Applications of data mining algorithms for customer recommendations in retail marketing

Basit Öğe Kaydı
dc.authorid0000-0002-0479-6937
dc.contributor.authorDelice, Elif
dc.contributor.authorPolatlı, Lütviye Özge
dc.contributor.authorDüzdar Argun, İrem
dc.contributor.authorTozan, Hakan
dc.date.accessioned2022-11-28T11:02:20Z
dc.date.available2022-11-28T11:02:20Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Fen Bilimleri Enstitüsü, Sağlık Sistemleri Mühendisliği Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
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.
dc.identifier.citationDelice, E., Polatlı, L. Ö., Düzdar Argun, İ. ve Tozan, H. (2022). Applications of data mining algorithms for customer recommendations in retail marketing. The Future of Data Mining içinde (29-49. ss.). Nova Science Publishers, Inc.
dc.identifier.endpage49
dc.identifier.isbn9798886973150
dc.identifier.isbn9798886972504
dc.identifier.scopus2-s2.0-85141258319
dc.identifier.scopusqualityN/A
dc.identifier.startpage29
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10037
dc.indekslendigikaynakScopus
dc.institutionauthorPolatlı, Lütviye Özge
dc.institutionauthorTozan, Hakan
dc.language.isoen
dc.publisherNova Science Publishers, Inc.
dc.relation.ispartofThe Future of Data Miningen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectData Mining
dc.subjectDecision Trees
dc.subjectK-Means
dc.subjectRandom Forest
dc.subjectX-Means
dc.titleApplications of data mining algorithms for customer recommendations in retail marketing
dc.typeBook Chapter

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Fen Bilimleri Enstitüsü Dergi Yayınlar Koleksiyonu
Kitap Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu