In this work, we propose a framework relying solely on chat-based customer support (CS) interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Consequently, our main contributions and objectives are to use Natural Language Processing (NLP) to assess and predict the recommendation behavior where, in addition to using static sentiment analysis, we exploit the predictive power of each user's sentiment dynamics. Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.
@article{arxiv.2211.03923,
title = {Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions},
author = {Juan Sebastián Salcedo Gallo and Jesús Solano and Javier Hernán García and David Zarruk-Valencia and Alejandro Correa-Bahnsen},
journal= {arXiv preprint arXiv:2211.03923},
year = {2022}
}
Comments
10 pages, 4 figures, 1 table. Already accepted at NeurIPS 2022, LatinX in AI Workshop