Paper Type
Complete
Paper Number
1360
Description
One effective solution for companies to expand their market share is to assign customers to optimal marketing strategy, also known as treatments, which are widely employed in randomized controlled trials or A/B tests. However, achieving optimal treatment assignment poses great challenges due to the limitations such as financial constraints and ethical issues associated with randomized controlled trials. To address the challenges, we propose a counterfactual-based uplift modeling approach. This approach involves generating counterfactual treatments and estimating corresponding effects using supervised learning models, ultimately determining the optimal treatment. Our methods have been evaluated on both synthetic and real-world data, demonstrating superior performance compared to other uplift modeling approaches in terms of the Qini coefficient. This study not only contributes to the research on causal inference in the business field but also offers practical implications for companies seeking to enhance business performance through effective marketing treatment assignment.
Recommended Citation
Zhan, Baoqiang; Ngai, Eric W. T.; and Wu, Chong, "Assigning the Optimal Treatment for Your Customers: A Counterfactual-based Uplift Modeling Approach" (2024). PACIS 2024 Proceedings. 3.
https://aisel.aisnet.org/pacis2024/track03_ba/track03_ba/3
Assigning the Optimal Treatment for Your Customers: A Counterfactual-based Uplift Modeling Approach
One effective solution for companies to expand their market share is to assign customers to optimal marketing strategy, also known as treatments, which are widely employed in randomized controlled trials or A/B tests. However, achieving optimal treatment assignment poses great challenges due to the limitations such as financial constraints and ethical issues associated with randomized controlled trials. To address the challenges, we propose a counterfactual-based uplift modeling approach. This approach involves generating counterfactual treatments and estimating corresponding effects using supervised learning models, ultimately determining the optimal treatment. Our methods have been evaluated on both synthetic and real-world data, demonstrating superior performance compared to other uplift modeling approaches in terms of the Qini coefficient. This study not only contributes to the research on causal inference in the business field but also offers practical implications for companies seeking to enhance business performance through effective marketing treatment assignment.
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