asLLR: LLM based Leads Ranking in Auto Sales
Yin Sun, Yiwen Liu, Junjie Song, Chenyu Zhang, Xinyuan Zhang, Lingjie Liu, Siqi Chen, Yuji Cao

TL;DR
This paper introduces asLLR, a novel LLM-based approach for ranking sales leads in auto sales, effectively combining natural language and tabular data modeling to improve prediction accuracy and sales outcomes.
Contribution
The study presents a new LLM-based model integrating CTR and QA losses for better lead ranking, with a custom dataset and significant real-world sales improvements.
Findings
Achieved an AUC of 0.8127, surpassing traditional methods by 0.0231.
Enhanced CTR models with text features by 0.0058.
Increased sales volume by approximately 9.5% in real-world testing.
Abstract
In the area of commercial auto sales system, high-quality lead score sequencing determines the priority of a sale's work and is essential for optimizing the efficiency of the sales system. Since CRM (Customer Relationship Management) system contains plenty of textual interaction features between sales and customers, traditional techniques such as Click Through Rate (CTR) prediction struggle with processing the complex information inherent in natural language features, which limits their effectiveness in sales lead ranking. Bridging this gap is critical for enhancing business intelligence and decision-making. Recently, the emergence of large language models (LLMs) has opened new avenues for improving recommendation systems, this study introduces asLLR (LLM-based Leads Ranking in Auto Sales), which integrates CTR loss and Question Answering (QA) loss within a decoder-only large language…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
