Arriving at the Ideal Price in Just 14 Days
Overview
This luxury footwear retailer is a growing direct-to-consumer (DTC) fashion company recognized for its stylish, high quality and comfortable footwear. The brand boasts a thoughtfully designed collection available in a wide range of sizes and widths, made to fit real women’s lives. Partnering with a family-run factory, the brand focuses on quality craftsmanship, ethical production, and sustainability. Beyond great shoes, their mission includes championing women and creating a product that feels as good as it looks.
Challenge
With every season, the brand releases a new collection of products. With no prior sales data for the new release, they lacked visibility into how customers would respond to various price points. Pricing the product too high risked limiting early traction, while setting it too low could erode margins and dilute brand perception. The company required a dynamic solution capable of quickly understanding true customer demand—one that would not only support profitability and strengthen brand equity, but also scale seamlessly across future product launches without relying on lengthy experimentation cycles.
Solution
Spresso’s price optimization platform stepped in to address these challenges by allocating site traffic to a variety of price points. Spresso tested incremental changes above and below the new product’s standard launch price, measuring real-time customer behavior and conversion data. With each data point, Spresso’s algorithms adjusted accordingly—giving more exposure to the price points that demonstrated higher conversion or stronger profit per session and reducing exposure to underperforming prices.
- Rapid Iteration
- Within hours of the launch, Spresso began capturing key performance metrics—conversion rate, purchase quantity per session, and profit per session—across multiple distinct price tests.
- This continuous feedback loop let the platform quickly eliminate less effective price points, funneling more visitors toward the winning prices.
- Data-Driven Insights
- Spresso’s dashboard revealed that offering a lower price by approximately 6% resulted in a 32% lift in conversion and a 34% increase in profit per session compared to the initial baseline.
- Optimized Allocation
- As Spresso gathered more session data, it dynamically reallocated traffic to the best-performing price tiers. This meant minimal wasted opportunities, since fewer customers were shown prices that did not convert well or that offered insufficient profit.
- This luxury footwear retailer could see, in near real-time, the performance shifts of each price point, simplifying the decision to pivot as soon as a price emerged as the clear winner.
Results
By day 14, Spresso had collected enough data to conclusively identify the optimal launch price—a level approximately 6% below the original standard. This strategic adjustment increased overall conversions by about 32%, while also lifting profit per session by around 34%, all without jeopardizing its margin targets or brand positioning.
Moreover, because Spresso continuously monitors customer behavior to adjust toward the highest-performing price points, the brand didn’t just stumble on a “lucky guess”; they arrived at a sustainable price that reflected real customer demand. Through these quick, data-driven tests, this luxury footwear retailer retained the flexibility to readjust if market sentiment changed or if production costs shifted.
Key Takeaways
Spresso’s dynamic price optimization enabled this luxury footwear retailer to confidently launch its new shoe with minimal guesswork, arriving at the ideal price in just 14 days. By leveraging real-time traffic allocation and actionable performance metrics, the brand quickly discovered how to balance conversion, profit, and perceived value. Beyond this specific launch, this luxury footwear retailer now has a proven framework for rolling out additional new products. Whether fine-tuning new launches, or current products, the company can rely on Spresso’s agile testing platform to guide data-backed pricing decisions—maximizing revenue, meeting customer expectations, and reducing time-to-market uncertainty.
