Employee Experience Score Analysis for Call Center Stress Reduction
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Solution Overview
Problem
Existing employee management systems fail to address the detrimental effects of high volume and high stress jobs on mental health and social isolation of customer service employees, leading to increased attrition and decreased job satisfaction.
Innovation Solution
A system and method that uses machine learning models to analyze audio data from calls to generate experience scores for employees, providing real-time alerts and notifications to improve their experience and well-being, and modifying call schedules to reduce stress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If employees handle high volume and high frequency of calls, then productivity increases, but mental health deteriorates and employee attrition increases
Solution Approach 1:
The system performs preliminary actions by monitoring employee experience scores in real-time during calls and proactively identifying stress indicators before critical thresholds are reached. This allows early intervention through notifications to supervisors or self-service options, preventing mental health deterioration before it leads to attrition, thus maintaining both high productivity and employee retention.
2Adaptability or versatility
If employees deal with more difficult issues and difficult customers, then problem-solving capability is improved, but mental health deteriorates
Solution Approach 1:
The system implements continuous feedback by analyzing acoustic features and text data from calls to generate real-time experience scores that reflect employee stress levels. This feedback loop enables dynamic adjustment of support interventions, allowing employees to maintain problem-solving capability on difficult issues while receiving timely support to prevent mental health deterioration.
3Ease of operation
If fully remote or work-from-home jobs are implemented, then employee flexibility is improved, but social isolation increases
Solution Approach 1:
The system acts as an intermediary by providing automated monitoring and support mechanisms that bridge the social connection gap in remote work environments. Through real-time experience score analysis and automated notifications to supervisors, the system enables maintained social and professional engagement while preserving the flexibility benefits of remote work, thus reducing social isolation without compromising work-from-home advantages.
Data Source
AI summary
Techniques for monitoring and improving emotional well-being of an employee are described. Stream of audio data corresponding to a call between an employee and a customer may be received. One or more acoustic features and/or audio feature data may be generated from the audio data. Word embedding data corresponding to the audio data may be generated. An employee experience score may be generated using a machine learning (ML) model, word embedding data, and the one or more acoustic features, where the score corresponds to an experience level of the first speaker during the call with the second speaker. Based on the score, an action may be caused to be performed. In some embodiments, one or more notifications may be generated based on data related to the audio data, where at least notification is configured to improve an experience level for the first speaker.