Advancing E-commerce Merchants Telemarketing with Synthetic Data-Driven LLMs

2025-12-30 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Marketing, Branding & Advertising, E-commerce & Digital Commerce · Depth: Advanced, quick

Summary

A new hybrid data synthesis framework has been developed to enhance large language models (LLMs) for e-commerce merchant telemarketing, addressing challenges like poor strategy-following and insufficient expert knowledge in real-world sales data. The framework unifies input schemas with sales-designed profiles and strategies, extracted via a multi-task paradigm. It also incorporates Role-playing Simulation and Session Prefix Completion to improve strategy adherence and inject long-tail expert knowledge into the fine-tuned model, named TeleBot. Online and offline evaluations confirm TeleBot's effectiveness, with its High Intention Rate matching the performance of the top 25% of human sales agents.

Key takeaway

For NLP Engineers developing dialogue systems for specialized sales contexts, you should consider hybrid data synthesis frameworks. This approach can overcome limitations of real-world data, such as strategic deviations and knowledge gaps, enabling your LLMs to achieve performance comparable to top human agents in complex telemarketing scenarios.

Key insights

Synthetic data generation can significantly improve LLM performance in complex, strategy-driven telemarketing dialogues.

Principles

• Real-world sales data often lacks strategic coherence.
• Expert knowledge is frequently sparse in collected corpora.

Method

The method involves unifying input schemas with sales profiles/strategies, extracting them via multi-task learning, and using Role-playing Simulation and Session Prefix Completion for data synthesis.

In practice

• Unify input schemas for consistent strategy application.
• Employ role-playing for strategy-following improvement.
• Use session prefix completion for expert knowledge injection.

Topics

• Telemarketing Automation
• Large Language Models
• Synthetic Data Generation
• Multi-task Learning
• Role-playing Simulation

Best for: NLP Engineer, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, Marketing Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.