| 书目名称 | Recommender System for Improving Customer Loyalty |
| 编辑 | Katarzyna Tarnowska,Zbigniew W. Ras,Lynn Daniel |
| 视频video | |
| 概述 | Presents the Recommender System for Improving Customer Loyalty.Describes recommender systems and their applications.Written by respected experts in the field |
| 丛书名称 | Studies in Big Data |
| 图书封面 |  |
| 描述 | .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 |
| 出版日期 | Book 2020 |
| 关键词 | CLIRS; Big Data; Recommender Systems; Customer Loyalty Improvement Recommender System; Computational Int |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-13438-9 |
| isbn_softcover | 978-3-030-13440-2 |
| isbn_ebook | 978-3-030-13438-9Series ISSN 2197-6503 Series E-ISSN 2197-6511 |
| issn_series | 2197-6503 |
| copyright | Springer Nature Switzerland AG 2020 |
发表于 2025-3-21 17:31:18
