论文标题
因果机器学习如何利用营销策略：评估和改善优惠券活动的绩效
How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign
论文作者
论文摘要
我们应用因果机器学习算法来评估营销干预措施的因果效应，即优惠券活动，对零售商的销售。除了评估不同类型的优惠券的平均影响外，我们还研究了不同亚组客户的因果效应的异质性，例如，在相对较高的客户与先前购买相对较高的客户之间。最后，我们使用最佳政策学习来确定（以数据驱动方式）哪些客户群体应由优惠券广告系列作为目标，以最大程度地提高营销干预措施在销售方面的有效性。我们发现，在检查的五个优惠券类别中，只有两个，即适用于药店产品和其他食品产品类别的优惠券，对零售商销售具有统计学上的积极影响。对小组平均治疗效果的评估表明，在商店中先前购买的客户群体中，尤其是在客户群体之间提供优惠券的影响的实质性差异，而药店优惠券在先前购买较高的客户中特别有效，并且先前购买较低的客户的其他食品优惠券。我们的研究为在业务分析中应用因果机学习提供了一种用例，以评估特定公司政策（例如营销活动）对决策支持的因果影响。
We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retailer. Besides assessing the average impacts of different types of coupons, we also investigate the heterogeneity of causal effects across different subgroups of customers, e.g., between clients with relatively high vs. low prior purchases. Finally, we use optimal policy learning to determine (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. We find that only two out of the five coupon categories examined, namely coupons applicable to the product categories of drugstore items and other food, have a statistically significant positive effect on retailer sales. The assessment of group average treatment effects reveals substantial differences in the impact of coupon provision across customer groups, particularly across customer groups as defined by prior purchases at the store, with drugstore coupons being particularly effective among customers with high prior purchases and other food coupons among customers with low prior purchases. Our study provides a use case for the application of causal machine learning in business analytics to evaluate the causal impact of specific firm policies (like marketing campaigns) for decision support.
