|
Abstract:
|
Influencer marketing is being increasingly used as a tool to reach customers because of the growing popularity of social media stars who primarily reach their audience(s) via custom videos. Using publicly available data on YouTube influencer videos, I 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, my approach avoids making a trade-off between interpretability and predictive ability. I 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.
|
