Data Driven Content Creation using Statistical and Natural Language Processing Techniques for Financial Domain

TL;DR

This paper presents a framework that combines multi-channel interaction data and natural language processing techniques to enhance content creation and customer intent understanding in the financial domain.

Contribution

It introduces a novel two-part framework that integrates data from various channels and extracts customer questions using advanced NLP methods.

Findings

Effective summarization of interaction data into customer intents

Organically generated intent taxonomy from interaction data

Improved question identification using TF-IDF and BERT

Abstract

Over the years customers' expectation of getting information instantaneously has given rise to the increased usage of channels like virtual assistants. Typically, customers try to get their questions answered by low-touch channels like search and virtual assistant first, before getting in touch with a live chat agent or the phone representative. Higher usage of these low-touch systems is a win-win for both customers and the organization since it enables organizations to attain a low cost of service while customers get served without delay. In this paper, we propose a two-part framework where the first part describes methods to combine the information from different interaction channels like call, search, and chat. We do this by summarizing (using a stacked Bi-LSTM network) the high-touch interaction channel data such as call and chat into short searchquery like customer intents and then creating an organically grown intent taxonomy from interaction data (using Hierarchical Agglomerative Clustering). The second part of the framework focuses on extracting customer questions by analyzing interaction data sources. It calculates similarity scores using TF-IDF and BERT(Devlin et al., 2019). It also maps these identified questions to the output of the first part of the framework using syntactic and semantic similarity.