Recommender system for improving customer loyalty / Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel.
Tarnowska, Katarzyna A., author.; Raś, Zbigniew, author.; Daniel, Lynn, author.
2020
QA76.9.I58
Available Online

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Title Recommender system for improving customer loyalty / Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel.
Author Tarnowska, Katarzyna A., author.
ISBN 9783030134389 (electronic book)
3030134385 (electronic book)
9783030134372
DOI https://doi.org/10.1007/978-3-030-13438-9
https://doi.org/10.1007/978-3-030-13
Published Cham, Switzerland : Springer, [2020]
Copyright ©2020
Language English
Description 1 online resource (xviii, 124 pages) : illustrations.
Item Number 10.1007/978-3-030-13438-9 doi
10.1007/978-3-030-13
Call Number QA76.9.I58
Dewey Decimal Classification 005.5/6
Summary 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 impac t.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 29, 2019).
Added Author Raś, Zbigniew, author.
Daniel, Lynn, author.
Series Studies in big data ; v. 55.
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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.

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