AI Customer Behavior Prediction via Call Transcript Analysis
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Solution Overview
Problem
Businesses face challenges in predicting customer behavior post-interaction with call centers, leading to potential dissatisfaction and negative social media feedback, which existing technologies have not effectively addressed.
Innovation Solution
A method and apparatus utilizing AI/ML-based systems to analyze call audio transcripts, extracting sentiment and metadata features, and predicting customer satisfaction and propensity to escalate on social media by determining sentiment scores, word counts, and call metrics, combined with customer activity profiles and escalation term analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional call center systems are used without AI/ML analysis, then the system complexity is low, but the ability to predict customer behavior and satisfaction is insufficient
Solution Approach 1:
An AI/ML-based prediction system is introduced as an intermediary component between the call management system and customer behavior outcomes. This intermediary analyzes call audio transcripts, sentiment, and metadata to generate prediction scores, thereby improving measurement precision without requiring fundamental changes to the core call management infrastructure.
Solution Approach 2:
Traditional manual or rule-based methods for assessing customer satisfaction are replaced with AI/ML-based automated analysis. The system substitutes mechanical human review with intelligent algorithms that process audio transcripts and sentiment data, achieving higher prediction accuracy while maintaining manageable system complexity through modular architecture.
2Measurement precision
If comprehensive audio analysis with multiple features is performed, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
Call audio transcripts are generated and prepared in advance during or immediately after the call concludes. Sentiment analysis and feature extraction are performed on preprocessed transcripts rather than raw audio, reducing real-time processing requirements while maintaining comprehensive analysis capabilities for accurate predictions.
Solution Approach 2:
The prediction system processes multiple features (sentiment scores, word counts, call metrics) in a segmented, modular manner. Each feature is extracted and analyzed independently before being combined for final prediction, allowing for optimized processing of individual features and reducing overall computational burden while maintaining comprehensive analysis.
Data Source
AI summary
A method and apparatus for predicting customer behavior is disclosed. The method comprises organizing a transcribed, diarized text of a conversation in a call, into a predefined number of sets, determining features corresponding to a sentiment score, a percentage and/or count of positive words, and a percentage and/or count of negative words for each of the a predefined number of sets, determining word count features corresponding to the word count for each of the a predefined number of sets, determining features corresponding to a call talk time, a call hold time and a call hold percentage based on the transcribed text. Based on all the determined features, the method determines whether the customer is satisfied or not, the customer activity based on an activity profile of the customer, and whether the customer used escalation terms based on the transcribed text. Based on the customer satisfaction, customer activity, and the customer use of escalation terms, the method determines a probability of a customer action.