LEADER 04836nam 22006135 450 001 9910739424503321 005 20200701223923.0 010 $a3-030-13438-5 024 7 $a10.1007/978-3-030-13438-9 035 $a(CKB)4100000007810355 035 $a(MiAaPQ)EBC5738727 035 $a(DE-He213)978-3-030-13438-9 035 $a(PPN)243767552 035 $a(EXLCZ)994100000007810355 100 $a20190319d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecommender System for Improving Customer Loyalty /$fby Katarzyna Tarnowska, Zbigniew W. Ras, Lynn Daniel 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (133 pages) $cillustrations 225 1 $aStudies in Big Data,$x2197-6503 ;$v55 311 $a3-030-13437-7 327 $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. 330 $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 impact. 410 0$aStudies in Big Data,$x2197-6503 ;$v55 606 $aComputational intelligence 606 $aCustomer relations?Management 606 $aData mining 606 $aPattern recognition 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aCustomer Relationship Management$3https://scigraph.springernature.com/ontologies/product-market-codes/513050 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aComputational intelligence. 615 0$aCustomer relations?Management. 615 0$aData mining. 615 0$aPattern recognition. 615 14$aComputational Intelligence. 615 24$aCustomer Relationship Management. 615 24$aData Mining and Knowledge Discovery. 615 24$aPattern Recognition. 676 $a001.64 676 $a005.56 700 $aTarnowska$b Katarzyna$4aut$4http://id.loc.gov/vocabulary/relators/aut$01424517 702 $aRas$b Zbigniew W$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aDaniel$b Lynn$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910739424503321 996 $aRecommender System for Improving Customer Loyalty$93553759 997 $aUNINA