Overview
- Presents the Recommender System for Improving Customer Loyalty
- Describes recommender systems and their applications
- Written by respected experts in the field
Part of the book series: Studies in Big Data (SBD, volume 55)
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Table of contents (10 chapters)
Keywords
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.Authors and Affiliations
About the authors
Bibliographic Information
Book Title: Recommender System for Improving Customer Loyalty
Authors: Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-030-13438-9
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-13437-2Published: 27 March 2019
Softcover ISBN: 978-3-030-13440-2Published: 14 August 2020
eBook ISBN: 978-3-030-13438-9Published: 19 March 2019
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XVIII, 124
Number of Illustrations: 10 b/w illustrations, 30 illustrations in colour
Topics: Computational Intelligence, Customer Relationship Management, Data Mining and Knowledge Discovery, Pattern Recognition