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020 _a9783030616410
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024 7 _a10.1007/978-3-030-61641-0
_2doi
050 4 _aQA76.9.U83
050 4 _aQA76.9.H85
072 7 _aUYZ
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072 7 _aCOM079010
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082 0 4 _a004.019
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100 1 _aGalitsky, Boris.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aArtificial Intelligence for Customer Relationship Management
_h[electronic resource] :
_bSolving Customer Problems /
_cby Boris Galitsky.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXIX, 463 p. 226 illus., 112 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aHuman–Computer Interaction Series,
_x2524-4477
505 0 _aChatbots 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 _aThe 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 _aUser interfaces (Computer systems).
650 0 _aHuman-computer interaction.
650 0 _aCustomer relations
_xManagement.
650 0 _aArtificial intelligence.
650 0 _aComputer simulation.
650 1 4 _aUser Interfaces and Human Computer Interaction.
650 2 4 _aCustomer Relationship Management.
650 2 4 _aArtificial Intelligence.
650 2 4 _aComputer Modelling.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030616403
776 0 8 _iPrinted edition:
_z9783030616427
776 0 8 _iPrinted edition:
_z9783030616434
830 0 _aHuman–Computer Interaction Series,
_x2524-4477
856 4 0 _uhttps://doi.org/10.1007/978-3-030-61641-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c177115
_d177115