Neural Insights for Digital Marketing Content Design

Fanjie Kong; Yuan Li; Houssam Nassif; Tanner Fiez; Ricardo Henao; Shreya Chakrabarti

doi:10.1145/3580305.3599875

Neural Insights for Digital Marketing Content Design

Fanjie Kong, Yuan Li, Houssam Nassif, Tanner Fiez, Ricardo Henao, Shreya Chakrabarti

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

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.

Original language	English (US)
Title of host publication	KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	4320-4332
Number of pages	13
ISBN (Electronic)	9798400701030
DOIs	https://doi.org/10.1145/3580305.3599875
State	Published - Aug 6 2023
Event	29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023 - Long Beach, United States Duration: Aug 6 2023 → Aug 10 2023

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023
Country/Territory	United States
City	Long Beach
Period	08/6/23 → 08/10/23

Bibliographical note

Publisher Copyright:
© 2023 ACM.

Keywords

deep learning
digital marketing
image and text recommendation
interactive system
model interpretation

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/3580305.3599875

Cite this

Kong, F., Li, Y., Nassif, H., Fiez, T., Henao, R., & Chakrabarti, S. (2023). Neural Insights for Digital Marketing Content Design. In KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4320-4332). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3580305.3599875

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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.",

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author = "Fanjie Kong and Yuan Li and Houssam Nassif and Tanner Fiez and Ricardo Henao and Shreya Chakrabarti",

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Kong, F, Li, Y, Nassif, H, Fiez, T, Henao, R & Chakrabarti, S 2023, Neural Insights for Digital Marketing Content Design. in KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 4320-4332, 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, United States, 08/6/23. https://doi.org/10.1145/3580305.3599875

Neural Insights for Digital Marketing Content Design. / Kong, Fanjie; Li, Yuan; Nassif, Houssam et al.
KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2023. p. 4320-4332 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Chakrabarti, Shreya

PY - 2023/8/6

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AB - 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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Kong F, Li Y, Nassif H, Fiez T, Henao R, Chakrabarti S. Neural Insights for Digital Marketing Content Design. In KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2023. p. 4320-4332. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3580305.3599875

Neural Insights for Digital Marketing Content Design

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this