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.

翻译：随着智能对话模型的迅速兴起，基于真实用户模拟的研究对于优化有效对话策略至关重要。本研究探讨了一种面向销售场景的智能体，该智能体能够根据涵盖年龄、性别和职业的用户画像自适应调整对话内容。研究发现，年龄和性别会影响整体性能，而职业则在对话意图上产生最显著的差异。基于这一洞察，我们提出了一种轻量级的、以职业为条件的策略，引导智能体优先选择符合用户偏好的对话意图，从而实现更简短且更成功的对话。我们的研究结果强调了丰富模拟用户画像的重要性，并展示了如何通过基于用户画像的简单策略来提升面向销售的对话系统的效能。