytics dashboard that pulls sales data from Shopify and POS, calculates velocity by SKU, category, and season, and generates reorder alerts and buy quantity recommendations. The dashboard highlights the top 20 fast-movers, flags the bottom 20 slow-movers, and generates a weekly buying brief the owner reviews in under 10 minutes.
Data Integration
Connected Shopify and in-store POS data into a unified dashboard — full SKU-level sales visibility from day one.
Velocity Scoring
Built a velocity scoring model that ranks every SKU by sales speed, margin contribution, and seasonal trend.
Reorder Automation
Set up automated reorder alerts when fast-moving SKUs hit a threshold — triggered 4 weeks before a likely stockout.
Weekly Buying Brief
Auto-generated a weekly buying brief: top performers to reorder, slow movers to discount, and seasonal buy recommendations.
Results Dashboard
Inventory Health Score — Before vs. After
Key Outcomes
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