Studies in Big Data

Recommender System for Improving Customer Loyalty

Authors: Tarnowska, Katarzyna, Ras, Zbigniew W., Daniel, Lynn

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  • Presents the Recommender System for Improving Customer Loyalty
  • Describes recommender systems and their applications
  • Written by respected experts in the field
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eBook $109.00
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  • ISBN 978-3-030-13438-9
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Hardcover $149.99
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Softcover $109.99
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About this book

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.

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Table of contents (10 chapters)

Table of contents (10 chapters)

Buy this book

eBook $109.00
price for USA in USD
  • ISBN 978-3-030-13438-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for USA in USD
Softcover $109.99
price for USA in USD
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Bibliographic Information

Bibliographic Information
Book Title
Recommender System for Improving Customer Loyalty
Authors
Series Title
Studies in Big Data
Series Volume
55
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-13438-9
DOI
10.1007/978-3-030-13438-9
Hardcover ISBN
978-3-030-13437-2
Softcover ISBN
978-3-030-13440-2
Series ISSN
2197-6503
Edition Number
1
Number of Pages
XVIII, 124
Number of Illustrations
10 b/w illustrations, 30 illustrations in colour
Topics