Price Optimization System for Return-Aware Revenue Maximization
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Solution Overview
Problem
Current price optimization systems for retailers focus on short-term revenue maximization without considering the probability of product returns, leading to increased return costs and inefficient inventory management.
Innovation Solution
A computerized system that determines a price schedule for products by accounting for the probability of returns, allocating inventory among customer segments, and adjusting prices to minimize return probabilities through promotion and markdown strategies, incorporating a per-segment demand model and objective function that maximizes revenue while considering return costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If price optimization systems maximize short-term revenue through promotions and markdowns, then short-term profit increases, but the probability of customer returns increases leading to higher return costs
Solution Approach 1:
The system performs preliminary analysis of return probability before finalizing pricing decisions. By evaluating the likelihood of returns at each pricing stage, the system proactively adjusts prices to prevent high-return scenarios rather than reacting to returns after they occur, thus reducing overall return costs while maintaining revenue optimization
Solution Approach 2:
The system incorporates feedback loops that continuously monitor actual return rates against predicted return probabilities. This feedback mechanism allows the system to learn from past pricing decisions and their return outcomes, progressively refining price recommendations to balance revenue maximization with return minimization over time
2Quantity of substance
If prices are increased to maximize revenue, then profit margins improve, but the probability of customer returns increases
Solution Approach 1:
The system dynamically adjusts pricing parameters based on multiple factors including product lifecycle stage, demand elasticity, inventory levels, and predicted return probabilities. Rather than using fixed pricing strategies, the system modifies price parameters in real-time to find the optimal balance between maximizing revenue and minimizing return risks for each specific product and time period
3Productivity
If aggressive markdown strategies are used to clear inventory, then inventory turnover increases, but total revenue over the product lifecycle decreases
Solution Approach 1:
The system performs preliminary optimization calculations that project total lifecycle revenue under different markdown scenarios before executing pricing strategies. By simulating various markdown timing and magnitude options in advance, the system identifies the optimal markdown schedule that achieves necessary inventory turnover while preserving maximum total revenue across the entire product lifecycle
Data Source
AI summary
Embodiments determine a price schedule for an item by, for each item, receiving a set of prices for the item, an inventory quantity for the item, a per-segment demand model for the item, and an objective function that is a function of the per-segment demand model and maximizes revenue based at least on a probability of a return of the item and a cost of the return. Embodiments allocate the inventory quantity among a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. Embodiments determine a markdown portion of the price schedule for the item that maximizes the objective function, where the markdown portion assigns a series of prices selected from the set of prices for respective time periods during a clearance season for the item.