A Semi-Automated approach to Measure Effectiveness of Call Center Agents

Abstract

For large pharmaceutical organizations to provide better quality of life to patients, it is imperative they interact with and provide services to patients with unmet needs.   Hence, the agents at patient call centers of such companies need to be highly efficient. To measure the effectiveness of such agents, it is important that the inbound and outbound calls with patients are evaluated closely. Doing this manually for every call, although effective, is time and cost intensive. Hence, the supervisors can evaluate only a handful sample of calls manually. Could we imagine an AI solution that can assess the sentiments on the call and adherence to call guidelines throughout the duration of each call? The idea, though novel, has some major obstacles due to (a) challenges in correctly separating the sentences spoken by patients and agents; (b) PII (Personal Identifiable Information) and PHI (Protected Health Information) regulations on patient data handling in the pharma industry; (c) correctly identify patient’s intent and emotion etc. In this paper, we shall demonstrate a deep learning based Natural Language Processing (NLP) solution to identify speakers (agent, non-agent, etc.) from redacted conversations, derive patients’ sentiments through the call duration and agents’ response to each negative emotion, agents’ adherence to call guidelines including repetitions, long hold-times, etc. and finally develop a scoring mechanism to assess the agent’s effectiveness in handling the call.