Decision Tree Model for Customer Feedback Economic Impact

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Solution Overview

Problem

Customer feedback data is often unstructured and voluminous, making it difficult to generate actionable insights about the financial impact of negative customer experiences on businesses.

Innovation Solution

A computer-implemented system using a classification and regression tree (CART) model and decision tree algorithm to process customer feedback data, identifying types of negative experiences and calculating their economic impact by analyzing frequency and financial impact, and visualizing the results through a graphical user interface.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If customer feedback data is collected comprehensively to capture all negative experiences, then the completeness of data coverage is improved, but the complexity of data processing increases due to unstructured and voluminous nature

Engineering Contradiction:

Improvecompleteness of data coverageVSAvoidcomplexity of data processing

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent introduces machine learning models as intermediary components that automatically process unstructured customer feedback data. These models serve as mediators between the raw data and the analysis system, converting unstructured text into structured insights without requiring manual intervention, thus maintaining complete data coverage while reducing processing complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual data processing methods with automated machine learning systems. Instead of human analysts manually examining unstructured feedback data, the system uses trained models to automatically classify, analyze, and extract insights from the data, significantly reducing the complexity of processing voluminous unstructured information

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If manual analysis of customer feedback is performed to ensure accuracy, then the precision of insights is improved, but the time required for analysis increases

Engineering Contradiction:

Improveprecision of insightsVSAvoidtime required for analysis

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent implements preliminary action by pre-training machine learning models on historical customer feedback data before deployment. These pre-trained models are ready to immediately analyze new feedback with high precision, eliminating the need for time-consuming manual analysis while maintaining accurate insights through the preliminary training phase

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables self-service analysis where the machine learning models automatically process and analyze customer feedback without requiring manual intervention. The models independently extract insights, classify experiences, and generate reports, significantly reducing analysis time while maintaining precision through the models' learned patterns from training data

Inventive Principle:

Principle #25Self-service

3Measurement precision

If detailed processing of all feedback data is performed to identify specific negative experiences, then the accuracy of economic impact calculation is improved, but the computational resources required increase

Engineering Contradiction:

Improveaccuracy of economic impact calculationVSAvoidcomputational resources required

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent applies segmentation by dividing the customer feedback data into distinct categories and types of negative experiences using machine learning classification. This segmentation allows the system to process different types of feedback through specialized analysis paths, improving the accuracy of economic impact calculations for specific experience types while reducing overall computational resources by avoiding uniform detailed processing of all data

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements local quality by applying different levels of processing detail to different segments of feedback data based on their characteristics. High-impact negative experiences receive more detailed analysis while less critical feedback receives streamlined processing, thereby improving accuracy where needed while conserving computational resources overall

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20230162214A1System and method for predicting impact on consumer spending using machine learning

Publication Date: 2023.05.25 VERDE GRP INC

AI summary

Systems and methods are provided for computing economic impact of customer experiences. Aspects of such systems and methods include: maintaining a data set including a plurality of types of negative customer experiences; maintaining a tree model for predicting economic impact of the plurality of types of negative customer experiences; receiving feedback data reflective of customer experiences; generating a decision tree based on the tree model, the data set and the feedback data, the decision tree having a plurality of internal nodes with each internal node corresponding to a type of the plurality of types of negative customer experiences; computing economic impact of at least one of the types of negative customer experiences using the generated decision tree and the feedback data; and causing to render, at a display screen, a graphic user interface visualizing the computed economic impact of at least one of the types of negative customer experiences.