Companies increasingly combine multiple media outlets when launching advertising campaigns. This study employs causal forest to examine the effects of complex multimedia campaigns. The model effectively corrects for selection bias, automatically identifies informative consumer features, and performs automated data-driven consumer segmentation based on the consumer features identified. We analyze a large dataset involving around seven million consumers and four thousand covariates, and provide empirical evidence on the nonlinear effect of repeated ad exposures in the multimedia context, how such effect varies across consumer groups, and the contingent existence of multimedia synergy. We demonstrate that negligence of the selection bias and heterogeneity across segments results in suboptimal conversions and a waste of advertising resources. The analysis procedure that we propose can facilitate decision making for complex advertising campaigns to improve their effectiveness.

中文翻译：

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评估多媒体广告活动的有效性


公司在推出广告活动时越来越多地结合多个媒体渠道。本研究采用因果森林来检验复杂多媒体活动的效果。该模型有效地纠正了选择偏差，自动识别信息丰富的消费者特征，并根据识别的消费者特征执行数据驱动的自动消费者细分。我们分析了一个涉及大约 700 万消费者和 4000 个协变量的大型数据集，并提供了关于多媒体环境中重复广告曝光的非线性效应、这种效应如何随消费者群体变化以及多媒体协同效应的偶然存在的经验证据。我们证明，忽视选择偏差和跨细分的异质性会导致次优转化和广告资源的浪费。我们提出的分析程序可以促进复杂广告活动的决策，以提高其有效性。