VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales
This work addresses a critical resource allocation problem for B2B sales organizations, though it appears incremental as it builds on existing uplift modeling frameworks.
The paper tackles the problem of identifying persuadable accounts in B2B sales with zero-inflated revenue distributions, where standard uplift models struggle with signal collapse and misalignment. It introduces VALOR, a unified framework that achieved a 20% improvement in rankability over state-of-the-art methods and a 2.7x increase in incremental revenue per account in a production A/B test.
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-art methods on public benchmarks and delivering a validated 2.7x increase in incremental revenue per account in a rigorous 4-month production A/B test.