[2302.01416] Neural Insights for Digital Marketing Content Design

In digital marketing, experimenting with new website content is one of the
key levers to improve customer engagement. However, creating successful
marketing content is a manual and time-consuming process that lacks clear
guiding principles. This paper seeks to close the loop between content creation
and online experimentation by offering marketers AI-driven actionable insights
based on historical data to improve their creative process. We present a
neural-network-based system that scores and extracts insights from a marketing
content design, namely, a multimodal neural network predicts the attractiveness
of marketing contents, and a post-hoc attribution method generates actionable
insights for marketers to improve their content in specific marketing
locations. Our insights not only point out the advantages and drawbacks of a
given current content, but also provide design recommendations based on
historical data. We show that our scoring model and insights work well both
quantitatively and qualitatively.