arXiv AI By Joyjit Roy, Samaresh Kumar Singh, Laxmi Shaw

Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles

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arXiv:2606. 07582v1 Announce Type: cross Abstract: Customer churn prediction is essential across data-driven industries such as insurance, digital banking, eCommerce, and subscription platforms, where retaining existing customers is typically more cost-effective than acquiring new ones.

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arXiv AI
Jun 2

ChurnNet: A Optimized Modern AI for Churn Prediction

arXiv:2606. 00169v1 Announce Type: cross Abstract: Increased competition and the growing similarity of products and services offered by retailers have lowered the barriers for customers to switch to competitors.

By Syed Saad Saif, Giulio Maggiore, Paolo Russo, Damiano Distante
Hugging Face Trending Papers
Jul 28

SPARC Segmentation to Prediction via Affine Regression and Counterfactuals

Transaction propensity prediction in B2B e commerce presents unique challenges distinct from B2C contexts, primarily due to the heterogeneous procurement behaviors of organizational entities, which violate SMOTE's implicit assumption of within class feature homogeneity. Specifically, B2B buyers exhibit multi modal procurement cycles that render linear interpolation between minority class samples structurally invalid, producing synthetic data that does not represent real purchasing behavior.