How a Women’s Apparel Brand Increased Margins by 12% with Pricing AI

Background

A direct-to-consumer women’s fashion brand built on Shopify had grown rapidly over the past two years, offering stylish, affordable apparel across dozens of categories. But with rising customer acquisition costs and shifting shopper behavior, pricing pressure mounted.

The brand was relying on historical discounting patterns, competitor observations, and internal instinct to make pricing decisions—spread across spreadsheets, Slack threads, and last-minute changes. With over 800 SKUs and constant seasonality shifts, this approach became unsustainable.

They needed a scalable solution that could help them react to market dynamics while maintaining brand integrity and profitability.

Industry: Women’s Fashion & Apparel (DTC eCommerce)

Product Implemented: Pricing AI and Competitor AI

Key Goals: Implement data-backed pricing that responds to trends, demand, and competition

Manual pricing workflows and inconsistent markdown logic led to:

• Frequent over-discounting that eroded margins
• Poor visibility into how pricing impacted demand at the SKU level
• Merchandising and marketing teams spending hours tweaking prices rather than focusing on strategy

The brand needed to transition from reactive to data-driven pricing—with better intelligence around elasticity, competitive pressure, and timing.

The brand integrated Hypersonix’s ProfitGPT via API to connect with their Shopify store. They activated Pricing AI and Competitor AI modules, which enabled them to:

• Automatically ingest sales, inventory, and customer data
• Analyze historical price-performance and demand curves
• Benchmark against key competitors in real time
• Optimize prices based on elasticity, sell-through targets, and brand goals

The Hypersonix Impact

• Markdowns became intentional, not reactive
• Pricing aligned with demand elasticity and competitive benchmarks
• Merchandising and planning teams reclaimed time to focus on campaign innovation and product expansion
• Seasonal transitions (e.g., Spring-to-Summer) became smoother, with fewer fire sales