Name: Artificial Intelligence for Customer Relationship Management: Solving Customer Problems (en Inglés)
Price: 503.93 PEN
Availability: InStock
Author: Galitsky, Boris
ISBN: 9783030616434

portada Artificial Intelligence for Customer Relationship Management: Solving Customer Problems (en Inglés)

Artificial Intelligence for Customer Relationship Management: Solving Customer Problems (en Inglés)

Formato

Libro Físico

Autor

Boris Galitsky

Editorial

Springer

Idioma

Inglés

N° páginas

463

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 2.5 cm

Peso

0.67 kg.

ISBN13

9783030616434

Categorías

Atención al cliente
Inteligencia artificial
Diseño de interfaces de usuario y usabilidad

Artificial Intelligence for Customer Relationship Management: Solving Customer Problems (en Inglés)

Boris Galitsky (Autor) · Springer · Tapa Blanda

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Reseña del libro "Artificial Intelligence for Customer Relationship Management: Solving Customer Problems (en Inglés)"

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.