LEADER 03479nam 2200505 450 001 9910483390703321 005 20210331183601.0 010 $a3-030-61641-X 024 7 $a10.1007/978-3-030-61641-0 035 $a(CKB)4100000011679117 035 $a(DE-He213)978-3-030-61641-0 035 $a(MiAaPQ)EBC6437710 035 $a(PPN)252515358 035 $a(EXLCZ)994100000011679117 100 $a20210316d2021 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial intelligence for customer relationship management $esolving customer problems /$fBoris Galitsky 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XIX, 463 p. 226 illus., 112 illus. in color.) 225 1 $aHuman-Computer Interaction Series 311 $a3-030-61640-1 327 $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. 330 $aThe second volume of this research monograph describes a number of applications of Artificial Intelligence in the field of Customer Relationship Management with the focus of solving customer problems. We design a system that tries to understand the customer complaint, his mood, and what can be done to resolve an issue with the product or service. To solve a customer problem efficiently, we maintain a dialogue with the customer so that the problem can be clarified and multiple ways to fix it can be sought. We introduce dialogue management based on discourse analysis: a systematic linguistic way to handle the thought process of the author of the content to be delivered. We analyze user sentiments and personal traits to tailor dialogue management to individual customers. We also design a number of dialogue scenarios for CRM with replies following certain patterns and propose virtual and social dialogues for various modalities of communication with a customer. After we learn to detect fake content, deception and hypocrisy, we examine the domain of customer complaints. We simulate mental states, attitudes and emotions of a complainant and try to predict his behavior. Having suggested graph-based formal representations of complaint scenarios, we machine-learn them to identify the best action the customer support organization can chose to retain the complainant as a customer. 410 0$aHuman-computer interaction series. 606 $aUser interfaces (Computer systems) 606 $aCustomer relations$xManagement 606 $aArtificial intelligence 615 0$aUser interfaces (Computer systems) 615 0$aCustomer relations$xManagement. 615 0$aArtificial intelligence. 676 $a005.437 700 $aGalitsky$b Boris$0860187 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483390703321 996 $aArtificial intelligence for customer relationship management$91919320 997 $aUNINA