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AN EMPIRICAL RESEARCH ON COMPARISON OF CUSTOMER SEGMENTATION MODELS

Year 2024, Volume: 19 Issue: 62, 130 - 145, 30.07.2024
https://doi.org/10.14783/maruoneri.1464977

Abstract

Identifying customers and determining their real needs and expectations is complicated for companies. In order to facilitate the process, customer segmentation is used to divide customers who share similar characteristics into smaller groups. Many models have been developed for customer segmentation. This study compares the traditional RFM (Recency, Frequency, Monetary) model with the extended LRFM (Lenght, Recency, Frequency, Monetary) and RFMV (Recency, Frequency, Monetary, Variety) customer segmentation models using 228 customer data sets of a company operating in the biotechnology sector. We examine how these models, extensively applied in the retail sector, can be used for B2B firms operating in the biotechnology sector. The results aim to determine which marketing strategies can be more effective by applying these methods and the most appropriate method according to the sector’s objectives.

References

  • Alvandi M., Fazli S. ve Seifi Abdoli F. (2012) K-Mean Clustering Method For Analysis Customer Lifetime Value With LRFM Relationship Model In Banking Services, International Research Journal of Applied and Basic Sciences, Sy2294-2302,C3
  • Başkol M. (2020) RFM ve Uyum Analizi Kullanılarak Müşteri Segmentasyonunun Belirlenmesi, Business & Management Studıes: An Internatıonal Journal, 902.
  • Belhadj T. (2021) Customer Value Analysis Using Weighted RFM model: Empirical Case Study, Al Bashaer Economic Journal, 932-948.
  • Chen D., Laing S. ve Guo K. (2012), Journal of Database Marketing & Customer Strategy Management, 197-208.
  • Gustriansyah R., Suhandi N., Antony F. (2019) Clustering Optimization İn RFM Analysis Based On K-Means, Indonesian Journal of Electrical Engineering and Computer Science, 470-477.
  • Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques. Waltham: Morgan Kaufmann.
  • Li M., Wang O., ve ShenY. (2021) Adherence predictor variables in AIDS patients: an empirical study using the data mining‑based RFM model, AIDS Research and Therapy, 1-8.
  • Jamunadevi C., Tamil Selvan S., Govindarajan M., Saravanan C. ve Janaki B. (2021) LRFM Model for Customer Purchase Behaviour Using Kmeans Algorithm, IOP Conf. Series: Materials Science and Engineering, 1-8.
  • Chang vd. (2013) A case study of applying LRFM model and clustering techniques to evaluate customer values, Sage Journals, 1-10.
  • Moghaddam, Abdolvand, & Harandi (2017) A RFMV Model and Customer Segmentation Based on Variety of Products, 155-161.
  • Nimbalkar, D. D., & Shah, P. (2013). Data mining using RFM Analysis. International Journal of Scientific & Engineering Research, 940-943.
  • Olson D., Cao Q., Gu C., Lee D. (2009), Comparison of customer response models, Orijinal Paper, 117-130.
  • Pakyürek M., Kestepe S., ve Yıldız T. (2018, May) Müşterilerin GSP Analizi Kullanarak Kümelenmesi, Konferans; Signal Processing and Communication Applications Conference At: İzmir, Turkey.
  • Qadaki M., Abdolvand N. ve Harandi S. (2017, June) A RFMV Model and Customer Segmentation Based on Variety of Products, Journal of Information Systems and Telecommunication, 156-161.
  • Sohrabi B., Khanlari A. (2007), Customer Lifetime Value (CLV) Measurement Based on RFM Model, Iranian Accounting & Auditing Review, 7- 20.
  • Şentürk H., Alp S. (2023). Perakende Sektöründe RFM Analizi ile Müşteri Segmentasyonu, İstanbul Ticaret Üniversitesi Girişimcilik Dergisi, 1-10.
  • Shih Y. ve Liu C. (2003) A method for customer lifetime value ranking - Combining the analytic hierarchy process and clustering analysis, Database Marketing & Customer Strategy Management, 159-172.
  • Ozkan P.ve Deveci Kocakoc İ. (2021, May) A Customer Segmentation Model Proposal for Retailers: RFM-V, Advances in Global Services and Retail Management, 1-12.
  • Tuncer İ. ve Karaboğa K. (2021), RFM Metriklerini Kullanarak Kümeleme Yöntemi ile Müşteri Bölümlendirme: Perakende Sektöründe Bir Uygulama, Üçüncü Sektör Sosyal Ekonomi Dergisi, 411-425.
  • Tyasnurita R. ve Kafif Ibrahim M. (2022, October) LRFM Model Analysis for Customer Segmentation Using K-Means Clustering, International Conference on Electrical and Information Technology

MÜŞTERİ SEGMENTASYON MODELLERİNİN KARŞILAŞTIRILMASI ÜZERİNE AMPİRİK BİR ARAŞTIRMA

Year 2024, Volume: 19 Issue: 62, 130 - 145, 30.07.2024
https://doi.org/10.14783/maruoneri.1464977

Abstract

Şirketler için müşterilerin tanımlanması, onların gerçek ihtiyaçlarının ve beklentilerinin belirlenmesi oldukça zor bir süreçtir. Süreci kolaylaştırmak adına müşteri segmentasyonu yaparak benzer özellikleri paylaşan müşteriler daha küçük gruplara ayrılmaktadır. Müşteri segmentasyonu yapabilmek için birçok model geliştirilmiştir. Bu çalışmada, biyoteknoloji sektöründe faaliyet gösteren bir firmaya ait 228 müşteri veri seti kullanılarak geliştirilen modellerden, geleneksel RFM (Recency, Frequency, Monetary) modeli ile genişletilmiş LRFM (Lenght, Recency, Frequency, Monetary) ve RFMV (Recency, Frequency, Monetary, Variety) müşteri segmentasyon modelleri kıyaslanmaktadır. Yoğun olarak perakende sektöründe uygulanan bu modellerin, biyoteknoloji sektöründe faaliyet gösteren B2B firmaları için nasıl kullanılabileceği incelenmektedir. Ulaşılan sonuçlar bu yöntemlerin uygulanmasıyla hangi pazarlama stratejilerinin daha etkili olabileceğini ve sektörün hedeflerine göre en uygun yöntemin belirlenmesini amaçlamaktadır.

