Computational marketing has become increasingly important in today's digital world, facing challenges such as massive heterogeneous data, multi-channel customer journeys, and limited marketing budgets. In this paper, we propose a general framework for marketing AI systems, the Neural Optimization with Adaptive Heuristics (NOAH) framework. NOAH is the first general framework for marketing optimization that considers both to-business (2B) and to-consumer (2C) products, as well as both owned and paid channels. We describe key modules of the NOAH framework, including prediction, optimization, and adaptive heuristics, providing examples for bidding and content optimization. We then detail the successful application of NOAH to LinkedIn's email marketing system, showcasing significant wins over the legacy ranking system. Additionally, we share details and insights that are broadly useful, particularly on: (i) addressing delayed feedback with lifetime value, (ii) performing large-scale linear programming with randomization, (iii) improving retrieval with audience expansion, (iv) reducing signal dilution in targeting tests, and (v) handling zero-inflated heavy-tail metrics in statistical testing.

翻译：在当今数字化世界中，计算营销日益重要，同时面临着海量异构数据、多渠道客户旅程以及有限营销预算等挑战。本文提出了一种营销人工智能系统的通用框架——基于自适应启发式神经优化（NOAH）框架。NOAH是首个兼顾企业级（2B）与消费级（2C）产品、同时涵盖自有渠道与付费渠道的营销优化通用框架。我们阐述了NOAH框架的核心模块，包括预测、优化与自适应启发式模块，并以竞价优化与内容优化为例进行说明。随后详细介绍了NOAH在领英电子邮件营销系统中的成功应用，展示了其相较于传统排序系统的显著优势。此外，我们分享了具有广泛适用性的技术细节与洞见，特别涉及：（i）通过客户终身价值处理延迟反馈，（ii）采用随机化方法进行大规模线性规划，（iii）通过受众扩展改进检索效果，（iv）减少定向测试中的信号稀释现象，以及（v）统计检验中处理零膨胀重尾指标的方法。