This 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.

Offers a foundation for making CRM applications intelligent Provides formalisms to determine human attitude and behavior so that a CRM system can better communicate with and guide customers Enriches a hands-on experience with CRM automation Includes complete instructions for building a chatbot for a CRM that provides customers with information, attempts to resolve their problems and complaints, as well as performing transactions for them

Autorentext

Inhalt
Introduction.- 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 <p