.png){kind=icon}
.png){kind=icon}
Return
A prescriptive analytics framework for jointly optimizing retention incentives and targeting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2025.114649.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Joint optimization of targeting and incentives with analytical conditions. • Closed form incentive (linear) and unique optimum for logistic under a mild bound. • Predictor-agnostic prescriptive layer driven by the posterior churn share. • Evaluation on a 75/25 train/test split across FIS, Logit, RF, NB, and XGBoost. • Consistent test-set profit gains; best performance from XGBoost with a sigmoid.
Keywords:
Prescriptive analytics
Profit-driven
Churn
Retention incentives
Mamdani fuzzy inference
Logistic response
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W