CUSTOMER SEGMENTATION WITH CLUSTERING METHODS IN THE RETAIL INDUSTRY

Hayriye Şentürk; Ebru Geçici; Selçuk Alp

Makale detayları

CUSTOMER SEGMENTATION WITH CLUSTERING METHODS IN THE RETAIL INDUSTRY

Yazarlar:

Hayriye Şentürk | Ebru Geçici | Selçuk Alp

Yayıncı:

Istanbul Aydin University

Ana Başlıklar:

Müşteri İlişkileri Yönetimi

Dergi:

İstanbul Aydın Üniversitesi Sosyal Bilimler Dergisi Vol.16 Issue 4, pp.551-573

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001330069

pISSN:

2757-7252

Anahtar kelimeler:

Özet:

 The marketing world moves away from product-oriented work, understood customer importance, and shifts towards customer-centered practices. Today with tech development and increasing competition, company-customer relations become more important. Creating a customer profile is critical for businesses to recognize their customers and distinguish their most profitable customers. By understanding their customer behavior, companies can tailor their marketing and customer relationship management strategies to suit them and fulfill their customer needs, increasing their satisfaction and loyalty to their business, and encouraging them to shop from them again. Thus, this study aims to categorize customers based on RFM metrics and interpret the obtained clusters from a marketing perspective. At the segmentation phase, hierarchical and non-hierarchical clustering methods, namely k-means, AGNES, and DBSCAN, are used and the results are compared. First, data, which consist of the shopping information of 38975 customers who shopped from e-commerce in one year, are collected from a textile retail company in Istanbul. Then, the purchase amount spent by customers is additionally scored to reveal the most valuable customers. It is observed that better results are mined from the k-means algorithms. As a result, four different customer types are determined: loyal customer, potential customer, new customer, and lost customer types. In conclusion, profile-oriented marketing strategies are presented.

 The marketing world moves away from product-oriented work, understood customer importance, and shifts towards customer-centered practices. Today with tech development and increasing competition, company-customer relations become more important. Creating a customer profile is critical for businesses to recognize their customers and distinguish their most profitable customers. By understanding their customer behavior, companies can tailor their marketing and customer relationship management strategies to suit them and fulfill their customer needs, increasing their satisfaction and loyalty to their business, and encouraging them to shop from them again. Thus, this study aims to categorize customers based on RFM metrics and interpret the obtained clusters from a marketing perspective. At the segmentation phase, hierarchical and non-hierarchical clustering methods, namely k-means, AGNES, and DBSCAN, are used and the results are compared. First, data, which consist of the shopping information of 38975 customers who shopped from e-commerce in one year, are collected from a textile retail company in Istanbul. Then, the purchase amount spent by customers is additionally scored to reveal the most valuable customers. It is observed that better results are mined from the k-means algorithms. As a result, four different customer types are determined: loyal customer, potential customer, new customer, and lost customer types. In conclusion, profile-oriented marketing strategies are presented.

 The marketing world moves away from product-oriented work, understood customer importance, and shifts towards customer-centered practices. Today with tech development and increasing competition, company-customer relations become more important. Creating a customer profile is critical for businesses to recognize their customers and distinguish their most profitable customers. By understanding their customer behavior, companies can tailor their marketing and customer relationship management strategies to suit them and fulfill their customer needs, increasing their satisfaction and loyalty to their business, and encouraging them to shop from them again. Thus, this study aims to categorize customers based on RFM metrics and interpret the obtained clusters from a marketing perspective. At the segmentation phase, hierarchical and non-hierarchical clustering methods, namely k-means, AGNES, and DBSCAN, are used and the results are compared. First, data, which consist of the shopping information of 38975 customers who shopped from e-commerce in one year, are collected from a textile retail company in Istanbul. Then, the purchase amount spent by customers is additionally scored to reveal the most valuable customers. It is observed that better results are mined from the k-means algorithms. As a result, four different customer types are determined: loyal customer, potential customer, new customer, and lost customer types. In conclusion, profile-oriented marketing strategies are presented.

