Clearance Markdown Scheduling with Demand and Elasticity Feedback
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Solution Overview
Problem
Retailers face challenges in accurately modeling item demand and price elasticity for inventory items on clearance in physical stores due to differences in operating behavior and data availability, especially when the item has never been on clearance before, making it difficult to optimize markdown schedules for maximizing revenue.
Innovation Solution
A method and system that utilizes a first model for demand forecast and a second model for estimated price elasticity, built using historical sales data and item attribute matching, with an iterative backtesting process to refine the optimization model, and an automated feedback dataflow for real-time updates during the clearance program.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demand modeling techniques are used for in-store clearance items, then the modeling process is simpler, but the accuracy of demand forecasting and revenue optimization is reduced
Solution Approach 1:
The patent introduces an intermediary automated feedback dataflow that mediates between actual sales data collection and model parameter updates. This feedback mechanism translates raw sales data into actionable model adjustments, improving demand forecasting accuracy without requiring complex manual intervention. The feedback dataflow acts as a bridge that automatically processes the complexity of data integration and model refinement.
Solution Approach 2:
The system implements continuous feedback loops where actual sales data from the clearance program is automatically fed back to update model parameters in real-time. This feedback mechanism allows the demand forecast model and price elasticity model to adapt dynamically to actual consumer behavior, significantly improving forecasting accuracy. The feedback process automatically adjusts markdown schedules based on observed sales patterns, resolving the contradiction between accuracy and complexity.
2Adaptability or versatility
If manual markdown schedule adjustments are made based on observed sales patterns, then adaptability to actual demand is improved, but response time and operational efficiency are reduced
Solution Approach 1:
The system enables self-service automation where the clearance management system automatically monitors sales data, updates model parameters, and adjusts markdown schedules without human intervention. The automated feedback dataflow continuously processes sales information and autonomously optimizes pricing strategies, achieving both high adaptability to actual demand and maintained operational efficiency. The system serves itself by automatically detecting demand patterns and implementing corrective pricing actions.
Solution Approach 2:
The patent implements dynamic markdown schedules that automatically adjust in real-time based on observed sales patterns. Rather than static pre-planned markdowns, the system continuously adapts pricing levels according to actual demand elasticity and sales velocity. This dynamic approach allows the markdown schedule to be as adaptable as manual adjustments would be, while maintaining operational efficiency through automation. The system transitions from static to dynamic pricing management.
3Measurement precision
If comprehensive historical data is collected for items never on clearance before, then model accuracy is improved, but data processing complexity and time requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical sales data in structured formats before clearance events occur. The feedback dataflow is pre-configured to automatically capture and process sales information as it becomes available. This preliminary preparation ensures that when clearance data is collected, it can be rapidly integrated with existing historical data without time-consuming processing delays, maintaining both accuracy and efficiency.
Solution Approach 2:
The automated feedback dataflow maintains continuous data collection and processing throughout the clearance program, eliminating interruptions or batch processing delays. Sales data is continuously captured, immediately processed, and fed back to update model parameters in real-time. This continuous action ensures comprehensive data collection for improving price elasticity estimation accuracy without incurring significant time losses, as the processing occurs continuously alongside sales activities rather than in separate batches.
Data Source
AI summary
Methods and systems for creation and management of a clearance schedule for in-store clearance of a retail item are disclosed. A markdown schedule may be generated using a first model representing a demand forecast and a second model that represents estimated price elasticity for the item to be placed into the clearance program. The estimated price elasticity may be determined from historical sales data of an identified past clearance item. In some instances, backtesting data may be generated from past clearance sales, and a comparison of the backtesting data to the current clearance program may be performed to update the markdown schedule. In some instances, updated sales data may be received, and model parameters updated. A user interface presenting a revised demand forecast may be generated, and a clearance schedule implementation tool may update the optimal markdown schedule for the inventory item for future periods of the clearance program.