基于地理实验的营销组合模型参数的结构估计

中文摘要 AI 辅助

营销组合模型（Marketing Mix Models, MMMs）被广泛用于营销测量与预算分配，但面临根本性的识别挑战：由于营销支出决策存在内生性，基于观测时间序列数据的MMMs估计无法恢复营销的真实因果效应。另一方面，地理实验通过随机化实现因果识别，但如何高效利用地理实验校准营销组合模型尚不明确。我们提出一种新颖的结构估计方法，可直接从地理实验时间序列中恢复MMMs的全部参数——广告留存衰减率（α）、饱和效应（λ）与营销有效性（β）。通过对实验组与对照组区域的结果进行差分，我们的方法消除了可观测与不可观测的混淆因素，同时保留了识别各参数所需的时间变异。我们在合成数据上验证，该方法可在实验覆盖的支出范围内恢复真实的广告支出回报率（ROAS）与响应曲线，同时提供基础参数的可信估计。我们的框架支持跨多个实验的高效池化，为充分利用实验所含信息的MMMs校准提供了严谨基础。

英文摘要

Marketing Mix Models (MMMs) are widely used for marketing measurement and budget allocation, but face fundamental identification challenges: due to endogenous marketing spend decisions, MMM estimation on observational time-series data cannot recover the true causal effects of marketing. On the other hand, geo-experiments provide causal identification through randomization, but it is not clear how to use them efficiently to calibrate marketing mix models. We propose a novel structural estimation approach that recovers the complete set of MMM parameters - adstock decay ($α$), saturation ($λ$), and effectiveness ($β$) - directly from geo-experimental time-series. By differencing outcomes between treatment and control regions, our method eliminates observed and unobserved confounding factors while preserving the temporal variation that identifies each parameter. We demonstrate on synthetic data that this approach recovers the true ROAS and the response curve over the range of spending covered by the experiments, together with credible estimates of the underlying parameters. Our framework enables efficient pooling across multiple experiments and provides a principled foundation for MMM calibration that fully utilizes the information experiments contain.

发表机构

• Zalando（赞达）

机构由 AI 辅助整理，请以论文原文为准。