Thanks

Çalışmamda emeği geçen değerli hocalarım Prof. Dr. Hakan YILDIRIM'a, Prof. Dr. Murat ÇİNKO'ya ve Doç. Dr. Serkan ETİ'ye teşekkürü borç bilirim.

References

  • Alvandi M., Fazli S. ve Seifi Abdoli F. (2012) K-Mean Clustering Method For Analysis Customer Lifetime Value With LRFM Relationship Model In Banking Services, International Research Journal of Applied and Basic Sciences, Sy2294-2302,C3
  • Başkol M. (2020) RFM ve Uyum Analizi Kullanılarak Müşteri Segmentasyonunun Belirlenmesi, Business & Management Studıes: An Internatıonal Journal, 902.
  • Belhadj T. (2021) Customer Value Analysis Using Weighted RFM model: Empirical Case Study, Al Bashaer Economic Journal, 932-948.
  • Chen D., Laing S. ve Guo K. (2012), Journal of Database Marketing & Customer Strategy Management, 197-208.
  • Gustriansyah R., Suhandi N., Antony F. (2019) Clustering Optimization İn RFM Analysis Based On K-Means, Indonesian Journal of Electrical Engineering and Computer Science, 470-477.
  • Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques. Waltham: Morgan Kaufmann.
  • Li M., Wang O., ve ShenY. (2021) Adherence predictor variables in AIDS patients: an empirical study using the data mining‑based RFM model, AIDS Research and Therapy, 1-8.
  • Jamunadevi C., Tamil Selvan S., Govindarajan M., Saravanan C. ve Janaki B. (2021) LRFM Model for Customer Purchase Behaviour Using Kmeans Algorithm, IOP Conf. Series: Materials Science and Engineering, 1-8.
  • Chang vd. (2013) A case study of applying LRFM model and clustering techniques to evaluate customer values, Sage Journals, 1-10.
  • Moghaddam, Abdolvand, & Harandi (2017) A RFMV Model and Customer Segmentation Based on Variety of Products, 155-161.
  • Nimbalkar, D. D., & Shah, P. (2013). Data mining using RFM Analysis. International Journal of Scientific & Engineering Research, 940-943.
  • Olson D., Cao Q., Gu C., Lee D. (2009), Comparison of customer response models, Orijinal Paper, 117-130.
  • Pakyürek M., Kestepe S., ve Yıldız T. (2018, May) Müşterilerin GSP Analizi Kullanarak Kümelenmesi, Konferans; Signal Processing and Communication Applications Conference At: İzmir, Turkey.
  • Qadaki M., Abdolvand N. ve Harandi S. (2017, June) A RFMV Model and Customer Segmentation Based on Variety of Products, Journal of Information Systems and Telecommunication, 156-161.
  • Sohrabi B., Khanlari A. (2007), Customer Lifetime Value (CLV) Measurement Based on RFM Model, Iranian Accounting & Auditing Review, 7- 20.
  • Şentürk H., Alp S. (2023). Perakende Sektöründe RFM Analizi ile Müşteri Segmentasyonu, İstanbul Ticaret Üniversitesi Girişimcilik Dergisi, 1-10.
  • Shih Y. ve Liu C. (2003) A method for customer lifetime value ranking - Combining the analytic hierarchy process and clustering analysis, Database Marketing & Customer Strategy Management, 159-172.
  • Ozkan P.ve Deveci Kocakoc İ. (2021, May) A Customer Segmentation Model Proposal for Retailers: RFM-V, Advances in Global Services and Retail Management, 1-12.
  • Tuncer İ. ve Karaboğa K. (2021), RFM Metriklerini Kullanarak Kümeleme Yöntemi ile Müşteri Bölümlendirme: Perakende Sektöründe Bir Uygulama, Üçüncü Sektör Sosyal Ekonomi Dergisi, 411-425.
  • Tyasnurita R. ve Kafif Ibrahim M. (2022, October) LRFM Model Analysis for Customer Segmentation Using K-Means Clustering, International Conference on Electrical and Information Technology
There are 20 citations in total.

Details

Primary Language Turkish
Subjects Customer Relationship Management, Marketing Management
Journal Section Makale Başvuru
Authors

Ebru Sarıoğlu 0009-0008-0482-0635

Mehmet İnel 0000-0002-6966-3238

Early Pub Date July 29, 2024
Publication Date July 30, 2024
Submission Date April 4, 2024
Acceptance Date June 10, 2024
Published in Issue Year 2024 Volume: 19 Issue: 62

Cite

APA Sarıoğlu, E., & İnel, M. (2024). MÜŞTERİ SEGMENTASYON MODELLERİNİN KARŞILAŞTIRILMASI ÜZERİNE AMPİRİK BİR ARAŞTIRMA. Öneri Dergisi, 19(62), 130-145. https://doi.org/10.14783/maruoneri.1464977

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Öneri

Marmara UniversityInstitute of Social Sciences

Göztepe Kampüsü Enstitüler Binası Kat:5 34722  Kadıköy/İstanbul

e-ISSN: 2147-5377