LEADER 04532nam 22006375 450 001 9910447243903321 005 20250818102349.0 010 $a3-030-52167-2 024 7 $a10.1007/978-3-030-52167-7 035 $a(CKB)4100000011645138 035 $a(DE-He213)978-3-030-52167-7 035 $a(MiAaPQ)EBC6421895 035 $a(PPN)252515366 035 $a(EXLCZ)994100000011645138 100 $a20201207d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence for Customer Relationship Management $eKeeping Customers Informed /$fby Boris Galitsky 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XI, 445 p. 261 illus., 147 illus. in color.) 225 1 $aHuman?Computer Interaction Series,$x2524-4477 311 08$a3-030-52166-4 327 $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. 330 $aThis research monograph brings AI to the field of Customer Relationship Management (CRM) to make a customer experience with a product or service smart and enjoyable. AI is here to help customers to get a refund for a canceled flight, unfreeze a banking account or get a health test result. Today, CRM has evolved from storing and analyzing customers? data to predicting and understanding their behavior by putting a CRM system in a customers? shoes. Hence advanced reasoning with learning from small data, about customers? attitudes, introspection, reading between the lines of customer communication and explainability need to come into play. Artificial Intelligence for Customer Relationship Management leverages a number of Natural Language Processing (NLP), Machine Learning (ML), simulation and reasoning techniques to enable CRM with intelligence. An effective and robust CRM needs to be able to chat with customers, providing desired information, completing their transactions and resolving their problems. It introduces a systematic means of ascertaining a customers? frame of mind, their intents and attitudes to determine when to provide a thorough answer, a recommendation, an explanation, a proper argument, timely advice and promotion or compensation. The author employs a spectrum of ML methods, from deterministic to statistical to deep, to predict customer behavior and anticipate possible complaints, assuring customer retention efficiently. Providing a forum for the exchange of ideas in AI, this book provides a concise yet comprehensive coverage of methodologies, tools, issues, applications, and future trends for professionals, managers, and researchers in the CRM field together with AI and IT professionals. . 410 0$aHuman?Computer Interaction Series,$x2524-4477 606 $aUser interfaces (Computer systems) 606 $aHuman-computer interaction 606 $aCustomer relations$xManagement 606 $aArtificial intelligence 606 $aComputer simulation 606 $aUser Interfaces and Human Computer Interaction 606 $aCustomer Relationship Management 606 $aArtificial Intelligence 606 $aComputer Modelling 615 0$aUser interfaces (Computer systems) 615 0$aHuman-computer interaction. 615 0$aCustomer relations$xManagement. 615 0$aArtificial intelligence. 615 0$aComputer simulation. 615 14$aUser Interfaces and Human Computer Interaction. 615 24$aCustomer Relationship Management. 615 24$aArtificial Intelligence. 615 24$aComputer Modelling. 676 $a005.437 700 $aGalitsky$b Boris$0860187 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910447243903321 996 $aArtificial intelligence for customer relationship management$91919320 997 $aUNINA