# A Unified Framework for Marketing Budget Allocation

arXiv: https://arxiv.org/abs/1902.01128 · HTML: https://mlsanity.com/paper/1902.01128/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2019.

## Built on by (most cited first, 30 of 33)

- [Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://mlsanity.com/paper/2306.00315/) (2023)
- [E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling](https://mlsanity.com/paper/2108.13298/) (2021)
- [Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketing](https://mlsanity.com/paper/2211.15728/) (2022)
- [LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm](https://mlsanity.com/paper/2201.12585/) (2022)
- [Device-Cloud Collaborative Recommendation via Meta Controller](https://mlsanity.com/paper/2207.03066/) (2022)
- [End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling](https://mlsanity.com/paper/2408.11623/) (2024)
- [Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach](https://mlsanity.com/paper/2105.08313/) (2021)
- [An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint](https://mlsanity.com/paper/2302.04477/) (2023)
- [Decision Focused Causal Learning for Direct Counterfactual Marketing Optimization](https://mlsanity.com/paper/2407.13664/) (2024)
- [Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing](https://mlsanity.com/paper/2402.03379/) (2024)
- [Uplift Modeling: from Causal Inference to Personalization](https://mlsanity.com/paper/2308.09066/) (2023)
- [CausalMMM: Learning Causal Structure for Marketing Mix Modeling](https://mlsanity.com/paper/2406.16728/) (2024)
- [Adversarial Learning for Incentive Optimization in Mobile Payment Marketing](https://mlsanity.com/paper/2112.15434/) (2021)
- [A Framework for Multi-stage Bonus Allocation in meal delivery Platform](https://mlsanity.com/paper/2202.10695/) (2022)
- [Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions](https://mlsanity.com/paper/2207.14798/) (2022)
- [Maximizing the Success Probability of Policy Allocations in Online Systems](https://mlsanity.com/paper/2312.16267/) (2023)
- [Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2309.02669/) (2023)
- [Benchmarking for Deep Uplift Modeling in Online Marketing](https://mlsanity.com/paper/2406.00335/) (2024)
- [Improve ROI with Causal Learning and Conformal Prediction](https://mlsanity.com/paper/2407.01065/) (2024)
- [Algorithmic Fairness: A Tolerance Perspective](https://mlsanity.com/paper/2405.09543/) (2024)
- [Bi-Level Decision-Focused Causal Learning for Large-Scale Marketing Optimization: Bridging Observational and Experimental Data](https://mlsanity.com/paper/2510.19517/) (2025)
- [Enhancing Monotonic Modeling with Spatio-Temporal Adaptive Awareness in Diverse Marketing](https://mlsanity.com/paper/2406.14132/) (2024)
- [DFF: Decision-Focused Fine-tuning for Smarter Predict-then-Optimize with Limited Data](https://mlsanity.com/paper/2501.01874/) (2025)
- [SACO: Sequence-Aware Constrained Optimization Framework for Coupon Distribution in E-commerce](https://mlsanity.com/paper/2508.09198/) (2025)
- [ACQ: A Deployed Two-Stage Framework for Automated Creative Quota Allocation in Large-Scale Online Advertising](https://mlsanity.com/paper/2412.06167/) (2024)
- [Causal-driven attribution (CDA): Estimating channel influence without user-level data](https://mlsanity.com/paper/2512.21211/) (2025)
- [Off-Policy Evaluation and Learning for Survival Outcomes under Censoring](https://mlsanity.com/paper/2603.22900/) (2026)
- [Auditing Marketing Budget Allocation with Hindsight Regret](https://mlsanity.com/paper/2604.25977/) (2026)
- [Learning to Spend: Model Predictive Control for Budgeting under Non-Stationary Returns](https://mlsanity.com/paper/2604.27186/) (2026)
- [Generative Optimization for Incentivized Advertising with Global Level Constraints](https://mlsanity.com/paper/2608.04421/) (2026)

## Abstract (arXiv)

While marketing budget allocation has been studied for decades in traditional business, nowadays online business brings much more challenges due to the dynamic environment and complex decision-making process. In this paper, we present a novel unified framework for marketing budget allocation. By leveraging abundant data, the proposed data-driven approach can help us to overcome the challenges and make more informed decisions. In our approach, a semi-black-box model is built to forecast the dynamic market response and an efficient optimization method is proposed to solve the complex allocation task. First, the response in each market-segment is forecasted by exploring historical data through a semi-black-box model, where the capability of logit demand curve is enhanced by neural networks. The response model reveals relationship between sales and marketing cost. Based on the learned model, budget allocation is then formulated as an optimization problem, and we design efficient algorithms to solve it in both continuous and discrete settings. Several kinds of business constraints are supported in one unified optimization paradigm, including cost upper bound, profit lower bound, or ROI lower bound. The proposed framework is easy to implement and readily to handle large-scale problems. It has been successfully applied to many scenarios in Alibaba Group. The results of both offline experiments and online A/B testing demonstrate its effectiveness.