Rethinking Sales Lead Scoring with LLM-based Hierarchical Preference Ranking
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 in...
| Authors | Chenyu Zhang, Yiwen Liu, Yin Sun et al. |
|---|---|
| Primary category | cs.IR |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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