Evolving Customer Experience Management in Internet Service Provider Company using Text Analytics

Abstract

Customer experience is of crucial significance to the constant growth of a business. It is necessary to ensure great customer experience, thus maintaining customer loyalty and satisfaction. An approach that intended to develop and improve customer experience is called Customer Experience Management (CEM). CEM is a strategy practiced to track, supervise, and arrange all synergy to help a business focal point on the needs of its customers. This research uses sentiment analysis and topic modeling to analyze the experience of Internet Service Provider customers. The output of this research expected to drive the strategies change in CEM. This research uses data taken from customer tweets on Twitter. It is considering that the data on social media is enormous and unstructured. Therefore, classification using Naive Bayes Classifier applied to assist and expedite in the sentiment analysis process. The classification for sentiment analysis using NBC gained accuracy above 82%. Hence, the classification models using NBC achieve excellent capability for sentiment analysis. To determining topics that often discussed by customers, this research uses the Latent Dirichlet Allocation models for Topic Modeling.