针对企业销售中高价值客户识别难题，提出新模型提升转化效果。
VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales
- 用门控稀疏网络捕捉治疗效应，防止因果信号丢失。
- 在真实测试中使每账户增量收入提升2.7倍，排名能力提高20%。
- 适合需要精准定位高价值客户的销售团队使用。
B2B销售组织需在零膨胀收入分布中识别出“可说服”客户，以优化人力投入。传统提升框架在高维空间中易出现处理信号坍缩，且回归校准与高价值客户排序不匹配。本文提出VALOR（Value-Aware Learning of Optimized B2B Revenue），采用治疗门控稀疏收入网络，通过双线性交互防止因果信号丢失。模型通过新颖的代价敏感焦点零膨胀负二项（Cost-Sensitive Focal-ZILN）目标函数优化，结合分布鲁棒性的焦点机制与基于财务规模加权的排序损失。为提升可解释性，进一步提出基于树的变体Robust ZILN-GBDT，采用自定义分裂准则以捕捉提升异质性。大量实验验证其优越性：在公开基准上排名能力较最先进方法提升20%，并在为期4个月的真实生产A/B测试中实现每账户增量收入2.7倍增长。
原文摘要 · Abstract (English)
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.
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