Impact Measurement Module for Customer Issue Assessment
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Solution Overview
Problem
Organizations face challenges in accurately determining the effectiveness of fixes or actions taken to address product or service-related issues, often relying on manual and unreliable methods such as 'gut feeling' by customer support experts, leading to inaccurate assessments.
Innovation Solution
A system comprising call agent stations and an analysis server that collects and analyzes data on customer interactions, using an impact measurement module to quantify the effect of actions or events by comparing quantification measures before and after the action, and employing categorizers trained through machine-learning algorithms to categorize and prioritize issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual determination of fix effectiveness is performed by customer support experts, then the assessment can be obtained quickly, but the accuracy and reliability of the assessment deteriorates due to reliance on 'gut feeling' and subjective judgment
Solution Approach 1:
The patent replaces the manual, subjective assessment process performed by customer support experts with an automated computer-based system that uses machine learning models and algorithms to objectively measure and determine the effectiveness of fixes. This substitution eliminates reliance on human 'gut feeling' while maintaining rapid assessment capabilities through automated data processing.
Solution Approach 2:
The system implements a feedback mechanism where customer support case data is collected, analyzed, and used to generate quantitative measurements of fix effectiveness. These measurements are then fed back to organizations to inform future problem-solving efforts, creating a continuous improvement loop that enhances both accuracy and reliability of assessments.
2Measurement precision
If automated data collection and analysis systems are implemented to measure fix effectiveness, then the accuracy and reliability of assessment improves, but the device complexity and implementation cost increases
Solution Approach 1:
The patent describes a multi-functional system that can handle various types of customer support data, analyze multiple problem categories, and generate comprehensive effectiveness measurements for different kinds of fixes and events. This universal approach consolidates multiple assessment functions into a single platform, reducing overall system complexity compared to implementing separate specialized systems for each function.
Solution Approach 2:
The system employs machine learning models that automatically learn from historical customer support data and improve their assessment capabilities over time without requiring manual reconfiguration or complex programming updates. This self-learning capability reduces the operational complexity of maintaining and updating the assessment system.
3Reliability
If comprehensive data collection from multiple sources is performed, then the reliability of impact measurement improves, but the loss of time and computational resources for data processing increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing customer support case data in the background before formal impact assessments are needed. Data is aggregated, cleaned, and organized in advance, so when a fix effectiveness measurement is required, the system can quickly retrieve and analyze pre-prepared data rather than collecting everything from scratch at the moment of assessment.
Data Source
AI summary
A categorizer produces a first measure regarding cases associated with an issue. Information regarding additional cases associated with the issue is received after one or more events have occurred with respect to the issue. Based on further output from the categorizer, a second measure is produced regarding the additional cases associated with the issue.