000 | 04054nam a22006015i 4500 | ||
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001 | 978-3-030-61641-0 | ||
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007 | cr nn 008mamaa | ||
008 | 201223s2021 sz | s |||| 0|eng d | ||
020 |
_a9783030616410 _9978-3-030-61641-0 |
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024 | 7 |
_a10.1007/978-3-030-61641-0 _2doi |
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050 | 4 | _aQA76.9.U83 | |
050 | 4 | _aQA76.9.H85 | |
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_a005.437 _223 |
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100 | 1 |
_aGalitsky, Boris. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXIX, 463 p. 226 illus., 112 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aHuman–Computer Interaction Series, _x2524-4477 |
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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. |
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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 |