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000921809 019__ $$a1105186189
000921809 020__ $$a9783030134389$$q(electronic book)
000921809 020__ $$a3030134385$$q(electronic book)
000921809 020__ $$z9783030134372
000921809 0247_ $$a10.1007/978-3-030-13438-9$$2doi
000921809 0247_ $$a10.1007/978-3-030-13
000921809 035__ $$aSP(OCoLC)on1090810939
000921809 035__ $$aSP(OCoLC)1090810939$$z(OCoLC)1105186189
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000921809 049__ $$aISEA
000921809 050_4 $$aQA76.9.I58
000921809 08204 $$a005.5/6$$223
000921809 1001_ $$aTarnowska, Katarzyna A.,$$eauthor.
000921809 24510 $$aRecommender system for improving customer loyalty /$$cKatarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel.
000921809 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2020]
000921809 264_4 $$c©2020
000921809 300__ $$a1 online resource (xviii, 124 pages) :$$billustrations.
000921809 336__ $$atext$$btxt$$2rdacontent
000921809 337__ $$acomputer$$bc$$2rdamedia
000921809 338__ $$aonline resource$$bcr$$2rdacarrier
000921809 4901_ $$aStudies in big data,$$x2197-6503 ;$$vvolume 55
000921809 504__ $$aIncludes bibliographical references.
000921809 5050_ $$aChapter 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.
000921809 506__ $$aAccess limited to authorized users.
000921809 520__ $$aThis 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.
000921809 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 29, 2019).
000921809 650_0 $$aRecommender systems (Information filtering)
000921809 650_0 $$aCustomer loyalty.
000921809 7001_ $$aRaś, Zbigniew,$$eauthor.
000921809 7001_ $$aDaniel, Lynn,$$eauthor.
000921809 830_0 $$aStudies in big data ;$$vv. 55.
000921809 852__ $$bebk
000921809 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-13438-9$$zOnline Access$$91397441.1
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000921809 980__ $$aBIB
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