AI Text Stress Detection for Contact Center Agent Interventions
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Solution Overview
Problem
Contact centers face high agent churn due to stressful interactions, particularly in text-based channels, which current methods fail to address effectively, leading to increased costs and reduced productivity.
Innovation Solution
A smart lexicon and custom neural network layer are developed to detect stressful interactions by filtering and clustering relevant language patterns, using cosine similarity to reduce complexity and enhance stress detection in text-based interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio signal analysis is used to detect stress, then stress detection precision is improved, but adaptability to text-based interactions deteriorates
Solution Approach 1:
The system changes the detection parameter from audio signals to text data parameters. By transforming the input modality from acoustic to textual, the system maintains stress detection capability while adapting to text-based interaction channels, thus resolving the contradiction between audio-specific precision and multi-channel adaptability
Solution Approach 2:
The patent replaces the audio signal processing mechanism with a text-based detection mechanism. Instead of analyzing acoustic waveforms and audio features, the system uses natural language processing techniques to detect stress indicators in text, substituting the detection 'mechanics' to work with text-based interactions
2Measurement precision
If comprehensive text analysis is performed to detect all stress indicators, then stress detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system extracts and isolates specific stress indicator patterns from the broader text data. By identifying and focusing on particular linguistic markers, phrases, and communication patterns that indicate stress, the system achieves accurate stress detection without needing to analyze every aspect of the text, thus reducing processing complexity
Solution Approach 2:
The patent segments the text analysis process into distinct components: identifying stress indicators, classifying interaction types, and detecting patterns. This segmentation allows the system to handle complex text data through manageable analytical steps, maintaining accuracy while controlling system complexity
3Reliability
If real-time stress detection is implemented to provide timely interventions, then agent retention is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing by pre-identifying and storing stress indicator patterns and communication patterns before actual stress detection is needed. This preliminary preparation allows the system to quickly match incoming text interactions against pre-established patterns, enabling real-time detection without requiring extensive processing time during actual stress events
Data Source
AI summary
Stress detection systems and methods, and non-transitory computer readable media, include building a library including previously identified stressful sentences and stressful phrases; receiving, by a trained neural network model, the library; receiving, by the trained neural network model, a text interaction between a customer and an agent; calculating, by the trained neural network model, a cosine similarity score between each stressful sentence or stressful phrase in the library and each sentence in the text interaction; determining, by the trained neural network model, a probability that the text interaction is stressful based on the calculated cosine similarity score; determining that a percentage of stressful interactions for the agent in a time interval is greater than a threshold percentage; providing a manager with recommended actions to decrease stress on the agent; receiving, from the manager, a selection of one or more recommended actions; and implementing the one or more recommended actions.