Neural Insights for Digital Marketing Content Design
- URL: http://arxiv.org/abs/2302.01416v3
- Date: Wed, 7 Jun 2023 20:23:14 GMT
- Title: Neural Insights for Digital Marketing Content Design
- Authors: Fanjie Kong, Yuan Li, Houssam Nassif, Tanner Fiez, Ricardo Henao,
Shreya Chakrabarti
- Abstract summary: We present a neural-network-based system that scores and extracts insights from a marketing content design.
Our insights not only point out the advantages and drawbacks of a given current content, but also provide design recommendations based on historical data.
- Score: 22.922947923206756
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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