# Neural Insights for Digital Marketing Content Design

arXiv: https://arxiv.org/abs/2302.01416 · HTML: https://mlsanity.com/paper/2302.01416/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 75 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Counterfactual Visual Explanations](https://mlsanity.com/paper/1904.07451/) (2019)
- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions](https://mlsanity.com/paper/1710.04806/) (2017)
- [Explaining Image Classifiers by Counterfactual Generation](https://mlsanity.com/paper/1807.08024/) (2018)
- [OpenXAI: Towards a Transparent Evaluation of Model Explanations](https://mlsanity.com/paper/2206.11104/) (2022)
- [Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation](https://mlsanity.com/paper/1903.10992/) (2019)
- [Attention Branch Network: Learning of Attention Mechanism for Visual Explanation](https://mlsanity.com/paper/1812.10025/) (2018)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models](https://mlsanity.com/paper/2007.00163/) (2020)
- [Convolutional Dynamic Alignment Networks for Interpretable Classifications](https://mlsanity.com/paper/2104.00032/) (2021)
- [Towards Better Understanding Attribution Methods](https://mlsanity.com/paper/2205.10435/) (2022)
- [Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation](https://mlsanity.com/paper/1607.04373/) (2016)
- [Sharpen Focus: Learning with Attention Separability and Consistency](https://mlsanity.com/paper/1811.07484/) (2018)
- [Contextual Multi-Armed Bandits for Causal Marketing](https://mlsanity.com/paper/1810.01859/) (2018)
- [Efficient Classification of Very Large Images with Tiny Objects](https://mlsanity.com/paper/2106.02694/) (2021)
- [Bayesian Meta-Prior Learning Using Empirical Bayes](https://mlsanity.com/paper/2002.01129/) (2020)
- [Personalized Embedding-based e-Commerce Recommendations at eBay](https://mlsanity.com/paper/2102.06156/) (2021)
- [Passive Attention in Artificial Neural Networks Predicts Human Visual Selectivity](https://mlsanity.com/paper/2107.07013/) (2021)
- [Seeker: Real-Time Interactive Search](https://mlsanity.com/paper/1905.13125/) (2019)
- [Temporal Poisson Square Root Graphical Models](https://mlsanity.com/paper/2005.06462/) (2020)

## Built on by (most cited first, 4 of 4)

- [Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice](https://mlsanity.com/paper/2402.10870/) (2024)
- [A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models](https://mlsanity.com/paper/2304.04916/) (2023)
- [Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation](https://mlsanity.com/paper/2409.19824/) (2024)
- [PinDCO: Whole-Page Aware Dynamic Creative Optimization at Scale](https://mlsanity.com/paper/2609.11943/) (2026)

## 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.