Evaluating the Incremental Value of E-commerce Promotional Campaigns: An Uplift Modeling Approach

Abstract

E-commerce platforms frequently deploy promotional campaigns to stimulate customer purchases, yet accurately quantifying their true incremental impact remains challenging due to the presence of self-selection bias among customers. Traditional response models often fail to distinguish between customers who would purchase regardless of the campaign and those genuinely influenced by it. This study employs uplift modeling, a causal inference technique, to estimate the individual-level incremental effect of promotional campaigns on customer purchasing behavior. Using a real-world e-commerce dataset, we compare the performance of various uplift modeling algorithms, including the uplift random forest and causal forest, against conventional classification approaches. Our findings reveal that uplift models significantly outperform traditional methods in identifying persuadable customers—those whose purchase decisions are positively influenced by the campaign. Additionally, the results highlight the potential for cost savings by targeting only high-uplift segments, thereby avoiding wasteful spending on customers who are either immune or likely to purchase organically. This research underscores the strategic importance of uplift modeling in optimizing marketing resource allocation and enhancing return on investment for e-commerce promotions.