E-Commerce Markdown Pricing Using Demand Forecasts and Elasticity
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Solution Overview
Problem
Existing price markdown systems for e-commerce platforms lack the ability to dynamically and automatically adjust prices based on real-time data, particularly for items with insufficient historical data, leading to inefficient inventory clearance.
Innovation Solution
A system utilizing machine learning models to predict demand and generate markdown prices, incorporating price elasticity when available, and employing decay rates when data is lacking, with bounded prices to ensure effective inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing price markdown solutions for physical stores are applied to e-commerce, then the system structure remains simple, but the ability to dynamically adjust prices based on real-time data is lost
Solution Approach 1:
The system implements dynamic price adjustment by continuously monitoring real-time data features (sales velocity, inventory levels, customer behavior) and automatically modifying prices based on predefined rules and machine learning models. The pricing engine can respond to changing market conditions within minutes, transitioning from static to dynamic pricing operations.
Solution Approach 2:
The pricing system operates autonomously by self-adjusting prices based on collected data and predefined strategies. The machine learning models automatically learn from historical data and refine pricing decisions without requiring manual intervention, enabling the system to serve itself in optimizing inventory clearance.
2Productivity
If multiple price adjustments are made during markdown period, then inventory clearance efficiency improves, but the complexity of managing multiple pricing rules increases
Solution Approach 1:
The pricing strategy is segmented into multiple adjustable parameters including price elasticity, decay rates, and promotion intensity. Each parameter can be independently optimized and controlled. The system divides the markdown period into multiple adjustment cycles, allowing different pricing strategies to be applied at different stages of inventory clearance.
Solution Approach 2:
The system manages complexity by changing key parameters such as price elasticity coefficients, decay rates, and promotion intensity levels rather than creating entirely new pricing rules. These parameter adjustments allow multiple price adjustments to be made systematically while maintaining a consistent pricing framework that reduces management complexity.
3Measurement precision
If price elasticity data is collected for all items, then pricing accuracy improves, but data collection and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing default price elasticity values and decay rates for cold start items before they have sufficient historical data. These preliminary parameters allow immediate pricing decisions for new items while the system continues to collect and learn from actual sales data, gradually improving accuracy without data gaps.
Solution Approach 2:
The machine learning models act as intermediaries between limited data and pricing decisions. For items with insufficient historical data, the models use surrogate data from similar products or general market patterns to infer price elasticity, bridging the gap between data scarcity and pricing accuracy requirements.
4Productivity
If automated pricing decisions are implemented, then operational efficiency improves, but the ability to handle edge cases and exceptions decreases
Solution Approach 1:
The system incorporates feedback mechanisms where automated pricing decisions are continuously monitored and adjusted based on actual sales performance and market responses. This feedback loop allows the system to learn from edge cases and refine its automated decisions, gradually improving its ability to handle exceptions while maintaining high operational efficiency.
Data Source
AI summary
Systems and methods for dynamically and automatically updating item prices on e-commerce platform are disclosed. In some embodiments, an item is offered for purchase with a current price on a website. When price elasticity data and predicted demand data for the item are both available, a first markdown price is generated for the item using a first model based on: the price elasticity data, the predicted demand data, and the current price. When the price elasticity data and the predicted demand data are not both available, a second markdown price is generated for the item using a second model based on: a decay rate, the current price, and availability of the predicted demand data. A bounded price is generated by applying an upper bound and a lower bound to either the first markdown price or the second markdown price; and transmitted to a computing device for updating the current price of the item on the website.