@inproceedings{0699fcf338c140389fea879f2b8e6800,
title = "Neural Insights for Digital Marketing Content Design",
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.",
keywords = "deep learning, digital marketing, image and text recommendation, interactive system, model interpretation",
author = "Fanjie Kong and Yuan Li and Houssam Nassif and Tanner Fiez and Ricardo Henao and Shreya Chakrabarti",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023 ; Conference date: 06-08-2023 Through 10-08-2023",
year = "2023",
month = aug,
day = "6",
doi = "10.1145/3580305.3599875",
language = "English (US)",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "4320--4332",
booktitle = "KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
}