 Pazarlama dünyası ürün odaklı çalışmalardan uzaklaşıyor, müşterinin önemini anlıyor ve müşteri odaklı uygulamalara doğru kayıyor. Günümüzde teknolojinin gelişmesi ve rekabetin artmasıyla birlikte şirket-müşteri ilişkileri daha da önem kazanmaktadır. İşletmelerin müşterilerini tanıması ve en kârlı müşterilerini ayırt edebilmesi için müşteri profili oluşturmak kritik öneme sahiptir. Şirketler, müşteri davranışlarını anlayarak, pazarlama ve müşteri ilişkileri yönetimi stratejilerini kendilerine uyacak ve müşteri ihtiyaçlarını karşılayacak şekilde uyarlayabilir, işlerine olan memnuniyetlerini ve bağlılıklarını artırabilir ve onları onlardan tekrar alışveriş yapmaya teşvik edebilir. Böylece bu çalışma, RFM metriklerine göre müşterileri kategorize etmeyi ve elde edilen kümeleri pazarlama perspektifinden yorumlamayı amaçlamaktadır. Segmentasyon aşamasında k-means, AGNES ve DBSCAN gibi hiyerarşik ve hiyerarşik olmayan kümeleme yöntemleri kullanılarak sonuçlar karşılaştırılmıştır. Öncelikle İstanbuldaki bir tekstil perakende firmasından bir yıl içinde e-ticaretten alışveriş yapan 38975 müşterinin alışveriş bilgilerinden oluşan veriler toplanmıştır. Daha sonra müşterilerin harcadığı satın alma tutarı ek olarak puanlanarak en değerli müşteriler ortaya çıkarılır. K-means algoritmalarından daha iyi sonuçlar elde edildiği görülmektedir. Sonuç olarak dört farklı müşteri tipi belirlendi: sadık müşteri, potansiyel müşteri, yeni müşteri ve kayıp müşteri tipleri. Sonuç olarak profil odaklı pazarlama stratejileri sunulmaktadır.

Makale detayları

CUSTOMER SEGMENTATION WITH CLUSTERING METHODS IN THE RETAIL INDUSTRY

Yazarlar:

Hayriye Şentürk | Ebru Geçici | Selçuk Alp

Yayıncı:

Istanbul Aydin University

Ana Başlıklar:

Müşteri İlişkileri Yönetimi

Dergi:

İstanbul Aydın Üniversitesi Sosyal Bilimler Dergisi Vol.16 Issue 4, pp.551-573

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001330069

pISSN:

2757-7252

Customer Segmentation

RFM Analysis

ClusteringMüşteri Segmentasyonu

RFM Analizi

Kümeleme

Sayı

Makale detayları

CUSTOMER SEGMENTATION WITH CLUSTERING METHODS IN THE RETAIL INDUSTRY

Yazarlar: Hayriye Şentürk | Ebru Geçici | Selçuk Alp

Yayıncı: Istanbul Aydin University

Ana Başlıklar: Müşteri İlişkileri Yönetimi

Dergi: İstanbul Aydın Üniversitesi Sosyal Bilimler Dergisi Vol.16 Issue 4, pp.551-573

Belge Tipi: Makale

Yayın Tarihi: 10/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001330069

pISSN: 2757-7252

Customer Segmentation

RFM Analysis

ClusteringMüşteri Segmentasyonu

RFM Analizi

Kümeleme

Sayı

Yazarlar:

Hayriye Şentürk | Ebru Geçici | Selçuk Alp

Yayıncı:

Istanbul Aydin University

Ana Başlıklar:

Müşteri İlişkileri Yönetimi

Dergi:

İstanbul Aydın Üniversitesi Sosyal Bilimler Dergisi Vol.16 Issue 4, pp.551-573

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001330069

pISSN:

2757-7252