Segmentation of Online Customers Based on Household Panel Data Using Unsupervised Learning

Serhan Berke Erden; Mert Erişen; Utku Doğruak; Tolga Kaya

doi:10.1007/978-3-031-67195-1_23

Segmentation of Online Customers Based on Household Panel Data Using Unsupervised Learning

Serhan Berke Erden^*, Mert Erişen, Utku Doğruak, Tolga Kaya

^*Corresponding author for this work

Department of Management Engineering

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

Abstract

Marketing departments of companies often struggle with cost-related issues arising from insufficient and inaccurate definition of their target customers. They desire to focus on customers who are highly profitable and loyal. The aim of this study is to enable companies with e-commerce activities to precisely define their target customer segments and understand their characteristics better. For this purpose, the e-commerce transaction dataset provided by a household panel company operating in Türkiye is used. In this project, where a real-life case is analysed, unsupervised machine learning algorithm, K-Means, is used. By doing cluster analysis, the eventual aim is to reach the right number of clusters having similar customer behaviours. The result demonstrates successful modelling, achieving distinct segments consisting of homogenous personas. Consequently, enterprises with e-commerce activities will be able to identify different customer types more effectively, which paves the way for the development of customised marketing strategies, increasing loyalty and profitability.

Original language	English
Title of host publication	Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A. Cagri Tolga
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	177-184
Number of pages	8
ISBN (Print)	9783031671944
DOIs	https://doi.org/10.1007/978-3-031-67195-1_23
Publication status	Published - 2024
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2024 - Canakkale, Turkey Duration: 16 Jul 2024 → 18 Jul 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1089 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2024
Country/Territory	Turkey
City	Canakkale
Period	16/07/24 → 18/07/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Clustering
Customer Segmentation
Customised Marketing Strategies
K-Means
Loyalty
Machine Learning
Unsupervised Learning

Access to Document

10.1007/978-3-031-67195-1_23

Cite this

Erden, S. B., Erişen, M., Doğruak, U., & Kaya, T. (2024). Segmentation of Online Customers Based on Household Panel Data Using Unsupervised Learning. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. Ucal Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference (pp. 177-184). (Lecture Notes in Networks and Systems; Vol. 1089 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-67195-1_23

Erden, Serhan Berke ; Erişen, Mert ; Doğruak, Utku et al. / Segmentation of Online Customers Based on Household Panel Data Using Unsupervised Learning. Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A. Cagri Tolga. Springer Science and Business Media Deutschland GmbH, 2024. pp. 177-184 (Lecture Notes in Networks and Systems).

@inproceedings{427c76f946a54a1e9ca68cd91e9e569f,

title = "Segmentation of Online Customers Based on Household Panel Data Using Unsupervised Learning",

abstract = "Marketing departments of companies often struggle with cost-related issues arising from insufficient and inaccurate definition of their target customers. They desire to focus on customers who are highly profitable and loyal. The aim of this study is to enable companies with e-commerce activities to precisely define their target customer segments and understand their characteristics better. For this purpose, the e-commerce transaction dataset provided by a household panel company operating in T{\"u}rkiye is used. In this project, where a real-life case is analysed, unsupervised machine learning algorithm, K-Means, is used. By doing cluster analysis, the eventual aim is to reach the right number of clusters having similar customer behaviours. The result demonstrates successful modelling, achieving distinct segments consisting of homogenous personas. Consequently, enterprises with e-commerce activities will be able to identify different customer types more effectively, which paves the way for the development of customised marketing strategies, increasing loyalty and profitability.",

keywords = "Clustering, Customer Segmentation, Customised Marketing Strategies, K-Means, Loyalty, Machine Learning, Unsupervised Learning",

author = "Erden, {Serhan Berke} and Mert Eri{\c s}en and Utku Doğruak and Tolga Kaya",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; International Conference on Intelligent and Fuzzy Systems, INFUS 2024 ; Conference date: 16-07-2024 Through 18-07-2024",

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doi = "10.1007/978-3-031-67195-1_23",

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series = "Lecture Notes in Networks and Systems",

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

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

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Erden, SB, Erişen, M, Doğruak, U & Kaya, T 2024, Segmentation of Online Customers Based on Household Panel Data Using Unsupervised Learning. in C Kahraman, S Cevik Onar, B Oztaysi, I Ucal Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. Lecture Notes in Networks and Systems, vol. 1089 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 177-184, International Conference on Intelligent and Fuzzy Systems, INFUS 2024, Canakkale, Turkey, 16/07/24. https://doi.org/10.1007/978-3-031-67195-1_23

Segmentation of Online Customers Based on Household Panel Data Using Unsupervised Learning. / Erden, Serhan Berke; Erişen, Mert; Doğruak, Utku et al.
Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A. Cagri Tolga. Springer Science and Business Media Deutschland GmbH, 2024. p. 177-184 (Lecture Notes in Networks and Systems; Vol. 1089 LNNS).

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

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AU - Erden, Serhan Berke

AU - Erişen, Mert

AU - Doğruak, Utku

AU - Kaya, Tolga

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Marketing departments of companies often struggle with cost-related issues arising from insufficient and inaccurate definition of their target customers. They desire to focus on customers who are highly profitable and loyal. The aim of this study is to enable companies with e-commerce activities to precisely define their target customer segments and understand their characteristics better. For this purpose, the e-commerce transaction dataset provided by a household panel company operating in Türkiye is used. In this project, where a real-life case is analysed, unsupervised machine learning algorithm, K-Means, is used. By doing cluster analysis, the eventual aim is to reach the right number of clusters having similar customer behaviours. The result demonstrates successful modelling, achieving distinct segments consisting of homogenous personas. Consequently, enterprises with e-commerce activities will be able to identify different customer types more effectively, which paves the way for the development of customised marketing strategies, increasing loyalty and profitability.

AB - Marketing departments of companies often struggle with cost-related issues arising from insufficient and inaccurate definition of their target customers. They desire to focus on customers who are highly profitable and loyal. The aim of this study is to enable companies with e-commerce activities to precisely define their target customer segments and understand their characteristics better. For this purpose, the e-commerce transaction dataset provided by a household panel company operating in Türkiye is used. In this project, where a real-life case is analysed, unsupervised machine learning algorithm, K-Means, is used. By doing cluster analysis, the eventual aim is to reach the right number of clusters having similar customer behaviours. The result demonstrates successful modelling, achieving distinct segments consisting of homogenous personas. Consequently, enterprises with e-commerce activities will be able to identify different customer types more effectively, which paves the way for the development of customised marketing strategies, increasing loyalty and profitability.

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KW - Customised Marketing Strategies

KW - K-Means

KW - Loyalty

KW - Machine Learning

KW - Unsupervised Learning

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AN - SCOPUS:85206975977

SN - 9783031671944

T3 - Lecture Notes in Networks and Systems

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BT - Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference

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A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Ucal Sari, Irem

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A2 - Tolga, A. Cagri

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2024

Y2 - 16 July 2024 through 18 July 2024

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Erden SB, Erişen M, Doğruak U, Kaya T. Segmentation of Online Customers Based on Household Panel Data Using Unsupervised Learning. In Kahraman C, Cevik Onar S, Oztaysi B, Ucal Sari I, Cebi S, Tolga AC, editors, Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. Springer Science and Business Media Deutschland GmbH. 2024. p. 177-184. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-67195-1_23

Segmentation of Online Customers Based on Household Panel Data Using Unsupervised Learning

Abstract

Publication series

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Bibliographical note

Keywords

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