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Recommender System for Improving Customer Loyalty

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Feldname	Details
Vorliegende Sprache	eng
ISBN	978-3-030-13437-2
Name	Tarnowska, Katarzyna ¬[VerfasserIn]¬
Name	Ras, Zbigniew W. ¬[VerfasserIn]¬
Name ANZEIGE DER KETTE	Ras, Zbigniew W. ¬[VerfasserIn]¬
Name	Daniel, Lynn ¬[VerfasserIn]¬
T I T E L	Recommender System for Improving Customer Loyalty
Verlagsort	Cham
Verlag	Springer
Erscheinungsjahr	2020
Erscheinungsjahr	2020
Umfang	1 Online-Ressource (XVIII, 124 p. 40 illus., 30 illus. in color)
Reihe	Studies in Big Data ; 55
Titelhinweis	Erscheint auch als (Druck-Ausgabe)ISBN: 978-3-030-13437-2
ISBN	ISBN 978-3-030-13438-9
Klassifikation	COM004000
	UYQ
	UYQ
	TEC009000
	006.3
	Q342
Kurzbeschreibung	Chapter 1: Introduction -- Chapter 2: Customer Loyalty Improvement -- Chapter 3: State of the Art -- Chapter 4: Background -- Chapter 5: Overview of Recommender System Engine -- Chapter 6: Visual Data Analysis -- Chapter 7: Improving Performance of Knowledge Miner -- Chapter 8: Recommender System Based on Unstructured Data -- Chapter 9: Customer Attrition Problem -- Chapter 10: Conclusion
2. Kurzbeschreibung	This book presents the Recommender System for Improving Customer Loyalty. New and innovative products have begun appearing from a wide variety of countries, which has increased the need to improve the customer experience. When a customer spends hundreds of thousands of dollars on a piece of equipment, keeping it running efficiently is critical to achieving the desired return on investment. Moreover, managers have discovered that delivering a better customer experience pays off in a number of ways. A study of publicly traded companies conducted by Watermark Consulting found that from 2007 to 2013, companies with a better customer service generated a total return to shareholders that was 26 points higher than the S&P 500. This is only one of many studies that illustrate the measurable value of providing a better service experience. The Recommender System presented here addresses several important issues. (1) It provides a decision framework to help managers determine which actions are likely to have the greatest impact on the Net Promoter Score. (2) The results are based on multiple clients. The data mining techniques employed in the Recommender System allow users to “learn” from the experiences of others, without sharing proprietary information. This dramatically enhances the power of the system. (3) It supplements traditional text mining options. Text mining can be used to identify the frequency with which topics are mentioned, and the sentiment associated with a given topic. The Recommender System allows users to view specific, anonymous comments associated with actual customers. Studying these comments can provide highly accurate insights into the steps that can be taken to improve the customer experience. (4) Lastly, the system provides a sensitivity analysis feature. In some cases, certain actions can be more easily implemented than others. The Recommender System allows managers to “weigh” these actions and determine which ones would have a greater impact
SWB-Titel-Idn	166674610X
Signatur	Springer E-Book
Bemerkungen	Elektronischer Volltext - Campuslizenz
Elektronische Adresse	$uhttps://doi.org/10.1007/978-3-030-13438-9
Internetseite / Link	Resolving-System

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