Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing

TL;DR

This paper introduces a novel revenue uplift modeling framework that employs zero-inflated lognormal loss and listwise ranking loss to better handle long-tail distributions and optimize ranking, validated through extensive experiments.

Contribution

It proposes a new revenue uplift modeling approach with tailored loss functions and ranking optimization, improving accuracy and effectiveness over existing methods.

Findings

Effective handling of long-tail revenue distributions.

Improved uplift ranking accuracy.

Validated on public, industrial, and real-world datasets.

Abstract

Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individuals toward interventions like coupons or discounts. Compared with traditional \textit{conversion uplift modeling}, \textit{revenue uplift modeling} exhibits higher potential due to its direct connection with the corporate income. However, previous works can hardly handle the continuous long-tail response distribution in revenue uplift modeling. Moreover, they have neglected to optimize the uplift ranking among different individuals, which is actually the core of uplift modeling. To address such issues, in this paper, we first utilize the zero-inflated lognormal (ZILN) loss to regress the responses and customize the corresponding modeling network, which can be adapted to different existing uplift models. Then, we study the ranking-related uplift modeling error from the theoretical perspective and propose two tighter error bounds as the additional loss terms to the conventional response regression loss. Finally, we directly model the uplift ranking error for the entire population with a listwise uplift ranking loss. The experiment results on offline public and industrial datasets validate the effectiveness of our method for revenue uplift modeling. Furthermore, we conduct large-scale experiments on a prominent online fintech marketing platform, Tencent FiT, which further demonstrates the superiority of our method in real-world applications.