VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales
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 accounts. We introduce VALOR (Value-Aware Revenue Uplift Modeling), 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 satisfy the interpretability requirements of human-in-the-loop sales
programs, we further derive Robust ZILN-GBDT, a tree-based variant
utilizing a custom splitting criterion for uplift heterogeneity. Extensive evaluations demonstrate VALOR’s effectiveness, achieving a 20%
improvement in rankability over state-of-the-art methods on public
benchmarks and delivering a validated 2.7.× increase in incremental revenue per account in a rigorous 4-month production A/B test.
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 accounts. We introduce VALOR (Value-Aware Revenue Uplift Modeling), 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 satisfy the interpretability requirements of human-in-the-loop sales
programs, we further derive Robust ZILN-GBDT, a tree-based variant
utilizing a custom splitting criterion for uplift heterogeneity. Extensive evaluations demonstrate VALOR’s effectiveness, achieving a 20%
improvement in rankability over state-of-the-art methods on public
benchmarks and delivering a validated 2.7.× increase in incremental revenue per account in a rigorous 4-month production A/B test.
