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Artificial intelligence for customer...

Galitsky, Boris.

Artificial intelligence for customer relationship managementsolving customer problems /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence for customer relationship managementby Boris Galitsky.
Reminder of title:	solving customer problems /
Author:	Galitsky, Boris.
Published:	Cham :Springer International Publishing :2021.
Description:	xix, 463 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	User interfaces (Computer systems)
Online resource:	https://doi.org/10.1007/978-3-030-61641-0
ISBN:	9783030616410$q(electronic bk.)

Artificial intelligence for customer relationship managementsolving customer problems /
Galitsky, Boris.

Artificial intelligence for customer relationship managementsolving customer problems /[electronic resource] :by Boris Galitsky. - Cham :Springer International Publishing :2021. - xix, 463 p. :ill. (some col.), digital ;24 cm. - Human-computer interaction series,1571-5035. - Human-computer interaction series..

Chatbots for CRM and Dialogue Management -- Recommendation by Joining a Human Conversation -- Adjusting Chatbot Conversation to User Personality and Mood -- A Virtual Social Promotion Chatbot with Persuasion and Rhetorical Coordination -- Concluding a CRM Session -- Truth, Lie and Hypocrisy -- Reasoning for Resolving Customer Complaints- Concept-based Learning of Complainant's Behavior -- Reasoning and Simulation of Mental Attitudes of a Customer -- CRM Becomes Seriously Ill -- Conclusions.

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 customer 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.

ISBN: 9783030616410$q(electronic bk.)

Standard No.: 10.1007/978-3-030-61641-0doiSubjects--Topical Terms:

189334
User interfaces (Computer systems)

LC Class. No.: QA76.9.U83

Dewey Class. No.: 005.437

Artificial intelligence for customer relationship managementsolving customer problems /
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520 $a 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 customer 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.
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