  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.
