Artificial Intelligence for Customer Relationship Management: Solving Customer Problems (Human–Computer Interaction Series) - Hardcover

Über die Autorin bzw. den Autor

Dr. Boris Galitsky has contributed linguistic and machine learning technologies to Silicon Valley startups for the last 25 years, as well as eBay and Oracle, where he is currently an architect of a digital assistant project. An author of two computer science books, 150+ publications and 15+ patents, he is now researching how discourse analysis improves search relevance and supports dialogue management. In his previous book, Dr. Galitsky presented a foundation of autistic reasoning which shed a light on how chatbots should facilitate conversations. Boris is an Apache committer to OpenNLP where he created the OpenNLP.Similarity component that is a basis for chatbot development.

Von der hinteren Coverseite

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.