Bayesian Network for Customer Return Root Cause Analysis
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Solution Overview
Problem
Existing methods for identifying the root causes of product returns are inaccurate due to customer-entered return reason codes and comments, leading to unfair treatment of sellers and increased frustration, as customers may intentionally or unintentionally provide incorrect information, resulting in noisy signals that confuse sellers and affect their ratings and business practices.
Innovation Solution
A machine learning model, specifically a latent variable Bayesian network, is trained using both customer-selected return reason codes and free-text comments to infer the true underlying root cause of returns, providing estimates and explanations to improve accuracy and transparency, even with a shallow language processing approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customer-entered return reason codes and comments are used to identify root causes, then the process is simple and fast, but the accuracy is low due to intentional or unintentional incorrect information
Solution Approach 1:
The patent introduces an intermediary system comprising multiple analysis components (natural language processing module, sentiment analysis module, rule-based analysis module, machine learning module) that act as a mediator between customer input and root cause determination. This intermediary layer processes and validates customer-entered return reason codes and comments, filtering out intentional or unintentional incorrect information while identifying true root causes, thereby resolving the contradiction between simplicity and accuracy.
2Reliability
If basic customer input is used without advanced processing, then the system is easy to operate, but the seller trust and fairness are reduced due to inaccurate return reason identification
Solution Approach 1:
The system employs self-service mechanisms where the sophisticated multi-module analysis process operates automatically without requiring seller intervention or complex configuration. The natural language processing, sentiment analysis, rule-based analysis, and machine learning components work autonomously to identify root causes, maintaining ease of operation while significantly improving reliability and seller trust through accurate return reason identification.
3Productivity
If customer return information is taken at face value, then the processing is quick and straightforward, but the return rates and negative customer experience counts are inflated due to noisy signals
Solution Approach 1:
The system performs preliminary action by pre-processing and analyzing customer return information through multiple analytical modules before final root cause determination. The natural language processing module, sentiment analysis module, and machine learning module proactively identify and filter noisy signals, extracting accurate return cause information in advance. This preliminary analysis maintains quick processing speeds while preventing loss of accurate information that would occur if customer input were taken at face value.
Data Source
AI summary
Root cause estimation for a data set corresponding to customer returns of a product may use a probabilistic model to associate customer-entered product return data with probability distributions relating to possible root causes for the returns. A particular application relates to applying a Bayesian network to customer-selected return reason codes and customer-entered return reason comments to estimate a probability distribution for root causes of a plurality of returns and uncertainties relating to the probability distribution estimation. A bag-of-n-grams can be used to enable the Bayesian network to process natural language portions of the customer-entered product return data. The output of the model and other data relating to the root cause estimation can be conveyed to a seller of the returned products via a user interface.