Neural Insights for Digital Marketing Content Design

TL;DR

This paper introduces a neural-network system that predicts marketing content attractiveness and provides actionable insights to enhance digital marketing content design, bridging content creation and online experimentation.

Contribution

It presents a multimodal neural network with a post-hoc attribution method that scores and offers design recommendations for marketing content based on historical data.

Findings

The scoring model accurately predicts content attractiveness.

Insights effectively identify content strengths and weaknesses.

Recommendations improve content performance in marketing campaigns.

Abstract

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.