Store Segmentation in Retail Industry Using Clustering Algorithms

Ayşegül Ünal; Merve Önal; Tolga Kaya; Tuncay Özcan

doi:10.1007/978-3-031-09176-6_47

Store Segmentation in Retail Industry Using Clustering Algorithms

Ayşegül Ünal, Merve Önal^*, Tolga Kaya, Tuncay Özcan

^*Bu çalışma için yazışmadan sorumlu yazar

İşletme Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

Özet

In today’s digital age, the development of technology has made it easier for customers to reach everything. Store segmentation, which is one of the new methods, can be done in order to survive in the competitive environment due to the increase in retail companies. By doing this, they can gain an advantage by developing target marketing strategies specific to each segment instead of a whole marketing strategy. In this study, the data of 101 stores of a retail company were segmented according to 9 variables. These variables include the location of the stores, income levels, invoice numbers, inventory turnover, etc. has. Fuzzy C-means and K-means clustering algorithms were used for this study. Optimal cluster numbers were determined as 8 for Fuzzy C-means in terms of Dunn index and 7 for K-Means in terms of Silhouette index, which measure the effectiveness of clustering study.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2
Editörler	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, A. Cagri Tolga, Selcuk Cebi
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	409-416
Sayfa sayısı	8
ISBN (Basılı)	9783031091759
DOI'lar	https://doi.org/10.1007/978-3-031-09176-6_47
Yayın durumu	Yayınlandı - 2022
Etkinlik	International Conference on Intelligent and Fuzzy Systems, INFUS 2022 - Izmir, Turkey Süre: 19 Tem 2022 → 21 Tem 2022

Yayın serisi

Adı	Lecture Notes in Networks and Systems
Hacim	505 LNNS
ISSN (Basılı)	2367-3370
ISSN (Elektronik)	2367-3389

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	International Conference on Intelligent and Fuzzy Systems, INFUS 2022
Ülke/Bölge	Turkey
Şehir	Izmir
Periyot	19/07/22 → 21/07/22

Bibliyografik not

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Belgeye Erişim

10.1007/978-3-031-09176-6_47

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Ünal, A., Önal, M., Kaya, T., & Özcan, T. (2022). Store Segmentation in Retail Industry Using Clustering Algorithms. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, A. C. Tolga, & S. Cebi (Eds.), Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2 (pp. 409-416). (Lecture Notes in Networks and Systems; Hac. 505 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-09176-6_47

Ünal, Ayşegül ; Önal, Merve ; Kaya, Tolga et al. / Store Segmentation in Retail Industry Using Clustering Algorithms. Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2. editör / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; A. Cagri Tolga ; Selcuk Cebi. Springer Science and Business Media Deutschland GmbH, 2022. pp. 409-416 (Lecture Notes in Networks and Systems).

@inproceedings{8746b41a0b7849e9a8960d63b4f9ece4,

title = "Store Segmentation in Retail Industry Using Clustering Algorithms",

abstract = "In today{\textquoteright}s digital age, the development of technology has made it easier for customers to reach everything. Store segmentation, which is one of the new methods, can be done in order to survive in the competitive environment due to the increase in retail companies. By doing this, they can gain an advantage by developing target marketing strategies specific to each segment instead of a whole marketing strategy. In this study, the data of 101 stores of a retail company were segmented according to 9 variables. These variables include the location of the stores, income levels, invoice numbers, inventory turnover, etc. has. Fuzzy C-means and K-means clustering algorithms were used for this study. Optimal cluster numbers were determined as 8 for Fuzzy C-means in terms of Dunn index and 7 for K-Means in terms of Silhouette index, which measure the effectiveness of clustering study.",

keywords = "Clustering, Dunn, Fuzzy C-means, K-means, Retailing, Silhouette, Store segmentation",

