Personalized Delivery Estimates Using Historical Transaction Data
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Solution Overview
Problem
Current delivery estimate systems provide inaccurate time ranges that frustrate buyers, leading to reduced purchases and visits to online sellers due to uncertainty about when items will be received.
Innovation Solution
A personalized delivery estimate system that utilizes historical transaction data, buyer and seller information, item details, and external factors to generate precise delivery date estimates by analyzing similar transactions and adjusting for seasonal and external conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general delivery time range estimate is provided to buyers, then the system is simple to operate, but the measurement precision of delivery time is poor
Solution Approach 1:
The patent segments the delivery time estimation by creating separate estimation paths for different buyer-seller item combinations. Instead of using a single general estimate, the system divides estimates into personalized segments based on historical transaction data, buyer profiles, seller profiles, and item characteristics, thereby improving precision without requiring complete system redesign
Solution Approach 2:
The system performs preliminary actions by pre-collecting and storing historical transaction data, buyer information, seller information, and item information in databases before actual delivery estimation is needed. This pre-processing enables faster, more accurate real-time estimates without adding complexity to the core estimation logic
2Measurement precision
If personalized delivery estimates are generated using historical data and multiple factors, then delivery time measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent creates a universal estimation framework that handles multiple delivery scenarios through a single system architecture. The same core estimation engine processes different types of data (buyer info, seller info, item info, historical transactions) and applies to various buyer-seller-item combinations, reducing overall system complexity despite the personalized nature of estimates
Solution Approach 2:
The system enables self-service by automatically collecting, processing, and analyzing historical transaction data and relevant information without manual intervention. The estimation algorithm autonomously generates personalized delivery estimates by processing stored data, reducing the need for complex manual data management and simplifying system operation
Data Source
AI summary
A personalized delivery estimate system is described. A commercial transaction is generated between a seller and a buyer for an item in an online marketplace. Historical transactions of buyers and sellers in the online marketplace are stored in a storage device. A personalized delivery time estimate is computed for the buyer of the commercial transaction using seller information, buyer information, and item information with the historical transactions of buyers and sellers in the online marketplace.