学习在销售对话中何时退出
Learning When to Quit in Sales Conversations
AI总结：
本研究将销售对话中的动态筛选决策形式化为最优停止问题，开发了基于语言模型的停止智能体，通过模仿回顾性推断的最优策略学习何时退出对话，在保留销售的同时显著减少失败通话时间并提升预期销量。
摘要：
销售人员经常面临动态筛选决策：是继续对话还是放弃它以追求下一个潜在客户。然而，关于这些决策是如何做出的、是否有效，或如何改进它们，人们知之甚少。我们在大量外呼销售的背景下研究这些决策，其中潜在客户充足，但时间稀缺且失败常见。我们将动态筛选决策形式化为一个最优停止问题，并开发了一个基于生成式语言模型的序列决策智能体——一个停止智能体——它通过模仿回顾性推断的最优停止策略来学习是否以及何时退出对话。我们的方法处理高维文本状态，可扩展至大型语言模型，并适用于开源和专有语言模型。应用于一家大型欧洲电信公司的通话时，我们的停止智能体将失败通话上花费的时间减少了54%，同时保留了几乎所有销售；重新分配节省的时间使预期销售增加高达37%。在检查驱动销售人员退出决策的语言线索时，我们发现他们倾向于过度关注消费者不感兴趣的少数显著表达，并误判通话失败风险，这表明他们在做出实时对话决策时存在认知局限。我们的发现突显了人工智能算法纠正认知受限的人类决策并提高销售团队效率的潜力。
英文摘要：
Salespeople frequently face the dynamic screening decision of whether to persist in a conversation or abandon it to pursue the next lead. Yet, little is known about how these decisions are made, whether they are efficient, or how to improve them. We study these decisions in the context of high-volume outbound sales where leads are ample, but time is scarce and failure is common. We formalize the dynamic screening decision as an optimal stopping problem and develop a generative language model-based sequential decision agent - a stopping agent - that learns whether and when to quit conversations by imitating a retrospectively-inferred optimal stopping policy. Our approach handles high-dimensional textual states, scales to large language models, and works with both open-source and proprietary language models. When applied to calls from a large European telecommunications firm, our stopping agent reduces the time spent on failed calls by 54% while preserving nearly all sales; reallocating the time saved increases expected sales by up to 37%. Upon examining the linguistic cues that drive salespeople's quitting decisions, we find that they tend to overweight a few salient expressions of consumer disinterest and mispredict call failure risk, suggesting cognitive bounds on their ability to make real-time conversational decisions. Our findings highlight the potential of artificial intelligence algorithms to correct cognitively-bounded human decisions and improve salesforce efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
