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.

翻译：在数字营销中，尝试新的网站内容是提升客户参与度的关键手段之一。然而，创建成功的营销内容是一个缺乏明确指导原则的手动且耗时的过程。本文旨在通过基于历史数据为营销人员提供人工智能驱动的可操作洞察，以改进其创作流程，从而弥合内容创作与在线实验之间的差距。我们提出了一种基于神经网络的系统，用于对营销内容设计进行评分并提取洞察：具体而言，多模态神经网络预测营销内容的吸引力，而一种事后归因方法则生成可操作的洞察，帮助营销人员在特定营销位置改进其内容。我们的洞察不仅指出当前给定内容的优缺点，还基于历史数据提供设计建议。我们证明，评分模型和洞察在定量和定性两方面均表现良好。