東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Artificial intelligence for customer...

Galitsky, Boris.

Linked to FindBook

Google Book

Amazon

博客來

Artificial intelligence for customer relationship management = solving customer problems /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence for customer relationship management/ by 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.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	User interfaces (Computer systems) -
Online resource:	https://doi.org/10.1007/978-3-030-61641-0
ISBN:	9783030616410

Artificial intelligence for customer relationship management = solving 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

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

539550
User interfaces (Computer systems)

LC Class. No.: QA76.9.U83

Dewey Class. No.: 005.437

Artificial intelligence for customer relationship management = solving customer problems /
LDR:02965nmm a2200337 a 4500 001 2236768
003 DE-He213
005 20201223083755.0
006 m d
007 cr nn 008maaau
008 211111s2021 sz s 0 eng d
020 $a 9783030616410 $q (electronic bk.)
020 $a 9783030616403 $q (paper)
024 7 $a 10.1007/978-3-030-61641-0 $2 doi
035 $a 978-3-030-61641-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.U83
072 7 $a UYZG $2 bicssc
072 7 $a COM070000 $2 bisacsh
072 7 $a UYZG $2 thema
082 0 4 $a 005.437 $2 23
090 $a QA76.9.U83 $b G159 2021
100 1 $a Galitsky, Boris. $3 3166388
245 1 0 $a Artificial intelligence for customer relationship management $h [electronic resource] : $b solving customer problems / $c by Boris Galitsky.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xix, 463 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Human-computer interaction series, $x 1571-5035
505 0 $a 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.
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.
650 0 $a User interfaces (Computer systems) $3 539550
650 0 $a Customer relations $x Management. $3 582814
650 0 $a Artificial intelligence. $3 516317
650 0 $a Computer simulation. $3 525602
650 1 4 $a User Interfaces and Human Computer Interaction. $3 892554
650 2 4 $a Customer Relationship Management. $3 2186425
650 2 4 $a Simulation and Modeling. $3 890873
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Human-computer interaction series. $3 1568009
856 4 0 $u https://doi.org/10.1007/978-3-030-61641-0
950 $a Computer Science (SpringerNature-11645)