author = "Ay{\c s}eg{\"u}l {\"U}nal and Merve {\"O}nal and Tolga Kaya and Tuncay {\"O}zcan",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2022 ; Conference date: 19-07-2022 Through 21-07-2022",

year = "2022",

doi = "10.1007/978-3-031-09176-6_47",

language = "English",

isbn = "9783031091759",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "409--416",

editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Basar Oztaysi and Sari, {Irem Ucal} and Tolga, {A. Cagri} and Selcuk Cebi",

booktitle = "Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2",

address = "Germany",

}

Ünal, A, Önal, M, Kaya, T & Özcan, T 2022, Store Segmentation in Retail Industry Using Clustering Algorithms. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, AC Tolga & S Cebi (eds), Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2. Lecture Notes in Networks and Systems, hac. 505 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 409-416, International Conference on Intelligent and Fuzzy Systems, INFUS 2022, Izmir, Turkey, 19/07/22. https://doi.org/10.1007/978-3-031-09176-6_47

Store Segmentation in Retail Industry Using Clustering Algorithms. / Ünal, Ayşegül; Önal, Merve; Kaya, Tolga et al.
Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; A. Cagri Tolga; Selcuk Cebi. Springer Science and Business Media Deutschland GmbH, 2022. p. 409-416 (Lecture Notes in Networks and Systems; Hac. 505 LNNS).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Store Segmentation in Retail Industry Using Clustering Algorithms

AU - Ünal, Ayşegül

AU - Önal, Merve

AU - Kaya, Tolga

AU - Özcan, Tuncay

PY - 2022

Y1 - 2022

N2 - In today’s digital age, the development of technology has made it easier for customers to reach everything. Store segmentation, which is one of the new methods, can be done in order to survive in the competitive environment due to the increase in retail companies. By doing this, they can gain an advantage by developing target marketing strategies specific to each segment instead of a whole marketing strategy. In this study, the data of 101 stores of a retail company were segmented according to 9 variables. These variables include the location of the stores, income levels, invoice numbers, inventory turnover, etc. has. Fuzzy C-means and K-means clustering algorithms were used for this study. Optimal cluster numbers were determined as 8 for Fuzzy C-means in terms of Dunn index and 7 for K-Means in terms of Silhouette index, which measure the effectiveness of clustering study.

AB - In today’s digital age, the development of technology has made it easier for customers to reach everything. Store segmentation, which is one of the new methods, can be done in order to survive in the competitive environment due to the increase in retail companies. By doing this, they can gain an advantage by developing target marketing strategies specific to each segment instead of a whole marketing strategy. In this study, the data of 101 stores of a retail company were segmented according to 9 variables. These variables include the location of the stores, income levels, invoice numbers, inventory turnover, etc. has. Fuzzy C-means and K-means clustering algorithms were used for this study. Optimal cluster numbers were determined as 8 for Fuzzy C-means in terms of Dunn index and 7 for K-Means in terms of Silhouette index, which measure the effectiveness of clustering study.

KW - Clustering

KW - Dunn

KW - Fuzzy C-means

KW - K-means

KW - Retailing

KW - Silhouette

KW - Store segmentation

UR - http://www.scopus.com/inward/record.url?scp=85135063149&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-09176-6_47

DO - 10.1007/978-3-031-09176-6_47

M3 - Conference contribution

AN - SCOPUS:85135063149

SN - 9783031091759

T3 - Lecture Notes in Networks and Systems

SP - 409

EP - 416

BT - Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Tolga, A. Cagri

A2 - Cebi, Selcuk

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2022

Y2 - 19 July 2022 through 21 July 2022

ER -

Ünal A, Önal M, Kaya T , Özcan T. Store Segmentation in Retail Industry Using Clustering Algorithms. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Tolga AC, Cebi S, editörler, Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2. Springer Science and Business Media Deutschland GmbH. 2022. p. 409-416. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-09176-6_47

Store Segmentation in Retail Industry Using Clustering Algorithms

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap