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Recommender System for Improving Customer Loyalty

  • Katarzyna Tarnowska
  • Zbigniew W. Ras
  • Lynn Daniel
Book

Part of the Studies in Big Data book series (SBD, volume 55)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel
    Pages 1-6
  3. Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel
    Pages 7-11
  4. Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel
    Pages 13-19
  5. Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel
    Pages 21-39
  6. Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel
    Pages 41-57
  7. Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel
    Pages 59-67
  8. Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel
    Pages 69-85
  9. Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel
    Pages 87-111
  10. Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel
    Pages 113-122
  11. Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel
    Pages 123-124

About this book

Introduction

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.


Keywords

CLIRS Big Data Recommender Systems Customer Loyalty Improvement Recommender System Computational Intelligence Customer Retention Meta-action Retraction Recommendation from Action Rules Actionable Knowledge Sentiment Analysis

Authors and affiliations

  • Katarzyna Tarnowska
    • 1
  • Zbigniew W. Ras
    • 2
  • Lynn Daniel
    • 3
  1. 1.Department of Computer ScienceSan Jose State UniversitySan JoseUSA
  2. 2.Department of Computer ScienceUniversity of North CarolinaCharlotteUSA
  3. 3.The Daniel GroupCharlotteUSA

Bibliographic information