In this work, we propose a framework relying solely on chat-based customer support (CS) interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Consequently, our main contributions and objectives are to use Natural Language Processing (NLP) to assess and predict the recommendation behavior where, in addition to using static sentiment analysis, we exploit the predictive power of each user's sentiment dynamics. Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.
翻译:在这项工作中,我们提出了一个完全依靠聊天式客户支持(CS)互动来预测个人用户建议决定的框架。在我们的案例研究中,我们分析了拉丁美洲大型电子商务公司财政垂直范围内16.4k用户和48.7k客户支持对话的总数,因此,我们的主要贡献和目标是利用自然语言处理(NLP)来评估和预测建议行为,除了使用静态情绪分析外,我们还利用每个用户情绪动态的预测力。我们的结果表明,根据各自的特性可解释性,可以预测用户仅仅根据他们的CS对话以完全自动化的方式进行信息式情绪演进推荐产品或服务的可能性。