How Optimization Increased Clearance Revenue by 6% and Generated €35 Million in Additional Profit

Most retailers assume clearance pricing is a demand problem. For this global fashion retailer, the bigger constraint turned out to be the cost of changing a price tag.

Every year, a global fashion retailer introduces more than 11,000 new designs across a network spanning over 70 countries.

When the regular season ends, roughly 20% of inventory enters a two-month clearance window. What happens in those eight weeks has an outsized impact on annual profitability. During this phase, the company must balance multiple objectives simultaneously:

• Maximize clearance revenue and profitability
• Accelerate inventory liquidation before the next collection arrives
• Maintain a consistent brand image across markets
• Simplify execution for store operations
• Adapt pricing to local demand conditions

For years, the company managed clearance pricing the way most retailers do: a central team issued markdown recommendations, country managers adjusted them for local conditions, and store employees physically relabeled thousands of garments each week. The process was slow, labor-intensive, and nearly impossible to optimize at scale.

The retailer needed a solution capable of evaluating thousands of pricing scenarios while respecting both commercial and operational, and financial trade-offs simultaneously.

The Hidden Cost of Changing a Price Tag

Before describing the solution, it’s worth pausing on a constraint that shaped the entire project.

Clearance pricing at this scale doesn’t just involve deciding what discount to offer. It involves deciding whether a discount is large enough to justify the labor cost of relabeling thousands of items across hundreds of stores. Too small a markdown, and the effort isn’t worth it. Too large, and you’ve left margin on the table unnecessarily.

This operational reality turned store execution into a pricing variable. The value of a markdown could no longer be measured by demand alone; it also had to justify the operational effort required to implement it. A good pricing decision wasn’t simply the one that maximized sales. It was the one that balanced demand, margin, and execution costs simultaneously.

Combining Demand Forecasting and Optimization to Maximize Clearance Revenue

The methodology was validated through live pilots and large-scale field tests conducted in European markets. Compared to the traditional pricing process, the optimization-driven approach delivered statistically significant improvements in both revenue and profitability while reducing the effort required to manage pricing decisions across markets.

Key outcomes included:

• Approximately €35 million in additional profit during a single clearance campaign
• 4% to 6% increase in clearance revenue
• Faster pricing decisions with significantly less manual effort
• Reduced dependence on manual analysis
• Stronger inventory liquidation performance
• Better alignment between pricing strategy and store operations
• Following the success, the retailer expanded the solution to support its broader clearance pricing process.

Built to Last: Ensuring Long-Term Performance at Scale

Several years after its initial deployment, the optimization platform remained a critical component of the retailer’s pricing operations, supporting large-scale decision-making across multiple markets.

To ensure continued performance, scalability, and maintainability, a comprehensive optimization health check was conducted, reviewing:

• Mathematical model formulation
• Solver configuration and parameterization
• Architecture performance
• Data processing workflows
• Online optimization execution

The assessment identified opportunities to further improve processing efficiency and ensure the platform could continue supporting increasingly complex business requirements, demonstrating that long-term success requires not just an initial deployment, but ongoing care and technical stewardship of the solution.

From Intuition to Optimization: The Future of Retail Pricing Decisions

What made this project successful wasn’t the sophistication of the mathematics alone, it was the ability to balance commercial, financial, and operational objectives within a single decision-making framework. Clearance pricing isn’t simply a demand problem. It’s a demand problem, a margin problem, and an operational problem all at once.

The retailers that will price most effectively in the years ahead won’t necessarily be those with the most aggressive discounting strategies. They’ll be the ones that can successfully balance these competing objectives and act on them consistently at scale.

Clearance pricing is the last lever in a long apparel value chain. The same optimization discipline applies upstream: DecisionBrain has helped a North American apparel company optimize its supply chain and production planning and a global garment manufacturer streamline production scheduling across 11 factories, in both cases matching demand more precisely to production and inventory.