mlsanityData to 7 Oct 2026

Neural Insights for Digital Marketing Content Design

arXiv 2302.01416 ↗First posted 2 Feb 2023cs.LG cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 33 of its 75 references that are in the map. First posted on arXiv 2 Feb 2023.

Builds on

33 of 75 references.

References · 33

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.

4 papers.

  1. 2024Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practicecited by 4
  2. 2023A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Modelscited by 3
  3. 2024Counterfactual Evaluation of Ads Ranking Models through Domain Adaptationcited by 0
  4. 2026PinDCO: Whole-Page Aware Dynamic Creative Optimization at Scalecited by 0