  Influencer marketing has become a very popular tool to reach customers.
Despite the rapid growth in influencer videos, there has been little research
on the effectiveness of their constituent features in explaining video
engagement. We study YouTube influencers and analyze their unstructured video
data across text, audio and images using an "interpretable deep learning"
framework that accomplishes both goals of prediction and interpretation. Our
prediction-based approach analyzes unstructured data and finds that "what is
said" in words (text) is more influential than "how it is said" in imagery
(images) or acoustics (audio). Our novel interpretation-based approach is
implemented after completion of model prediction by analyzing the same source
of unstructured data to measure importance attributed to the video features. We
eliminate several spurious relationships in two steps, identifying a subset of
relationships which are confirmed using theory. We uncover novel findings that
establish distinct associations for measures of shallow and deep engagement
based on the dual-system framework of human thinking. Our approach is validated
using simulated data, and we discuss the learnings from our findings for
influencers and brands.
