# Enhancing Uplift Modeling in Multi-Treatment Marketing Campaigns: Leveraging Score Ranking and Calibration Techniques

arXiv: https://arxiv.org/abs/2408.13628 · HTML: https://mlsanity.com/paper/2408.13628/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 24 Aug 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)
- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [Uplift Modeling with Multiple Treatments and General Response Types](https://mlsanity.com/paper/1705.08492/) (2017)
- [Uplift Modeling for Multiple Treatments with Cost Optimization](https://mlsanity.com/paper/1908.05372/) (2019)
- [Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints](https://mlsanity.com/paper/2008.06293/) (2020)
- [Contextual Multi-Armed Bandits for Causal Marketing](https://mlsanity.com/paper/1810.01859/) (2018)

## Abstract (arXiv)

Uplift modeling is essential for optimizing marketing strategies by selecting individuals likely to respond positively to specific marketing campaigns. This importance escalates in multi-treatment marketing campaigns, where diverse treatment is available and we may want to assign the customers to treatment that can make the most impact. While there are existing approaches with convenient frameworks like Causalml, there are potential spaces to enhance the effect of uplift modeling in multi treatment cases. This paper introduces a novel approach to uplift modeling in multi-treatment campaigns, leveraging score ranking and calibration techniques to improve overall performance of the marketing campaign. We review existing uplift models, including Meta Learner frameworks (S, T, X), and their application in real-world scenarios. Additionally, we delve into insights from multi-treatment studies to highlight the complexities and potential advancements in the field. Our methodology incorporates Meta-Learner calibration and a scoring rank-based offer selection strategy. Extensive experiment results with real-world datasets demonstrate the practical benefits and superior performance of our approach. The findings underscore the critical role of integrating score ranking and calibration techniques in refining the performance and reliability of uplift predictions, thereby advancing predictive modeling in marketing analytics and providing actionable insights for practitioners seeking to optimize their campaign strategies.