Dynamic Pricing Engine for E-commerce Conversion
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Solution Overview
Problem
In e-commerce, a significant percentage of sales are lost due to impulse purchases not being completed as customers often leave websites in search of better prices or product features, leading to failed conversions.
Innovation Solution
An AI-based method and system that analyzes user search patterns and shopping cart activities to identify price as the reason for failed conversions, generates a dynamic price range with a confidence score, and communicates personalized pricing and promotions to users to encourage purchases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If customers are offered fixed pricing without dynamic adjustments, then pricing simplicity is maintained, but cart-to-order conversion is reduced due to impulse purchases being abandoned
Solution Approach 1:
The patent implements dynamic pricing by adjusting prices in real-time based on customer behavior signals. The system monitors shopping cart activities and automatically modifies pricing to convert impulse purchases, transforming static pricing into a dynamic, adaptive system that responds to customer intent.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring shopping cart activities and using this information to adjust pricing decisions. The feedback loop captures customer intent signals and feeds them back to the pricing engine, enabling data-driven price optimization that improves conversion while maintaining operational simplicity.
2Productivity
If dynamic pricing is implemented to capture impulse purchases, then cart-to-order conversion increases, but system complexity and computational requirements increase
Solution Approach 1:
The pricing system operates autonomously by automatically detecting shopping cart activities and generating price adjustments without human intervention. The system serves itself by using its own monitoring capabilities to trigger pricing decisions, eliminating the need for complex manual oversight while maintaining high conversion rates.
Solution Approach 2:
The patent introduces an intermediary pricing engine that sits between the customer's shopping cart and the final transaction. This mediator analyzes customer intent signals and translates them into optimized pricing decisions, simplifying the overall system architecture by centralizing the complex decision-making logic in a dedicated component.
3Measurement precision
If price adjustments are made without analyzing customer intent, then response time is reduced, but pricing accuracy and effectiveness decrease
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring shopping cart activities and pre-processing customer intent signals before a purchase decision is finalized. This advance preparation enables the pricing engine to generate accurate price recommendations instantly when needed, balancing precision with speed.
Solution Approach 2:
The patent replaces manual pricing analysis with automated computational systems that use algorithms to interpret customer intent signals. This substitution of mechanical human analysis with electronic processing enables both high accuracy and rapid response times by leveraging computational power to analyze patterns and generate pricing decisions in real-time.
Data Source
AI summary
Generation of pricing and promotions for personalized sale of an item includes receiving ordering data associated with an item from a user using an electronic shopping platform accessed via a user device. A failure of the user to complete a purchase order is determined using shopping cart activities of an ordering system. Based on a search pattern of the user received from the ordering system, a price of the item is identified as causing the determined failure of the user to complete the purchase order. Within a predefined time interval from the determined failure, a price range for the item is generated including a confidence score derived based on the search pattern of the user. Based on the confidence score of the generated price range exceeding a threshold, a final price recommendation for the item is generated and communicated to the user device.