Sales lead conversion in high-stakes domains (e.g., automotive, real estate) differs fundamentally from e-commerce recommendation due to prolonged decision cycles and multi-stage funnels. Traditional lead scoring methods rule-based scorecards, machine learning, or pointwise CTR models face severe challenges: sparse supervision, a semantic gap in unstructured CRM logs, and inability to capture relative lead priority. While Large Language Models(LLMs) offer superior semantic understanding of customer interactions, general-purpose LLMs are ill-suited for lead ranking: they generate text rather than comparable scores, and lack alignment with the hierarchical priorities of sales funnels. We introduce an LLM-based discriminative framework for sales lead scoring, which supports joint modeling of structured CRM features and unstructured customer interactions. On top of this framework, we propose HPRO (Hierarchical Preference Ranking Optimization), which augments sales lead scoring with a hierarchical preference ranking objective. HPRO employs a margin-aware Bradley-Terry formulation to transform sparse binary labels into dense, funnel-aware preference pairs, enabling lead scoring to leverage both pointwise and pairwise supervision. Experiments on large-scale data from a leading NEV brand demonstrate state-of-the-art classification (AUC 0.8161) and ranking performance (+39.7% precision among top-ranked leads). A 132-day online A/B test validates 9.5% sales volume uplift, confirming real-world commercial impact.

翻译：高风险领域（如汽车、房地产）的销售线索转化与电子商务推荐存在根本性差异，其决策周期长且涉及多阶段漏斗。传统线索评分方法（基于规则评分卡、机器学习或点式点击率模型）面临严峻挑战：监督信号稀疏、非结构化CRM日志存在语义鸿沟，以及无法捕捉线索相对优先级。尽管大语言模型（LLMs）具备对客户交互的卓越语义理解能力，但通用型LLMs并不适用于线索排序：它们生成的是文本而非可比较的评分值，且与销售漏斗的分层优先级缺乏对齐。我们提出一种基于LLM的判别式销售线索评分框架，该框架支持对结构化CRM特征与非结构化客户交互进行联合建模。基于此框架，我们提出HPRO（分层偏好排序优化），通过分层偏好排序目标增强销售线索评分。HPRO采用基于边界感知的Bradley-Terry公式，将稀疏二元标签转化为包含漏斗感知的密集偏好对，使线索评分能够同时利用点式监督和成对监督。在领先新能源汽车品牌的大规模数据上进行的实验表明，该方法在分类任务（AUC达0.8161）和排序性能（排名前位的线索精确率提升39.7%）均达到当前最优水平。通过132天的在线A/B测试验证，该方法带来9.5%的销量提升，证实了其实际商业价值。