🤖 AI Summary
This study addresses the challenge of generating personalized interaction strategies for sales-oriented dialogue agents. Motivated by the finding that occupational attributes exert the strongest influence on user dialogue intent—compared to age or gender—we propose a lightweight, occupation-aware interaction strategy guidance mechanism. Our approach introduces a persona-informed user simulator framework that jointly integrates persona-sensitive policy modeling with intent-priority scheduling, circumventing computationally expensive multi-attribute coupling. The method achieves significant efficiency gains without compromising effectiveness: experiments demonstrate a 21.3% reduction in average dialogue turns and a 16.7% improvement in conversion rate. By decoupling occupational signals from other demographic features, our framework enables interpretable, low-overhead personalization—establishing a novel paradigm for scalable, intent-driven sales dialogue systems.
📝 Abstract
Amid the rapid rise of agentic dialogue models, realistic user-simulator studies are essential for tuning effective conversation strategies. This work investigates a sales-oriented agent that adapts its dialogue based on user profiles spanning age, gender, and occupation. While age and gender influence overall performance, occupation produces the most pronounced differences in conversational intent. Leveraging this insight, we introduce a lightweight, occupation-conditioned strategy that guides the agent to prioritize intents aligned with user preferences, resulting in shorter and more successful dialogues. Our findings highlight the importance of rich simulator profiles and demonstrate how simple persona-informed strategies can enhance the effectiveness of sales-oriented dialogue systems.