[2604.02472] VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales

Computer Science > Machine Learning arXiv:2604.02472 (cs) [Submitted on 2 Apr 2026] Title:VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales Authors:Vamshi Guduguntla, Kavin Soni, Debanshu Das View a PDF of the paper titled VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales, by Vamshi Guduguntla and 2 other authors View PDF HTML (experimental) Abstract:B2B sales organizations must identify "persuadable" accounts within zero-inflated revenue distributions to optimize expensive human resource allocation. Standard uplift frameworks struggle with treatment signal collapse in high-dimensional spaces and a misalignment between regression calibration and the ranking of high-value "whales." We introduce VALOR (Value Aware Learning of Optimized (B2B) Revenue), a unified framework featuring a Treatment-Gated Sparse-Revenue Network that uses bilinear interaction to prevent causal signal collapse. The framework is optimized via a novel Cost-Sensitive Focal-ZILN objective that combines a focal mechanism for distributional robustness with a value-weighted ranking loss that scales penalties based on financial magnitude. To provide interpretability for high-touch sales programs, we further derive Robust ZILN-GBDT, a tree based variant utilizing a custom splitting criterion for uplift heterogeneity. Extensive evaluations confirm VALOR's dominance, achieving a 20% improvement in rankability over state-of-the...