We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retail company. Besides assessing the average impacts of different types of coupons, we also investigate the heterogeneity of causal effects across subgroups of customers, e.g. across clients with relatively high vs. low previous purchases. Finally, we use optimal policy learning to learn (in a data-driven way) which customer groups should be targeted by the coupon campaign in order to maximize the marketing intervention's effectiveness in terms of sales. Our study provides a use case for the application of causal machine learning in business analytics, in order to evaluate the causal impact of specific firm policies (like marketing campaigns) for decision support.

翻译：我们运用因果机学习算法来评估市场干预,即优惠券运动对零售公司的销售的因果关系。除了评估不同种类的优惠券的平均影响外,我们还调查不同客户分组之间因果效应的异质性,例如,在相对高的客户之间和以往低的购买之间。 最后,我们利用最佳的政策学习来(以数据驱动的方式)了解哪些客户群体应该成为优惠券运动的目标,以便最大限度地提高市场干预在销售方面的效力。我们的研究为在商业分析中应用因果机学提供了一个使用案例,以便评估具体公司政策(如营销运动)对决策支持的因果影响。