Real-Time Customer Satisfaction Scoring via Interaction Topic Analysis
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Solution Overview
Problem
Current methods for measuring customer satisfaction are inadequate as they rely on aggregated, lagging indicators and are not true reflections of global customer satisfaction, as they are typically surveyed after the fact and not completed by all customers.
Innovation Solution
A customer management system that scores end user interactions based on user-defined criteria by determining associated topics, retrieving penalties, and calculating a satisfaction score using a scoring equation, comparing it to ranges to generate a status, and displaying the current status to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customer surveys and NPS are used to measure customer satisfaction, then customer satisfaction levels can be measured, but the measurements are aggregated and lagging, not reflecting real-time global customer satisfaction
Solution Approach 1:
The patent replaces traditional mechanical survey-based measurement systems with an automated computational system that uses machine learning models and algorithms to analyze customer interactions in real-time. This substitution enables continuous, real-time satisfaction scoring without the time delays inherent in manual survey collection and aggregation.
Solution Approach 2:
The system enables self-service by automatically collecting and analyzing customer interaction data without requiring customers to complete surveys. The machine learning models autonomously process interaction data, generate satisfaction scores, and update customer profiles in real-time, eliminating the need for customer participation in traditional survey processes.
2Quantity of substance
If traditional customer surveys are conducted, then some customer satisfaction data is obtained, but not all customers complete them and the data is not a true indication of global customer satisfaction
Solution Approach 1:
The system performs multiple functions simultaneously: it collects interaction data, analyzes sentiment, generates satisfaction scores, and updates customer profiles all through a single automated process. This multi-functional approach ensures comprehensive data collection from all customer interactions without requiring separate survey completion from each customer.
Solution Approach 2:
The patent implements continuous monitoring and analysis of customer interactions rather than periodic survey collection. The machine learning models continuously process incoming interaction data, generating ongoing satisfaction metrics that reflect the entire customer base at all times, ensuring both completeness and representativeness.
3Productivity
If a scoring system with multiple topics and penalties is implemented, then real-time customer satisfaction monitoring is achieved, but the system complexity increases
Solution Approach 1:
The patent segments the customer satisfaction measurement into distinct interaction topics and applies specific penalties or adjustments for each topic type. This segmentation allows the complex scoring system to be broken down into manageable, rule-based components that can be processed systematically by the machine learning models, reducing overall system complexity while maintaining real-time monitoring capability.
Data Source
AI summary
A system and method are disclosed for scoring an interaction over one or more channels by an end user and an entity by monitoring the communications over the channels and assigning penalties and scores based on topics associated with the communications.