用Transformer模型提升营销效果评估精度，能分析广告创意和搜索词等复杂数据。
NNN: Next-Generation Neural Networks for Marketing Measurement
- 基于Transformer的嵌入表示，融合定量与定性营销信息
- 在真实和模拟数据上显著提升预测能力，尤其改善归因准确性
- 适合需要深度理解广告效果的营销分析人员
我们提出NNN，一种基于Transformer的实验性神经网络方法，用于营销测量。与依赖标量输入和参数化衰减函数的传统营销组合模型（MMMs）不同，NNN利用丰富的嵌入向量捕捉营销及自然渠道（如搜索查询、广告创意）的量化与质性特征。结合注意力机制，有望建模复杂交互关系，捕捉长期影响，并提升销售归因准确率。我们证明，通过L1正则化，可在典型数据受限场景下使用此类高表达力模型。在模拟与真实数据上的评估显示其有效性，尤其体现在预测性能的显著提升。除营销测量外，该框架还可通过模型探查提供补充洞察，如关键词或创意的有效性评估。
原文摘要 · Abstract (English)
We present NNN, an experimental Transformer-based neural network approach to marketing measurement. Unlike Marketing Mix Models (MMMs) which rely on scalar inputs and parametric decay functions, NNN uses rich embeddings to capture both quantitative and qualitative aspects of marketing and organic channels (e.g., search queries, ad creatives). This, combined with its attention mechanism, potentially enables NNN to model complex interactions, capture long-term effects, and improve sales attribution accuracy. We show that L1 regularization permits the use of such expressive models in typical data-constrained settings. Evaluating NNN on simulated and real-world data demonstrates its efficacy, particularly through considerable improvement in predictive power. In addition to marketing measurement, the NNN framework can provide valuable, complementary insights through model probing, such as evaluating keyword or creative effectiveness.
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