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

^*Corresponding author for this work

Department of Management Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, A. Cagri Tolga, Selcuk Cebi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	409-416
Number of pages	8
ISBN (Print)	9783031091759
DOIs	https://doi.org/10.1007/978-3-031-09176-6_47
Publication status	Published - 2022
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2022 - Izmir, Turkey Duration: 19 Jul 2022 → 21 Jul 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	505 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2022
Country/Territory	Turkey
City	Izmir
Period	19/07/22 → 21/07/22

Bibliographical note

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

Keywords

Clustering
Dunn
Fuzzy C-means
K-means
Retailing
Silhouette
Store segmentation

Access to Document

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

Cite this

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; Vol. 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. editor / 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).

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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.",

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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",

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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, vol. 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; Vol. 505 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

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SN - 9783031091759

T3 - Lecture Notes in Networks and Systems

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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, editors, 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

Abstract

Publication series

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Bibliographical note

Keywords

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