Context
Wardrobe Market is a French multi-brand fashion marketplace aggregating 85 creators and around 5,200 active SKUs. The Shopify Plus store generates 680,000 euros in monthly revenue and runs on a consignment model: products are stored in the central warehouse and creators are paid on sale.
Fashion is particularly exposed to stock issues: strong seasonality, ephemeral collections, unpredictable trend effects and high cost of end-of-season overstock. The stock team (2 people) manages replenishment via a custom ERP and daily Shopify exports.
The challenge
The stockout rate reaches 18%, meaning one in five product searches ends on an out-of-stock page. The revenue impact is estimated at 75,000 euros per month (lost revenue). Conversely, 22% of the catalog is overstocked and will need end-of-season discounting with negative margin.
Sales forecasts are done manually by the team based on the last 4 weeks of history, without accounting for seasonality, Google Trends, marketing campaign effects or new launches cannibalizing existing products.
The team is overwhelmed: managing 5,200 SKUs manually is impossible and replenishment decisions focus on the 200 obvious best-sellers, leaving the long tail neglected.
The Fullmetrix solution
Wardrobe Market connects Shopify Plus to Fullmetrix and activates the ML Forecasts module. Models are trained on 3 years of history and account for seasonality, trend effects and SKU velocity.
For each SKU, Fullmetrix calculates a 30, 60 and 90-day sales forecast with a confidence interval. Automatic alerts flag products at risk of stockout in the next 14 days, sorted by potential revenue impact. The team can thus prioritize the 50 most critical replenishments each week instead of reacting to stockouts.
A second view identifies low-rotation products at risk of becoming overstock. The team can then activate targeted promotions or bundles to sell through before end of season. The module also integrates margin per product to prioritize marketing efforts on the most profitable SKUs.