Automated Markdown Optimization for Retail Pricing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Retailers face challenges in manually determining the optimal time and amount for product markdowns, leading to inefficiencies and increased costs, as well as difficulties in selecting the right items and stores for markdowns.
Innovation Solution
A system and method that utilize historical product data and market inputs to identify products for markdown, generate optimal markdown timing and values, and prioritize markdowns based on these calculations, while also considering user-specific constraints and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual review and determination of markdown parameters is performed, then flexibility and control over markdown decisions are maintained, but operational burden increases and markdown costs rise
Solution Approach 1:
The system enables self-service through automated markdown parameter determination. The markdown optimization module automatically analyzes product data, demand forecasts, and inventory levels to generate optimal markdown recommendations without requiring manual review, thereby reducing operational burden while maintaining control through configurable parameters and approval workflows.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. The system uses algorithms to process product data, calculate optimal markdown parameters, and generate recommendations, substituting human manual analysis with automated computational methods to improve efficiency while maintaining decision quality.
2Ease of operation
If manual markdown determination is performed, then control over markdown timing and percentage is maintained, but markdown costs increase and sales are lost
Solution Approach 1:
The system implements feedback loops where markdown performance data is continuously collected and analyzed. The markdown optimization module uses this feedback to refine future markdown recommendations, learning from actual outcomes to improve timing and percentage decisions, thereby reducing lost sales and optimizing markdown costs while maintaining control.
Solution Approach 2:
The system performs preliminary analysis and optimization before markdown execution. By pre-calculating optimal markdown parameters based on forecasted demand and inventory projections, the system determines the best timing and percentage in advance, preventing suboptimal markdown decisions that would result in lost sales or excessive costs.
3Productivity
If automated markdown optimization is implemented, then operational efficiency and revenue optimization improve, but system complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional markdown optimization platform that handles product identification, parameter optimization, timing determination, and performance tracking within a single integrated system. This consolidates multiple functions into one coherent architecture, improving efficiency without proportionally increasing complexity.
Solution Approach 2:
The patent applies segmentation by dividing the markdown optimization system into distinct modular components: product identification module, optimization parameter determination module, timing module, and execution module. This modular architecture manages complexity by organizing functions into separate, manageable segments that can be independently developed and maintained.
Data Source
AI summary
System and methods for controlling product price adjustments are disclosed. In some embodiments, a disclosed method includes: identifying, based on the historical product data, one of more products from the plurality of products for implementation of a markdown, receiving, from a user interface, a plurality of market inputs associated with the one or more products, generating an optimal time period and optimal markdown value associated with the markdown of the one or more products, and mapping the one or products to a prioritization indication based on the optimal time period and the optimal markdown value.