Publisher
Springer Science and Business Media LLC
Subject
Marketing,Statistics, Probability and Uncertainty,Strategy and Management,Economics, Econometrics and Finance (miscellaneous)
Reference55 articles.
1. Albert, J., and D. Goldenberg. 2021. E-commerce promotions personalization via online multiple-choice knapsack with uplift modeling. In Proceedings of the 31st ACM ınternational conference on ınformation and knowledge management, 2863–2872. New York: ACM.
2. Ascarza, E. 2018. Retention futility: Targeting high-risk customers might be ineffective. Journal of Marketing Research 55 (1): 80–98.
3. Athey, S., and G. Imbens. 2015. Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences 113 (27): 7353–7360.
4. Baier, D., and B. Stöcker. 2022. Profit uplift modeling for direct marketing campaigns: Approches and applications for online shops. Journal of Business Economics 92: 645–673.
5. Börthas, L., and J.K. Sjölander. 2020. Machine learning based prediction and classification for uplift modeling (Degree project). Stockholm: KTH Royal Insitute of Technology School of Engineering Sciences.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Predictive Analytics for Customer Behavior;Advances in Marketing, Customer Relationship Management, and E-Services;2024-07-12
2. Predicting Potential Customers in Direct Marketing Using Uplift Modelling and Advanced Machine Learning;2023 International Conference on Computer and Applications (ICCA);2023-11-28