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Case Study: How a leading European apparel retailer boosted revenue by 2.4% and reduced markdown loss by 2%

Background: Scaling over 400 stores across Europe

A major European apparel retailer, with a presence in over 400 stores across 20+ countries, offers high-quality fashion for the whole family, from clothing and footwear to accessories. With a focus on maintaining revenue while managing large-scale seasonal inventories, the retailer sought a way to improve its markdown strategy and sell-through performance throughout the product lifecycle.

Challenge: Outgrowing traditional markdown strategies 

The retailer wanted to accelerate its growth by optimizing markdown timing and depth throughout the season. However, traditional pricing methods were no longer adequate for managing the increasing complexity of decisions across diverse markets, product categories and customer behaviors.

One of the key issues was determining the right moment and discount level for markdowns to maximize revenue. Excessive markdowns were leading to unnecessary erosion, while delayed or insufficient markdowns risked leaving unsold inventory on the shelves. The retailer also needed to improve sell-through performance without compromising margin. Another major challenge was forecasting demand for new products that had limited or no historical sales data—an area where conventional approaches fell short. 

Solution: AI-powered markdown optimization

After an in-depth evaluation of potential solution providers, the retailer selected invent.ai for its proven success in the fashion retail space and its advanced, AI-powered approach to pricing and inventory management.

Invent.ai tailored its algorithms to fit the retailer’s specific business model. One strategy focused on maximizing the initial effects of markdowns by applying a targeted clearance approach during the early weeks of discounting. For products that didn’t meet expectations after launch, a more adaptive “clear-as-you-go” method was used, allowing for gradual markdowns throughout the season.

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The solution also took into account the markdown aging effect, recognizing that the influence of price cuts tends to decline over time. This helped the retailer plan markdown timing more effectively to maintain momentum in sell-through.

A key strength of the platform was its use of attribute-based forecasting. By analyzing detailed product characteristics, the system could accurately predict how price changes would affect each item—and how similar products might respond. Factors like seasonality, inventory status, promotional events and broken assortments were also incorporated, allowing for more precise decisions at every stage.

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Results: Increased revenue, better sell-through, lower loss

By adopting invent.ai’s markdown optimization platform, the retailer realized significant financial and operational gains:
  • 2.4% increase in overall revenue
  • 6.9% higher sell-through
  • 2% reduction in markdown loss
With the full implementation now in place, the retailer is equipped to accurately forecast demand even for products with limited sales history, determine the optimal markdown path for each SKU over its lifecycle and align pricing actions with real-time inventory and sales data.


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“Invent.ai’s forecasting model—factoring in elasticity, broken assortments, seasonality and promotional events—gives us clarity on the best timing, depth and frequency for markdowns. We’re now able to answer questions like: When should we mark down? By how much? And for which products?”

Director, Merchandise Planning & Controlling, Leading Apparel Retailer