Customer Feedback Impact Prediction Using CART Decision Trees
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Solution Overview
Problem
Existing methods for analyzing customer feedback data are inefficient and often result in inaccurate data analysis due to the shortcomings of conventional problem assessment methods, which have not been effectively addressed by existing technologies, leading to inefficient analysis and generation of actionable insights from unstructured customer feedback data.
Innovation Solution
A computer-implemented system using machine learning techniques, specifically a classification and regression tree (CART) model, processes unstructured customer feedback data to generate a decision tree that quantifies the economic impact of negative customer experiences, providing actionable insights through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional problem assessment methods are used to analyze customer feedback data, then the analysis process is simple to implement, but the analysis efficiency is low and the accuracy is poor
Solution Approach 1:
The patent replaces conventional mechanical problem assessment methods with machine learning algorithms (CART decision trees, random forests, gradient boosting). These algorithms automatically process unstructured customer feedback data, transforming the manual analysis process into an automated computational system that delivers both high efficiency and accurate insights without requiring complex manual intervention
Solution Approach 2:
The system enables self-service analysis by allowing the machine learning models to automatically ingest, process, and analyze customer feedback data without human intervention. The models autonomously generate insights, identify negative experiences, and compute economic impacts, freeing analysts from manual data processing while maintaining high accuracy through iterative model training and validation
2Measurement precision
If machine learning techniques are used to process unstructured customer feedback data, then the analysis accuracy and insight quality improve, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex analysis task into distinct computational stages: data preprocessing (cleaning, tokenization), feature extraction (identifying negative experiences, sentiments), model training (CART, random forests, gradient boosting), and insight generation (economic impact calculation). This segmentation allows each stage to be optimized independently, improving accuracy while managing computational complexity through modular processing
Solution Approach 2:
The system applies partial action by focusing computational resources on the most critical aspects of feedback analysis - specifically identifying negative customer experiences and their economic impacts. Rather than analyzing all aspects of feedback equally, the model prioritizes high-impact negative experiences, achieving high measurement precision for the most important metrics while reducing overall computational burden
3Loss of information
If large volumes of unstructured feedback data are collected for comprehensive analysis, then the completeness of insights improves, but the time and computational resources required for processing increase
Solution Approach 1:
The patent applies preliminary action through data preprocessing steps that prepare feedback data before main analysis. The system pre-processes text data (cleaning, normalization, tokenization) and pre-trains models on historical data, so when new feedback arrives, the system can quickly process it with minimal computational overhead. This preliminary preparation reduces processing time while maintaining complete insight generation
Solution Approach 2:
The system implements continuous analysis by processing feedback data in real-time or near-real-time streams rather than batch processing. The machine learning models continuously ingest new feedback, update insights, and generate economic impact measurements without interruption. This continuous action ensures complete insights are maintained while reducing time delays between data collection and actionable insights
Data Source
AI summary
Systems and methods are provided for analyzing unstructured data to generate problem statements using retrieval-augmented generation techniques. Unstructured data representative of customer experiences may be obtained and analyzed to generate problem statements using a large language model augmented by a historical problem statement data set. Generated problem statements may include one or more attributes, including an identification of the source quotation from the unstructured data used as the basis for the generated problem statement. Generated problem statements may further include attributes indicating severity. The historical problem statement data set may be updated to include additional generated problem statements.