Markdown Planning

The goal of markdown optimization is to minimize profit erosion, as many retailers mark items down too deeply. The complexity of markdown planning stems from fluctuating product lifecycles, seasonal demand, diverse assortments, and intense price competition. Traditional manual, spreadsheet-based methods often rely on a single key performance indicator such as “Week Cover,” which overlooks factors like future demand and price elasticity. This limited approach creates excess inventory, high logistics costs, margin erosion, and reliance on flash sales.

Compounding the problem, poor demand and supply planning leads to misplaced inventory and misaligned strategies. U.S. retail inventories rose average around $800 billion through the first half of 2025, according to U.S. Census Bureau data, underscoring the scale of inventory at risk and the need for precise markdown optimization.

AI-powered markdown optimization replaces guesswork with machine learning models that analyze large sets of historical and real-time data. These systems incorporate market trends, sales history, and external factors such as weather and economic conditions. A central concept is price elasticity, which measures how demand responds to changes in price.

AI models—including non-linear approaches such as Support Vector Regression and neural networks—capture complex relationships between price, demand, and external factors. These models identify optimal markdown timing and depth, helping retailers balance margin protection with inventory clearance.

Integration into point-of-sale (POS) systems enables continuous monitoring of inventory and sales data. AI tools automate markdown and clearance processes, reducing manual tasks and minimizing spoilage. Yet adoption requires organizational readiness. Employees need training to adapt workflows, and without a clear process for use and support, adoption rates may lag.

Overall, AI transforms markdown planning from a reactive, manual process into a proactive, data-driven discipline that aligns pricing with business strategy.

Retailers applying AI-powered markdown planning report measurable financial improvements. A UK multi-channel retailer, using Peak’s AI-driven price suggestions on just 15% of its stock, identified savings worth £2.4 million ($3 million), equivalent to 1% of overall turnover.

Global retail clients of Dataiku have achieved uplifts of 17% in margin and 22% in revenue compared with traditional methods. In field tests, stores using AI-driven markdowns generated over 22% higher net revenue than control stores.

Market adoption is expanding as more retailers see markdown optimization cut costs by up to 15%, according to industry analyses. Convenience chain One Stop, for example, implemented AI forecasting in 2022 and reported rapid improvements within four months. Success depends not only on technology but also on readiness to integrate AI into decision-making.

In short, real-world results demonstrate consistent revenue, margin, and productivity gains, validating AI-driven markdown planning across retail formats.

The markdown optimization ecosystem includes enterprise resource planning vendors, pricing startups, and retail planning platforms. Gartner emphasizes that retail price optimization is now a profit driver, requiring unified platforms that account for consumer behavior and competitor pricing.

Key evaluation factors include integration with POS and inventory data, scalability, precision at store and channel level, and vendor support. Retailers must ensure tools balance advanced analytics with ease of use to empower decision-making across teams.

Looking forward, markdown optimization will increasingly rely on real-time, autonomous pricing decisions supported by explainable AI models. Retailers implementing these systems can expect improved return on investment and stronger customer loyalty through more responsive, data-driven pricing.

The following list includes the major solution providers:

• o9 Solutions: Integrated planning platform with machine learning for lifecycle pricing optimization.
• Blue Yonder: AI-powered markdown optimization as part of its retail planning suite.
• Salesforce Einstein: Cloud-based AI pricing optimization integrated with customer data.
• Dataviva: Flexible pricing and promotion planning framework adaptable to complex workflows.
• RELEX Solutions: Unified retail planning combining forecasting, inventory, and pricing optimization.
• Peak.ai: Business-specific AI for in-season promotions and stock clearance.
• Invent.ai: Named in Gartner’s 2024 Market Guide for unified price and markdown optimization.
• Impact Analytics: Uses analytics and machine learning for clearance pricing strategies.
• Solvoyo: Markdown solutions for fashion and apparel, addressing seasonal transitions.
• Fabric Inc: Pricing and promotions engine supporting efficient stock clearance.

Markdown planning is critical to efficient inventory management, but it is only one part of the broader lifecycle. Returns are a reality of retailing, and AI-powered tools can help in both forward and reverse supply chains.