Exploring Personality-Aware Interactions in Salesperson Dialogue Agents (2025)

Abstract: The integration of dialogue agents into the sales domain requires a deep understanding of how these systems interact with users possessing diverse personas. This study explores the influence of user personas, defined using the Myers-Briggs Type Indicator (MBTI), on the interaction quality and performance of sales-oriented dialogue agents. Through large-scale testing and analysis, we assess the pre-trained agent’s effectiveness, adaptability, and personalization capabilities across a wide range of MBTI-defined user types. Our findings reveal significant patterns in interaction dynamics, task completion rates, and dialogue naturalness, underscoring the future potential for dialogue agents to refine their strategies to better align with varying personality traits. This work not only provides actionable insights for building more adaptive and user-centric conversational systems in the sales domain but also contributes broadly to the field by releasing persona-defined user simulators. These simulators, unconstrained by domain, offer valuable tools for future research and demonstrate the potential for scaling personalized dialogue systems across diverse applications.

## introduction dialogue systems are becoming essential in the sales industry, enabling businesses to communicate more effectively with their customers @xcite . these aidriven systems assist customers by providing product recommendations, answering inquiries, and supporting better purchasing decisions. however, each user is unique, with communication styles that vary according to their personality traits. to create more effective dialogue systems, it is crucial to account for these individual differences. most conventional conversational systems adopt a "one size fits all" approach, which often results in impersonal interactions. incorporating an understanding of personality differences can enable dialogue systems to tailor their responses and communication styles, enhancing user satisfaction and engagement. personality significantly influences conversational preferences. while some individuals favor concise, direct exchanges, others value detailed and exploratory interactions. for instance, @xcite proposed creating user profiles based on 5-sentence textual descriptions, such as: "i am a vegetarian. i like swimming. my father used to work for ford. my favorite band is maroon5. i got a new job last month, which is about advertising design." current dialogue systems often rely on general user information, such as name, age, preferences, and occupation, to personalize interactions. however, these approaches failed to capture the deeper psychological traits that drive user behavior and shape user preference. to fill in the gap, this study investigates how personality traits, as defined by the myers-briggs type indicator (mbti), influence interaction quality and the performance of sales-oriented dialogue agents. the mbti is a widely used framework that categorizes individuals into 16 personality types based on four key dimensions: extraversion (e) vs. introversion (i), sensing (s) vs. intuition (n), thinking (t) vs. feeling (f), and judging (j) vs. perceiving (p). these dimensions shape how individuals gather information, make decisions, and respond to various communication styles. in this study, we employed salesagent @xcite , a pretrained salesoriented dialogue agent, to investigate how different personality traits influence conversational outcomes. the goal was to explore whether variations in user personalities lead to distinct interaction patterns, thereby highlighting the potential for developing customized dialogue agents tailored for marketing applications @xcite . salesagent @xcite ) is designed to initiate interactions with casual chit-chat before transitioning into task-oriented conversations. we conducted experiments using salesagent with a diverse set of mbti-based user simulators. each simulator represented a specific personality type, characterized by hobbies, occupations, and preferences reflective of their mbti profile. the agent's marketing performance was evaluated across key metrics, including task completion rates and dialogue quality (measured by naturalness, coherence, smoothness, agent aggressiveness, and consistency). our analysis uncovered significant differences in how users with various personality traits interacted with the agent. for instance, some personality types responded positively to direct, structured communication, while others preferred more flexible, open-ended interactions. our findings highlight the importance of personality-based approaches in enhancing dialogue agents. the results revealed clear distinctions in user-agent interactions based on mbti personality types. extraverted (e) users achieved the highest task success rate at 82.7%, while judging (j) users had the lowest at 62.1%. similarly, feeling (f) users sustained conversations on the same topic longer, with a continuation ratio of 40.58%, compared to 31.30% for judging (j) users. these findings demonstrate the need for dialogue agents to adapt their strategies to accommodate diverse personality traits effectively. beyond providing practical recommendations, this study contributes to the broader field of personalized conversational ai by introducing a set of mbti-based persona-defined user simulators. these simulators serve as a valuable resource for future research, enabling the development of more personalized systems across various domains, such as psychological counseling. the insights gained from this work demonstrate the potential for expanding personalized dialogue systems to a wide range of applications. ## personality-defined user simulation to investigate how personality influences user behavior, we examine the interactions between salesagent and personality-defined user simulators @xcite . this section outlines the process of constructing user simulators with assigned personality traits and presents statistics on the resulting user profiles. ## experimental results and analysis in this section, we evaluate the performance of salesagent across different personality traits, focusing on key conversational aspects such as marketing success rates, conversation quality, conversation length, and the agent's thought patterns. the primary objective is to analyze how personality traits influence interaction patterns and assess the feasibility of adapting the agent model for personalized dialogue strategies. ## conclusion in this study, we explored how personality traits, defined by mbti, influence the interactions and performance of sales-oriented dialogue agents. our findings demonstrate that different personality types significantly affect user engagement and the agent's effectiveness, providing a clear framework for evaluating and improving dialogue strategies. additionally, we introduce a persona-defined user simulator dataset, offering a valuable resource for advancing research in personalized dialogue systems and understanding the impact of personality traits across various domains. personalizing conversational models to adapt to individual personality traits represents a promising direction for future work, with the potential to enhance sales interactions and deliver improved customer experiences.