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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/32556
Title: Unsupervised ranking of clients: machine learning approach to define a "good customer"
Authors: Parkhimenka, U.
Tatur, M.
Khandogina, O.
Keywords: публикации ученых;loyalty ladder;ranking;ecommerce;automatic marketing decision-making;machine learning;data mining & knowledge discovery;latent variable analysis
Issue Date: 2017
Publisher: Faculty of Management Science and Informatics
Citation: Parkhimenka, U. Unsupervised ranking of clients: machine learning approach to define a "good customer" / U. Parkhimenka, M. Tatur, O. Khandogina // Central European Researchers Journal. – 2017. - Volume 3, Issue 2. - Pp. 10 - 15.
Abstract: Ranking of clientsis a natural problem for every business. Though usually it can be solved by common sense and intuition of managers, in the case of a big business entity (e.g. global online stores), the problem becomes more complicated with obvious obstacles in derivation of fast and accurate solution. This article deals with the clients ranking problem using machine learning methodology.
URI: https://libeldoc.bsuir.by/handle/123456789/32556
Appears in Collections:Публикации в зарубежных изданиях

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