Sensitivity and Early Detection of Bayesian Causal Impact Models for Marketing Interventions
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营销干预中贝叶斯因果影响模型的敏感性与早期检测
- 研究问题
- 贝叶斯因果影响模型能多早发现营销表现恶化，效应大小、置信水平与监测期限如何共同影响报警能力？
- 方法与思路
- 作者对营销流程的日流量反复施加受控扰动，比较低于预测下界的观测比例与累计影响连续为负两类报警标准。
- 材料陈述的贡献
- 摘要称检测表现取决于效应、置信水平和期限的交互；比例标准随期限延长变弱，持续性标准在所考察设置中更稳定。
- 阅读关注点
- 应核对扰动是否保留原序列依赖、反事实预测如何形成，以及两类报警的阈值、误报控制与检测期限如何比较。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明因果识别条件、误报结果及跨营销场景适用性。
原文摘要
Marketing systems frequently undergo operational changes that may affect performance, making timely detection of adverse effects essential for decision making. While Bayesian Causal Impact models are widely used to estimate the causal effects of interventions, their ability to support early operational monitoring remains less explored. This paper proposes a simulation based framework to evaluate the sensitivity and detection capabilities of Bayesian causal impact analysis under controlled performance degradations. Using daily traffic data from an abandoned cart marketing journey, we repeatedly perturb the observed outcomes and assess alarm activation probabilities across different effect magnitudes, confidence levels, and detection horizons. Two alarm criteria are analyzed: one based on the proportion of observations falling below the predictive lower bound and another based on consecutive days of negative cumulative impact. Results show that detection performance depends strongly on the interaction between effect size, confidence level, and evaluation horizon. In particular, proportion based criteria become less effective as the monitoring horizon increases, whereas persistence based criteria provide more stable and operationally meaningful detection behavior. The proposed framework extends causal impact analysis beyond retrospective effect estimation, offering a practical methodology for quantifying detection sensitivity and supporting monitoring decisions in dynamic marketing environments.