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Concluding a CRM Session

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Artificial Intelligence for Customer Relationship Management

Part of the book series: Human–Computer Interaction Series ((HCIS))

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Abstract

We address the issue of how to conclude a CRM session in a comprehensive manner, to satisfy a user with the detailed extended answer with exhaustive information. For a question-answering session, the goal is to enable a user with thorough knowledge related to her initial question, from a simple fact to a comprehensive explanation. In many cases, a lengthy answer text, including multimedia content compiled from multiple sources, is the best. Whereas comprehensive, detailed answer is useful most of the times, in some cases, such an answer needs to defeat a customer claim or demand when it is unreasonable, unfair or is originated from a bad mood. We formulate a problem of finding a defeating reply for a chatbot to force completion of a chatbot session. Defeating a reply is expected to attack the user claims concerning product usability and interaction with customer support and provide an authoritative conclusive answer in an attempt to satisfy this user. We develop a technique to build a representation of a logical argument from discourse structure and to reason about it to confirm or reject this argument. Our evaluation also involves a machine learning approach and confirms that a hybrid system assures the best performance finding a defeating answer from a set of search result candidates.

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  1. Oracle Labs, Redwood Shores, CA, USA

    Boris Galitsky

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  1. Boris Galitsky
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Correspondence to Boris Galitsky .

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Galitsky, B. (2021). Concluding a CRM Session. In: Artificial Intelligence for Customer Relationship Management. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-030-61641-0_5

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  • DOI: https://doi.org/10.1007/978-3-030-61641-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61640-3

  • Online ISBN: 978-3-030-61641-0

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