Linguistic Elements of Engaging Customer Service Discourse on Social Media
This work addresses the challenge for brand agents in improving customer service discourse on social media, though it appears incremental by building on existing linguistic analysis methods.
The study tackled the problem of predicting customer engagement on social media by analyzing linguistic elements in initial posts, finding that specific content and stylistic features can influence interaction length.
Customers are rapidly turning to social media for customer support. While brand agents on these platforms are motivated and well-intentioned to help and engage with customers, their efforts are often ignored if their initial response to the customer does not match a specific tone, style, or topic the customer is aiming to receive. The length of a conversation can reflect the effort and quality of the initial response made by a brand toward collaborating and helping consumers, even when the overall sentiment of the conversation might not be very positive. Thus, through this study, we aim to bridge this critical gap in the existing literature by analyzing language's content and stylistic aspects such as expressed empathy, psycho-linguistic features, dialogue tags, and metrics for quantifying personalization of the utterances that can influence the engagement of an interaction. This paper demonstrates that we can predict engagement using initial customer and brand posts.