Text Analytics for Customer Engagement in Social Media

Abstract

Businesses have recognized that customers provide value to the firm beyond transactions, and leveraging this value through relationships in social media is a new area of interest for both academics and practitioners. Recent research has investigated how businesses can best manage their online presence on platforms not fully under their control, such as Facebook, YouTube, Instagram, TripAdvisor, and Yelp, among others. This dissertation extends the literature of customer engagement in social media through four contributions. First, we propose a framework that foregrounds the textual artifacts involved in online communication. Second, we develop a novel method for discovering the elements of successful Business to Customer (B2C) messages in online communities. Third, we propose a method, validated through experimentation, for finding critical product feedback in Customer to Customer (C2C) communications. Finally, we demonstrate that a set of novel numerical features can enhance the discovery of product defect mentions in C2C communications. We conclude by proposing a research agenda suggested by the framework that will further enhance our understanding of the complex customer interactions that characterize business in the era of social media.