Title: Causal machine learning in marketing: Assessing and improving the performance of a coupon campaign Authors:  Martin Huber - University of Fribourg (Switzerland)
Henrika Langen - University of Helsinki (Finland) [presenting]
Abstract: Causal machine learning algorithms are applied to assess the causal effect of a marketing intervention, namely a coupon campaign, on a retailer's sales. Besides assessing the average impacts of different types of coupons, the heterogeneity of causal effects is investigated across different subgroups of customers, e.g., between clients with relatively high vs. low prior purchases. Finally, optimal policy learning is used to determine (in a data-driven way) which customer groups should be targeted by the coupon campaign to maximize the marketing intervention's effectiveness in terms of sales. It is found 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. The 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.