Using Natural Language Processing to Understand Reasons and Motivators Behind Customer Calls in Financial Domain

TL;DR

This paper introduces NLP models to analyze customer call transcripts in the financial sector, aiming to identify reasons and motivators behind calls to improve service and reduce costs.

Contribution

It presents two novel models: an attention-based LSTM with hierarchical clustering and an ensemble of SVM and Logistic Regression for call reason detection.

Findings

Models effectively extract call reasons from transcripts.

Ensemble approach improves detection accuracy.

Framework aids in understanding customer motivations.

Abstract

In this era of abundant digital information, customer satisfaction has become one of the prominent factors in the success of any business. Customers want a one-click solution for almost everything. They tend to get unsatisfied if they have to call about something which they could have done online. Moreover, incoming calls are a high-cost component for any business. Thus, it is essential to develop a framework capable of mining the reasons and motivators behind customer calls. This paper proposes two models. Firstly, an attention-based stacked bidirectional Long Short Term Memory Network followed by Hierarchical Clustering for extracting these reasons from transcripts of inbound calls. Secondly, a set of ensemble models based on probabilities from Support Vector Machines and Logistic Regression. It is capable of detecting factors that led to these calls. Extensive evaluation proves the effectiveness of these models.