Dynamic Pricing Engine for Retail Inventory Segmentation
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Solution Overview
Problem
Retailers face challenges in determining optimal schedules for promotions and markdowns for short-life cycle merchandise like apparel, considering varying customer segments and inventory constraints, which complicates the process of maximizing profits and clearing inventory efficiently.
Innovation Solution
A computerized system that generates separate promotion and markdown price schedules for different customer segments, using a multi-phase process involving allocation, promotion, and markdown logic to allocate inventory and set prices based on demand models and objective functions, allowing for efficient pricing strategies across the regular and clearance seasons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If retailers implement uniform pricing strategies for all customer segments, then operational simplicity is maintained, but profit maximization is compromised due to inability to capture varying price sensitivities across segments
Solution Approach 1:
The patent segments customers into distinct groups based on price sensitivity and demand characteristics. The system divides the customer base into multiple segments and applies different pricing strategies to each segment, allowing retailers to capture varying price sensitivities while maintaining operational manageability through automated segmentation and pricing rule enforcement.
Solution Approach 2:
The patent implements dynamic pricing that automatically adjusts prices based on customer segment characteristics, inventory levels, and demand forecasts. The system dynamically determines optimal prices for each segment rather than using static uniform pricing, enabling profit maximization while the automation maintains operational simplicity.
2Productivity
If retailers use complex multi-segment pricing strategies, then profit maximization improves through tailored pricing, but operational complexity increases making implementation difficult
Solution Approach 1:
The patent implements a self-service system where the automated pricing engine independently performs customer segmentation, demand forecasting, and optimal price determination without requiring manual intervention. The system automatically generates and enforces pricing strategies for different segments, eliminating the operational burden of managing complex multi-segment pricing while capturing its profit benefits.
Solution Approach 2:
The patent replaces manual pricing decision-making processes with an automated computerized system that uses algorithms and data analytics to determine optimal prices. This substitution of mechanical/manual operations with automated computing eliminates the complexity burden on retailers while enabling sophisticated multi-segment pricing strategies.
3Loss of time
If retailers clear inventory quickly during clearance season, then inventory holding costs are reduced, but revenue loss increases due to permanent price reductions
Solution Approach 1:
The patent applies different pricing approaches to different customer segments during clearance season based on their specific price sensitivity and demand characteristics. High price-sensitive segments receive deeper discounts to drive volume, while less price-sensitive segments receive smaller discounts, allowing the system to clear inventory across segments at optimized rates rather than applying uniform markdowns.
Solution Approach 2:
The patent dynamically adjusts pricing parameters for different segments during clearance season rather than applying uniform markdowns. The system changes price parameters selectively across segments based on demand responses and inventory levels, enabling faster overall clearance while preserving revenue by minimizing unnecessary discounts on segments less sensitive to price changes.
Data Source
AI summary
Systems, methods, and other embodiments associated with generating a price schedule are described. An inventory quantity for the item is allocated amongst a plurality of customer segments based on a predicted contribution of each customer segment to the objective function. For each customer segment, based on a quantity of inventory allocated to the customer segment, a promotion portion of the price schedule is determined that maximizes the objective function. Remaining inventory allocated to the plurality of customer segments at the end of the regular season is aggregated. Based on the aggregated inventory, a markdown portion of the price schedule for the item is determined that maximizes the objective function. The promotion portion and the markdown portion are combined to create a price schedule for the item. In one embodiment, a price schedule may be generated that includes promotions on top of markdown prices during the clearance season.