Improving Customer Experience throughout the Customer Journey in the Big Data Era

My PhD dissertation focuses on how firms should adapt their strategies to improve customer engagement throughout customer journey. My first paper examines firm-customer conversations on social media. Many firms struggle with how to craft their messages in conversations with customers on social media, and the lack of guidance for interacting with customers is among the top social media challenges reported by firms. The problem is compounded by the fact that these conversations take place in different, simultaneous threads, each of which potentially requiring a different approach. This paper studies how firms can adapt their responses in individual social media conversations such that these conversations become more favorable to the firm in terms of valence and arousal. Drawing on Speech Act Theory and based on an analysis of 1.6 million tweets capturing over 210,000 conversations involving the four major US banks across 10 years, the paper shows how firms, depending on the ongoing conversation, should adjust their firm-generated content (FGC) in terms of its valence, arousal, subjectivity, and topic.

In my second paper I include all text communications recorded between a vacation rental firm and customers to realize how emotional content in firm messages can affect important customer outcomes. I conduct a thorough literature review to identify prior research on emotions in firm-customer communications. I suggest a novel deep learning tool in Natural Language Processing to extract and measure emotion in firm messages. To measure the outcomes of such emotional content in firm messages, I use repurchase as it is a critical indicator of loyalty.