A framework integrating explainable AI for feature attribution, survival analysis for time-to-churn modeling, and RFM profiling for behavioral segmentation to support interpretable retention strategies in online retail.
Research on cross-border e-commerce customer churn prediction based on enhanced xgboost algorithm with temporal- spatial features,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Explainability, risk modeling, and segmentation based customer churn analytics for personalized retention in e-commerce
A framework integrating explainable AI for feature attribution, survival analysis for time-to-churn modeling, and RFM profiling for behavioral segmentation to support interpretable retention strategies in online retail.