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

Vamshi Guduguntla, Kavin Soni, Debanshu Das

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-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.

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 "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.

Paper Structure

This paper contains 39 sections, 1 theorem, 9 equations, 2 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. The Gap in Current Methodologies
  3. Our Contributions
  4. Related Work
  5. Problem Formulation
  6. The Zero-Inflated Distribution
  7. Proposed Architecture: VALOR
  8. The Treatment-Gated Sparse-Revenue Network
  9. Proposed Hybrid Objective: Cost-Sensitive Focal-ZILN
  10. Focal-ZILN Loss ($\mathcal{L}_{FL-ZILN}$)
  11. Value-Weighted Ranking Loss ($\mathcal{L}_{V-Rank}$)
  12. Treatment-Gated Representation Learning
  13. Interpretable Variant: Robust ZILN-GBDT
  14. Experimental Evaluation
  15. Datasets
  16. ...and 24 more sections

Key Result

proposition 1

Minimizing the Value-Weighted Ranking Loss is equivalent to maximizing a smooth, convex lower bound on the Value-Weighted Pairwise Accuracy, thereby directly optimizing the expected revenue capture (Qini) rather than pointwise fit. $\blacktriangleleft$$\blacktriangleleft$

Figures (2)

  • Figure 1: The VALOR training pipeline processes features ($X$) and treatment ($T$) through three stages: (1) A Treatment-Gated Interaction Module uses a sigmoid gate to dynamically re-weight features, mitigating the "vanishing treatment" signal; (2) Sparse-Revenue Mixture Heads decouple the decision ($\pi$) and value ($\mu, \sigma$) processes to model zero-inflated revenue; and (3) A Hybrid Objective combines Focal-ZILN and Value-Weighted Ranking losses to ensure distributional robustness and maximize top-decile revenue capture.
  • Figure 2: End-to-end system architecture for the deployed VALOR framework. Program 2 utilizes a hybrid quarterly pacing strategy (VALOR prioritization followed by legacy propensity fallback). Treated accounts pass through Opportunity and Conversion gates before all accounts enter a mandatory 90-day cooling-off period.

Theorems & Definitions (1)

  • proposition 1