VoC-DL: Revisiting Voice Of Customer Using Deep Learning

Abstract

In the field of digital marketing, understanding the voice of the customer is paramount. Mining textual content written by visitors on websites or social media can offer new dimensions to marketers and CX executives. Traditional tasks in NLP like sentiment analysis, topic modeling etc. can solve only certain specific problems but don’t provide a generic solution to identifying/understanding the intention behind a text. In this paper we consider higher dimensional extensions to the sentiment concept by incorporating labels like product enquiry, buying intent, seeking help, feedback and pricing query which give us a deeper understanding of the text. We show how our model performs in a real-world enterprise use case. Word2Vec embeddings are used for word representations and later we compare three algorithms for classification. SVM’s provide us with a strong baseline. Two deep learning models viz. vanilla CNN and RNN’s with LSTM are compared. With no use of hard-coded or hand engineered features, our generic model can be used in a variety of use cases where text mining is involved with ease.