How Kate Hewko Reduced Returns by 21% Using Loop Return Data

Kate Hewko is a contemporary women's fashion brand known for curated collections and trend-forward apparel, focused on delivering style and quality to fashion-conscious shoppers.

The Challenge

Too many products were competing for attention, leading to diluted engagement and missed revenue opportunities. The original setup factored in conversion rate, sales, and new arrivals but without inventory-aware sorting — and there was no separate strategy per product segment, causing competing parameters to clash.

• Lower visibility for best-sellers
• New products struggling to gain traction
• Inventory-heavy items not getting prioritized exposure

The Solution

Products were divided into four dynamic segments, each with its own rules and performance-based sorting signals:

(1) New products — recently published, sorted for early visibility. (2) Best sellers — above-average sales and conversion, prioritized for high-intent shoppers. (3) Non-moving Inventory — high stock and slower sell-through, strategically surfaced for inventory efficiency. (4) The rest — remaining products, sorted by performance signals.

Key parameters included conversion rate, sales quantity, inventory levels, page views, variants with stock ratios, and returns rate via Loop integration. Loop return data was integrated directly into collection sorting to optimize for products that convert and stay sold.

The Results

Over a 30-day A/B test, the segmented merchandising strategy delivered a 178% revenue increase, a 21% reduction in return rates using Loop data, and a 27.6% improvement in inventory health.