# Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing

arXiv: https://arxiv.org/abs/2405.15301 · HTML: https://mlsanity.com/paper/2405.15301/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 6 of 6)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Learning Causal Effects on Hypergraphs](https://mlsanity.com/paper/2207.04049/) (2022)
- [Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketing](https://mlsanity.com/paper/2211.15728/) (2022)
- [Response Transformation and Profit Decomposition for Revenue Uplift Modeling](https://mlsanity.com/paper/1911.08729/) (2019)
- [Interpretable Multiple Treatment Revenue Uplift Modeling](https://mlsanity.com/paper/2101.03336/) (2021)

## Built on by (most cited first, 12 of 12)

- [Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework](https://mlsanity.com/paper/2602.12972/) (2026)
- [UpliftBench: Revealing Outcome-Regime and Objective Mismatch in Uplift Evaluation](https://mlsanity.com/paper/2608.00915/) (2026)
- [AdaPRL: Adaptive Pairwise Regression Learning with Uncertainty Estimation for Universal Regression Tasks](https://mlsanity.com/paper/2501.05809/) (2025)
- [The Estimation of Continual Causal Effect for Dataset Shifting Streams](https://mlsanity.com/paper/2504.20471/) (2025)
- [Hidden Representation Clustering with Multi-Task Representation Learning towards Robust Online Budget Allocation](https://mlsanity.com/paper/2506.00959/) (2025)
- [Deep Learning of Continuous and Structured Policies for Aggregated Heterogeneous Treatment Effects](https://mlsanity.com/paper/2507.05511/) (2025)
- [We Have It Covered: A Resampling-based Method for Uplift Model Comparison](https://mlsanity.com/paper/2509.04315/) (2025)
- [HT-GNN: Hyper-Temporal Graph Neural Network for Customer Lifetime Value Prediction in Baidu Ads](https://mlsanity.com/paper/2601.13013/) (2026)
- [VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales](https://mlsanity.com/paper/2604.02472/) (2026)
- [Treatment Effect Estimation with Differentiated Networked Effect on Graph Data](https://mlsanity.com/paper/2605.24358/) (2026)
- [FunnelCausalNet: Funnel-aware Joint Conversion-Revenue Uplift for Multi-tier Coupon Allocation](https://mlsanity.com/paper/2608.11675/) (2026)
- [Marginal Expected Revenue for Jointly Ranking Auction and Fixed-Price Listings in E-Commerce Sponsored Search](https://mlsanity.com/paper/2609.01628/) (2026)

## Abstract (arXiv)

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 textitconversion uplift modeling, textitrevenue 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.