The Challenge: Reducing Losses from Overstocked Products
A leading retail chain with hundreds of locations faced a costly challenge—offstocking products efficiently when items were removed from shelves due to internal decisions or external market shifts.
Historically, the company relied on a linear discounting approach, first dropping margins to 0%, followed by an aggressive 75% clearance markdown, leading to significant revenue loss. This one-size-fits-all strategy failed to account for regional demand variations, leaving some stores stocked with unsold items while others ran out too soon. The retailer needed an AI-driven solution to intelligently adjust pricing based on local demand, purchasing behavior, and inventory levels.
The Solution: AI-Powered Price Elasticity Optimization
Digiwit developed a dynamic AI pricing model that optimized clearance sales by analyzing price elasticity at the store level. The AI model:
✅ Analyzed regional purchasing patterns – Identified how customers in different locations responded to varying discount levels.
✅ Predicted optimal pricing strategies – Determined the best discount timeline per store to maximize sell-through rates.
✅ Automated real-time price adjustments – Adjusted markdowns dynamically based on demand, inventory movement, and competitor trends.
✅ Eliminated stock within predefined timeframes – Ensured all remaining stock was sold without unnecessary deep discounts.
💡 Key Insight: Instead of using static discounting, the AI-driven model applied location-based dynamic markdowns, preserving revenue while reducing clearance losses.
The Impact: Increased Profitability & Optimized Inventory Turnover
📉 40% Reduction in Final Clearance Losses – AI-driven pricing strategies prevented unnecessary deep discounts.
📊 Higher Margins Maintained Throughout the Discount Cycle – Adjusted markdowns prevented premature profit erosion.
⏳ Faster Inventory Sell-Through – Products were cleared within the planned timeframe, minimizing unsold stock.
🏬 Store-Specific Optimization – AI ensured discounts were tailored per location, avoiding overstocking in certain regions.
Conclusion: Smarter Discounting, Higher Profitability
By leveraging AI-powered price elasticity modeling, the retailer transformed its markdown strategy—moving from loss-heavy clearance to a data-driven, location-based approach that maximized revenue while efficiently clearing stock.
🔹 Want to optimize pricing and reduce clearance losses with AI? Schedule a demo with Digiwit today!
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