Fuzzy RFM Analysis: An Application in E-Commerce

Basar Oztaysi; Mert Kavi

doi:10.1007/978-3-030-51156-2_143

Fuzzy RFM Analysis: An Application in E-Commerce

Basar Oztaysi^*, Mert Kavi

^*Corresponding author for this work

Department of Industrial Engineering

Modanisa Head Office

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

1 Citation (Scopus)

Abstract

RFM (recency, frequency, monetary) analysis is an essential tool for customer segmentation, which is very important for marketing, communication, and even operations management activities. RFM is a widely adopted segmentation tool since it can be accomplished by using purchase transactions. In this study, we use the purchase transactions of Modanisa.com, which is a global e-commerce website from Turkey, to build a fuzzy RFM module. In the first step, the transaction data is converted to R, F, M data parameters, and then the fuzzy c-means algorithm is used to build the RFM clusters.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques
Subtitle of host publication	Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A. Cagri Tolga
Publisher	Springer
Pages	1225-1232
Number of pages	8
ISBN (Print)	9783030511555
DOIs	https://doi.org/10.1007/978-3-030-51156-2_143
Publication status	Published - 2021
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2020 - Istanbul, Turkey Duration: 21 Jul 2020 → 23 Jul 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1197 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2020
Country/Territory	Turkey
City	Istanbul
Period	21/07/20 → 23/07/20

Bibliographical note

Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

E-commerce
Fuzzy c-means
RFM analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-51156-2_143

Cite this

Oztaysi, B., & Kavi, M. (2021). Fuzzy RFM Analysis: An Application in E-Commerce. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference (pp. 1225-1232). (Advances in Intelligent Systems and Computing; Vol. 1197 AISC). Springer. https://doi.org/10.1007/978-3-030-51156-2_143

Oztaysi, Basar ; Kavi, Mert. / Fuzzy RFM Analysis : An Application in E-Commerce. Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A. Cagri Tolga. Springer, 2021. pp. 1225-1232 (Advances in Intelligent Systems and Computing).

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title = "Fuzzy RFM Analysis: An Application in E-Commerce",

abstract = "RFM (recency, frequency, monetary) analysis is an essential tool for customer segmentation, which is very important for marketing, communication, and even operations management activities. RFM is a widely adopted segmentation tool since it can be accomplished by using purchase transactions. In this study, we use the purchase transactions of Modanisa.com, which is a global e-commerce website from Turkey, to build a fuzzy RFM module. In the first step, the transaction data is converted to R, F, M data parameters, and then the fuzzy c-means algorithm is used to build the RFM clusters.",

keywords = "E-commerce, Fuzzy c-means, RFM analysis",

author = "Basar Oztaysi and Mert Kavi",

note = "Publisher Copyright: {\textcopyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2020 ; Conference date: 21-07-2020 Through 23-07-2020",

year = "2021",

doi = "10.1007/978-3-030-51156-2_143",

language = "English",

isbn = "9783030511555",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "1225--1232",

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

booktitle = "Intelligent and Fuzzy Techniques",

address = "Germany",

}

Oztaysi, B & Kavi, M 2021, Fuzzy RFM Analysis: An Application in E-Commerce. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference. Advances in Intelligent Systems and Computing, vol. 1197 AISC, Springer, pp. 1225-1232, International Conference on Intelligent and Fuzzy Systems, INFUS 2020, Istanbul, Turkey, 21/07/20. https://doi.org/10.1007/978-3-030-51156-2_143

Fuzzy RFM Analysis: An Application in E-Commerce. / Oztaysi, Basar; Kavi, Mert.
Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A. Cagri Tolga. Springer, 2021. p. 1225-1232 (Advances in Intelligent Systems and Computing; Vol. 1197 AISC).

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

TY - GEN

T1 - Fuzzy RFM Analysis

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2020

AU - Oztaysi, Basar

AU - Kavi, Mert

PY - 2021

Y1 - 2021

N2 - RFM (recency, frequency, monetary) analysis is an essential tool for customer segmentation, which is very important for marketing, communication, and even operations management activities. RFM is a widely adopted segmentation tool since it can be accomplished by using purchase transactions. In this study, we use the purchase transactions of Modanisa.com, which is a global e-commerce website from Turkey, to build a fuzzy RFM module. In the first step, the transaction data is converted to R, F, M data parameters, and then the fuzzy c-means algorithm is used to build the RFM clusters.

AB - RFM (recency, frequency, monetary) analysis is an essential tool for customer segmentation, which is very important for marketing, communication, and even operations management activities. RFM is a widely adopted segmentation tool since it can be accomplished by using purchase transactions. In this study, we use the purchase transactions of Modanisa.com, which is a global e-commerce website from Turkey, to build a fuzzy RFM module. In the first step, the transaction data is converted to R, F, M data parameters, and then the fuzzy c-means algorithm is used to build the RFM clusters.

KW - E-commerce

KW - Fuzzy c-means

KW - RFM analysis

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U2 - 10.1007/978-3-030-51156-2_143

DO - 10.1007/978-3-030-51156-2_143

M3 - Conference contribution

AN - SCOPUS:85088747048

SN - 9783030511555

T3 - Advances in Intelligent Systems and Computing

SP - 1225

EP - 1232

BT - Intelligent and Fuzzy Techniques

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A. Cagri

PB - Springer

Y2 - 21 July 2020 through 23 July 2020

ER -

Fuzzy RFM Analysis: An Application in E-Commerce

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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