Artificial intelligence for customer relationship management solving customer problems / [electronic resource] :

• 作者： Galitsky, Boris.
• 其他作者：
  • SpringerLink (Online service)
• 其他題名：
  • Human-computer interaction series.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Human-computer interaction series,
• 主題： User interfaces (Computer systems) , Customer relations--Management. , Artificial intelligence. , Computer simulation. , User Interfaces and Human Computer Interaction. , Customer Relationship Management. , Simulation and Modeling.
• ISBN： 9783030616410 (electronic bk.) 、 9783030616403 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Chatbots 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.
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• 摘要註: The 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.
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