Engagement Quality: An Automated Measure of User Responses to Social Media Marketing Posts to Indicate Marketing Performance
(A2022-107011)

AUTHORS

Daniel Bruns, University of Wuppertal; Steffen Prior, University of Wuppertal

ABSTRACT

This paper presents and validates the measure of engagement quality to reflect users’ emotional responses to social media posts as an indicator of social media marketing success. Engagement quality is a two-dimensional emotional response score of valence and intensity of text- and/or emoji-based user comments to a social media post. We assess engagement quality via Naïve Bayes classification and examine its convergent, discriminant, and predictive validity with regard to a sample of influencer marketing campaigns. Employing linear mixed-effects regressions, we show that engagement quality can predict brand attitude, purchase intention, willingness to pay, and positive word of mouth, whereas the engagement rate as well as a one-dimensional sentiment score do not sufficiently predict these outcomes. From these findings we conclude that engagement quality can serve as a valuable performance indicator and helpful controlling tool for marketers.