Customer Segmentation Using RFM Model and Clustering Methods in Online Retail Industry

Sezgi Acar*, Fatma Köroğlu, Batuhan Duyuler, Tolga Kaya, Tuncay Özcan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In our era, the issue of analyzing and predicting customer behavior and thoroughly aligning their business strategies and marketing activities for companies increases its inevitability everyday much more than before. In this context, segmenting customers has become the most necessary action for the firms all around the world. This study aims to make customer segmentation using the invoice data of an eCommerce company in Turkey. Accordingly, customer segmentation is carried out by the application of the RFM (Recency, Frequency and Monetary) model which is one of the most significant models used in customer segmentation to identify valuable customers. More on that, clustering methods are applied on the data retrieved from the RFM model and characteristics of each customer group created are analyzed. For this purpose, the most widely used K-Means and Fuzzy C-Means algorithms in the literature were selected. Followingly, by Silhouette and Dunn Indexes, the best performing algorithm and optimum number of clusters of this eCommerce company located in Turkey are provided as an insight for strategies at the end of the study.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
EditorsCengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
PublisherSpringer Science and Business Media Deutschland GmbH
Pages69-77
Number of pages9
ISBN (Print)9783030856250
DOIs
Publication statusPublished - 2022
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey
Duration: 24 Aug 202126 Aug 2021

Publication series

NameLecture Notes in Networks and Systems
Volume307
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021
Country/TerritoryTurkey
CityIstanbul
Period24/08/2126/08/21

Bibliographical note

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

Keywords

  • Clustering
  • Customer segmentation
  • Fuzzy C-Means
  • K-Means
  • RFM model
  • eCommerce

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