9783030616434 - artificial intelligence for customer relationship management: solving customer problems (human–computer interaction series) von galitsky, boris (3 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2021
Serie: Human–Computer Interaction, Buch 124 von 141. Buch 124 von 141 - Human–Computer Interaction
- Softcover
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Weitere BilderSprache: Englisch
Verlag: Springer, 2021
Serie: Human–Computer Interaction, Buch 124 von 141. Buch 124 von 141 - Human–Computer Interaction
- Softcover
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Taschenbuch. Zustand: Neu. Artificial Intelligence for Customer Relationship Management | Solving Customer Problems | Boris Galitsky | Taschenbuch | Human-Computer Interaction Series | xix | Englisch | 2021 | Springer | EAN 9783030616434 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelbe…rg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, 2021
Serie: Human–Computer Interaction, Buch 124 von 141. Buch 124 von 141 - Human–Computer Interaction
- Softcover
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The second volume of this research monograph describes a number of applications of Artificial Intelligence in the field of Customer Relationship Management with the focus of solving customer problems. We design a system that tries to understand the c…ustomer complaint, his mood, and what can be done to resolve an issue with the product or service. To solve a customer problem efficiently, we maintain a dialogue with the customer so that the problem can be clarified and multiple ways to fix it can be sought. We introduce dialogue management based on discourse analysis: a systematic linguistic way to handle the thought process of the author of the content to be delivered. We analyze user sentiments and personal traits to tailor dialogue management to individual customers. We also design a number of dialogue scenarios for CRM with replies following certain patterns and propose virtual and social dialogues for various modalities of communication with a customer.After we learn to detect fake content, deception and hypocrisy, we examine the domain of customer complaints. We simulate mental states, attitudes and emotions of a complainant and try to predict his behavior. Having suggested graph-based formal representations of complaint scenarios, we machine-learn them to identify the best action the customer support organization can chose to retain the complainant as a customer.
