Context-Aware Item Return Prediction With Multivariate ML
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Solution Overview
Problem
Conventional methods lack systematic analysis of key driving forces behind consumer product returns in e-commerce, leading to inefficient management and substantial losses for retailers and manufacturers.
Innovation Solution
A method and system using multivariate Machine Learning (ML) models to analyze historical transaction data, generate return, similarity, and linkage spreads, and train a multivariate multiple binary ML model to predict potential returns by mapping return status with similarity and linkage spreads, adjusting cut-off values for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional return prediction methods are used, then the system is simple to implement, but the prediction accuracy is low and cannot systematically analyze key driving forces behind returns
Solution Approach 1:
The patent segments the return prediction problem into multiple components: return spread (actual return data), similarity spread (item similarity metrics), and linkage spread (association rules between items). This segmentation allows systematic analysis of different driving forces behind returns while maintaining manageable complexity through modular processing of each component.
Solution Approach 2:
The patent transitions from conventional single-dimensional return rate prediction to a multi-dimensional analysis framework that incorporates item similarity, purchase patterns, and contextual factors. By adding these additional dimensions, the system achieves comprehensive analysis of return drivers while using structured spreads to organize the complexity.
2Measurement precision
If multivariate ML models with dynamic adjustment are used, then the prediction accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing return spreads, similarity spreads, and linkage spreads from historical data. This pre-processing allows the multivariate ML model to operate on prepared data structures during prediction, reducing real-time computational burden while maintaining high accuracy through dynamic cut-off value adjustment.
3Adaptability or versatility
If liberal return policies are implemented, then customer loyalty and purchase motivation are improved, but substantial losses occur for retailers and manufacturers
Solution Approach 1:
The patent implements feedback mechanisms by using actual return data to continuously update and refine the return spread, similarity spread, and linkage spread. This feedback loop allows the system to learn from past returns and improve prediction accuracy, enabling retailers to maintain flexible return policies while identifying and preventing high-risk return scenarios through data-driven insights.
Data Source
AI summary
The present disclosure estimates items to be returned based on nature of items picked online in association with the context. The context includes time, shopper details, local events and the like. It is addressed by mapping return spread and similarity spread or linkage spread in unique format. Further, the intrinsic mechanism that result returns are captured by training a multivariate Machine Learning (ML) model using the actual return spread, the similarity spread, or the linkage spread and the customer profile data. The captured return mechanism is leveraged to pre-empt the returns online in the form of return spread at the time of ordering in real time.