VALOR：基于治疗门控表示的价值感知收入提升建模
VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales
AI总结：
VALOR通过治疗门控稀疏收入网络和成本敏感焦点ZILN目标，提升B2B销售中高价值客户的识别与收入预测能力，实现20%的排名提升和2.7倍的增量收入增长。
摘要：
B2B销售组织必须在零膨胀收入分布中识别'说服性'账户以优化昂贵的人力资源配置。标准提升框架在高维空间中面临治疗信号崩溃和回归校准与高价值'巨鲸'排序不一致的问题。我们引入VALOR（价值感知优化(B2B)收入学习），一个统一框架，包含使用双线性交互防止因果信号崩溃的治疗门控稀疏收入网络。该框架通过新颖的成本敏感焦点ZILN目标优化，结合焦点机制以实现分布鲁棒性和基于财务规模的价值加权排序损失。为了为高触销售计划提供可解释性，我们进一步推导出鲁棒ZILN-GBDT，一种基于树的变体，利用定制的分裂标准处理提升异质性。广泛评估证实了VALOR的主导地位，其在公开基准上实现了20%的排名提升，并在严格的4个月生产A/B测试中实现了每账户2.7倍的增量收入增长。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
