  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.
