Applications of data mining algorithms for customer recommendations in retail marketing

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dc.authorscopusid26631928800
dc.authorscopusid57953779000
dc.authorscopusid56007981300
dc.authorscopusid25929669700
dc.contributor.authorDelice, E.
dc.contributor.authorPolatli, L.Ö.
dc.contributor.authorArgun, I.D.
dc.contributor.authorTozan, H.
dc.date.accessioned2023-07-26T11:54:45Z
dc.date.available2023-07-26T11:54:45Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractIn recent years, researchers have highlighted how large volumes of data can be transformed into information to determine customer behaviors, and data mining applications have become a major trend. It has become critical for organizations to use a tool for understanding the relationships between data to protect their marketplace by increasing customer loyalty. Thanks to data mining applications, data can be processed and transformed into information, and in this way, target audiences can be determined while developing marketing strategies. This chapter aims to increase the market share with products specific to the customer portfolio, introduce strategic marketing tools for retaining old customers, introduce effective methods for acquiring new customers, and increase the retail sales chart, based on purchasing habits of customers. The data set was collected under pandemic conditions during the COVID-19 process and analyzed to support retail businesses in their online shopping orientation. By examining the local customer base, it was assumed that the customer group would display similar behaviors in online or teleordering methods, customer identification and order estimation were made to follow an effective sales policy. Segmentation was performed with data mining applications, and the grouped data were separated according to their similarities. The data set consisting of demographic characteristics and various product information of the enterprise's customers were analyzed with Decision Tree and Random Forest, which are data mining methods, the best performing algorithm in the data set was selected by comparing the performance of the methods. As a result of the findings, appropriate suggestions were given to the business to determine the purchasing tendencies of the customers and to increase the level of effectiveness in sales-marketing strategies. In this way, materials were presented to assist the enterprise in developing strategies to increase the number of sales by taking faster and more accurate action by avoiding the time and expense that would be lost by the trial-error method. © 2022 Nova Science Publishers, Inc. All rights reserved.en_US
dc.identifier.endpage49en_US
dc.identifier.isbn9798886973150
dc.identifier.isbn9798886972504
dc.identifier.scopus2-s2.0-85141258319en_US
dc.identifier.startpage29en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12915
dc.indekslendigikaynakScopusen_US
dc.institutionauthorArgun, İrem Düzdar
dc.language.isoenen_US
dc.publisherNova Science Publishers, Inc.en_US
dc.relation.ispartofThe Future of Data Miningen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectData miningen_US
dc.subjectDecision treesen_US
dc.subjectK-meansen_US
dc.subjectRandom foresten_US
dc.subjectX-meansen_US
dc.titleApplications of data mining algorithms for customer recommendations in retail marketingen_US
dc.typeBook Chapteren_US

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