AI CSAT Prediction via Call Transcript Analysis
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Solution Overview
Problem
Conventional methods for determining customer satisfaction (CSAT) in call centers face challenges such as low response rates, bias in survey data, and limited accessibility of CSAT data, which hinder effective customer satisfaction analysis and management.
Innovation Solution
A system utilizing an inference engine with an artificial intelligence model to predict CSAT based on call transcripts and attributes, enabling the generation of reports, root cause analysis, and dynamic monitoring of customer satisfaction, even when traditional survey data is sparse or biased.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional CSAT surveys are used to determine customer satisfaction, then customer satisfaction data can be collected, but the response rate is low (only 2-6% of customers respond)
Solution Approach 1:
The patent replaces the mechanical survey system (requiring active customer response) with an AI-based prediction system that passively analyzes call transcripts and attributes to automatically determine CSAT scores for all customers, eliminating the need for customers to manually respond to surveys
Solution Approach 2:
The system enables self-service by having the AI model automatically analyze call data and generate CSAT predictions without requiring customer participation or manual data collection, allowing the system to serve itself in determining customer satisfaction
2Loss of information
If CSAT surveys are used to gather customer satisfaction data, then satisfaction information can be obtained, but the data is biased toward extreme experiences
Solution Approach 1:
The patent replaces the biased survey mechanism with an AI prediction system that analyzes complete call transcripts and attributes, enabling objective measurement of customer satisfaction across all customers including those with neutral or positive experiences who would not respond to surveys
Solution Approach 2:
The AI model acts as an intermediary that processes raw call data (transcripts, attributes) and transforms it into accurate CSAT predictions, filtering out the bias inherent in direct survey responses by using comprehensive call context as an intermediate representation
3Loss of information
If traditional CSAT data collection methods are used, then survey responses can be gathered, but the data is not always accessible to all parties
Solution Approach 1:
The patent creates a universal CSAT prediction system that serves multiple stakeholders (call center managers, service providers, customers) by centrally processing call data and making predictions available to all authorized parties through a common platform, eliminating data silos
4Measurement precision
If AI-based prediction systems are implemented to improve CSAT accuracy, then comprehensive CSAT data can be obtained, but system complexity increases
Solution Approach 1:
The patent segments the complex AI prediction system into distinct functional components: data collection module (transcripts, attributes), AI inference engine (CSAT prediction), and analytics module (root cause analysis, dynamic monitoring), making the overall system more manageable and maintainable
Data Source
AI summary
A computer-implemented method of predicting customer satisfaction scores for a call center is disclosed, along with the use of the predicted customer satisfaction scores to perform various analytical functions, such as identifying changes to the predicted customer satisfaction score and identifying root causes of the predicted customer satisfaction scores. In some implementations, a pipeline includes an inference engine that includes an AI model trained on call transcripts and call attribute data to predict a customer satisfaction score.