AI 中文总结
针对B2B电子商务交易倾向预测难题，提出基于DiCE合成数据生成及PyPARC框架的倾向建模框架，通过生成高质量少数类样本和校准倾向概率，提升预测精度，助力高精度营销活动。
AI 中文摘要
B2B电子商务中的交易倾向预测面临独特挑战，因组织实体采购行为异质，违反SMOTE类内特征同质性假设。本文介绍一个生产部署的倾向建模框架。首先，用基于不同反事实解释（DiCE）的合成数据生成方法取代传统基于SMOTE的增强，其生成的少数类样本分布保真度更高。其次，采用PyPARC分段仿射分类框架生成校准倾向概率，将客户可解释地分割为可操作风险层级。在大规模B2B电子商务平台两年纵向数据上评估，该架构在决策阈值0.8时精度达93.1%，优于基于SMOTE的基线，证明框架在实现高精度营销活动方面的有效性。
英文摘要
Transaction propensity prediction in B2B e commerce presents unique challenges distinct from B2C contexts, primarily due to the heterogeneous procurement behaviors of organizational entities, which violate SMOTE's implicit assumption of within class feature homogeneity. Specifically, B2B buyers exhibit multi modal procurement cycles that render linear interpolation between minority class samples structurally invalid, producing synthetic data that does not represent real purchasing behavior. This paper introduces a production deployed propensity modeling framework designed to address these complexities through two primary contributions. First, we replace conventional SMOTE based augmentation with a synthetic data generation approach leveraging Diverse Counterfactual Explanations (DiCE). This method produces minority class samples with superior distributional fidelity compared to SMOTE, as validated through quantitative proximity analysis and UMAP cluster visualization. Second, we adapt the PyPARC piecewise affine classification framework to generate calibrated propensity probabilities, facilitating the interpretable segmentation of customers into actionable risk tiers. Evaluated on two years of longitudinal data from a large scale B2B e commerce platform with a 1 to 9 class imbalance ratio, the proposed architecture achieves 93.1% precision at a decision threshold of 0.8, a 9.2 percentage point improvement over SMOTE based baselines at the same threshold (83.9%), and a 26.1 point improvement over SMOTE at threshold 0.7 (66.04%), demonstrating consistent superiority across operating points. These results demonstrate the framework's efficacy in enabling high precision marketing campaigns with significant improvements in customer activation and return on investment.
Comments5 pages, 9 figures, 2 tables
