000 | 04533nam a22006015i 4500 | ||
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001 | 978-3-030-52167-7 | ||
003 | DE-He213 | ||
005 | 20240423125414.0 | ||
007 | cr nn 008mamaa | ||
008 | 201207s2020 sz | s |||| 0|eng d | ||
020 |
_a9783030521677 _9978-3-030-52167-7 |
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024 | 7 |
_a10.1007/978-3-030-52167-7 _2doi |
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050 | 4 | _aQA76.9.U83 | |
050 | 4 | _aQA76.9.H85 | |
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_aUYZ _2bicssc |
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_aUYZ _2thema |
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082 | 0 | 4 |
_a005.437 _223 |
082 | 0 | 4 |
_a004.019 _223 |
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] : _bKeeping Customers Informed / _cby Boris Galitsky. |
250 | _a1st ed. 2020. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020. |
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300 |
_aXI, 445 p. 261 illus., 147 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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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aHuman–Computer Interaction Series, _x2524-4477 |
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505 | 0 | _aIntroduction -- Distributional Semantics for CRM: Making word2vec Models Robust by Structurizing Them -- Employing Abstract Meaning Representation to Lay the Last Mile towards Reading Comprehension -- Summarized Logical Forms for Controlled Question Answering -- Summarized Logical Forms based on Abstract Meaning Representation and Discourse Trees -- Acquiring New Definitions of Entities -- Inferring Logical Clauses for Answering Complex Multi-hop Open Domain Questions -- Managing Customer Relations in an Explainable Way -- Recognizing Abstract Classes of Text Based on Discourse -- Conversational Explainability for CRM. | |
520 | _aThis research monograph brings AI to the field of Customer Relationship Management (CRM) to make a customer experience with a product or service smart and enjoyable. AI is here to help customers to get a refund for a canceled flight, unfreeze a banking account or get a health test result. Today, CRM has evolved from storing and analyzing customers’ data to predicting and understanding their behavior by putting a CRM system in a customers’ shoes. Hence advanced reasoning with learning from small data, about customers’ attitudes, introspection, reading between the lines of customer communication and explainability need to come into play. Artificial Intelligence for Customer Relationship Management leverages a number of Natural Language Processing (NLP), Machine Learning (ML), simulation and reasoning techniques to enable CRM with intelligence. An effective and robust CRM needs to be able to chat with customers, providing desired information, completing their transactions and resolving their problems. It introduces a systematic means of ascertaining a customers’ frame of mind, their intents and attitudes to determine when to provide a thorough answer, a recommendation, an explanation, a proper argument, timely advice and promotion or compensation. The author employs a spectrum of ML methods, from deterministic to statistical to deep, to predict customer behavior and anticipate possible complaints, assuring customer retention efficiently. Providing a forum for the exchange of ideas in AI, this book provides a concise yet comprehensive coverage of methodologies, tools, issues, applications, and future trends for professionals, managers, and researchers in the CRM field together with AI and IT professionals. . | ||
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: _z9783030521660 |
776 | 0 | 8 |
_iPrinted edition: _z9783030521684 |
776 | 0 | 8 |
_iPrinted edition: _z9783030521691 |
830 | 0 |
_aHuman–Computer Interaction Series, _x2524-4477 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-52167-7 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c177581 _d177581 |