Machine Learning Model Predicts Customer Satisfaction from Call Transcripts
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Solution Overview
Problem
Businesses face challenges in accurately and confidently tracking and assessing customer satisfaction in call centers due to its subjective nature and various influencing factors, making it difficult to predict customer satisfaction effectively.
Innovation Solution
A method and apparatus that utilize machine learning models to predict customer satisfaction by transcribing conversations between customers and agents, extracting call features and metadata, and combining them to generate outputs indicating customer satisfaction, with the model being trained and validated using multiple calls to achieve desired accuracy thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to assess customer satisfaction, then subjectivity and human error increase, but implementation complexity remains low
Solution Approach 1:
The patent replaces manual human assessment of customer satisfaction with an automated machine learning system that processes call transcriptions and metadata. This substitution eliminates human subjectivity and inconsistency while providing objective, data-driven satisfaction predictions through algorithmic analysis of conversation patterns, sentiment, and interaction metrics.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw call data and customer satisfaction assessment. This intermediary layer processes and interprets complex conversation data, extracting meaningful patterns and predictions while shielding users from the underlying complexity of the analysis methodology.
2Measurement precision
If comprehensive call features and metadata are analyzed, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing call transcriptions and extracting relevant features during or immediately after calls, rather than analyzing everything in real-time. Call transcripts are generated and stored beforehand, and the machine learning model can process pre-extracted features along with call metadata, reducing the computational burden during actual prediction operations.
Solution Approach 2:
The patent extracts only the most relevant features from comprehensive call data, such as sentiment scores, key conversation topics, agent-customer interaction patterns, and specific metadata elements. This selective extraction approach maintains high prediction accuracy by focusing on predictive features while discarding redundant information that would increase processing time and resource consumption.
3Reliability
If machine learning models are trained and validated with multiple calls, then prediction reliability improves, but system implementation complexity increases
Solution Approach 1:
The patent implements self-service through automated model training and validation processes that require minimal human intervention. The system automatically trains machine learning models using historical call data, performs validation against test sets, and iteratively improves prediction accuracy. This automation reduces the operational complexity of maintaining reliable predictions while ensuring continuous model optimization through systematic retraining and validation protocols.
4Productivity
If real-time prediction is implemented, then customer experience improvement is enhanced, but computational resource requirements increase
Solution Approach 1:
The patent applies local quality by providing real-time prediction capabilities selectively at critical moments in the customer interaction, such as during or immediately after calls, rather than continuously processing all data streams. This targeted real-time analysis focuses computational resources on high-value prediction opportunities while maintaining overall system efficiency and managing resource consumption through prioritized processing of time-sensitive data.
Data Source
AI summary
A method and an apparatus for predicting satisfaction of a customer pursuant to a call between the customer and an agent, in which the method comprises receiving a transcribed text of the call, dividing the transcribed text into a plurality of phases of a conversation, extracting at least one call feature for each of the plurality of phases, receiving call metadata, extracting metadata features from the call metadata, combining the call features and the metadata features, and generating an output, using a trained machine learning (ML) model, based on the combined features, indicating whether the customer is satisfied or not. The ML model is trained to generate an output indicating whether the customer is satisfied or not, based on an input of the combined features.