  Influencer marketing is being used increasingly as a tool to reach customers
because of the growing popularity of social media stars who primarily reach
their audience(s) via custom videos. Despite the rapid growth in influencer
marketing, there has been little research on the design and effectiveness of
influencer videos. Using publicly available data on YouTube influencer videos,
we implement novel interpretable deep learning architectures, supported by
transfer learning, to identify significant relationships between advertising
content in videos (across text, audio, and images) and video views, interaction
rates and sentiment. By avoiding ex-ante feature engineering and instead using
ex-post interpretation, our approach avoids making a trade-off between
interpretability and predictive ability. We filter out relationships that are
affected by confounding factors unassociated with an increase in attention to
video elements, thus facilitating the generation of plausible causal
relationships between video elements and marketing outcomes which can be tested
in the field. A key finding is that brand mentions in the first 30 seconds of a
video are on average associated with a significant increase in attention to the
brand but a significant decrease in sentiment expressed towards the video. We
illustrate the learnings from our approach for both influencers and brands.
