Personality-Based Content Engineering for Rich Digital Media
53 Pages Posted: 16 May 2019 Last revised: 18 Aug 2019
Date Written: April 4, 2019
Firms have increasingly turned to rich digital media, such as videos and photos, to attract attention and boost awareness. Although extant research may help firms promote these media more effectively, the marketing process truly begins with creation of the media. Content creators may thus benefit from understanding what content features are likely to help their media achieve greater popularity. We develop a method to understand the effect of content on the consumption of online videos, and apply this method on a unique dataset of 16,414 videos from 363 YouTube channels. Our approach labels videos as high- or low-performing relative to comparable videos, and employs natural language processing to characterize videos by the extent to which their captions reflect each of the “Big Five” personality traits. We then leverage a non-linear, data-driven machine learning inductive technique to identify content features associated with performance level. Our analysis uncovers novel predictive, economic, and prescriptive insights. We find that using just their personality, we can predict with 72% accuracy whether videos will perform better than comparable media. Furthermore, videos associated with high-performing personalities can expect a 15% increase in consumption relative to those with low-performing personalities. Finally, we examine which personalities are associated with high consumption, offering prescriptive insights for content engineering.
Keywords: content engineering, personality, rich digital media, random forests, natural language processing
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