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Journals / Finans Ekonomi ve Sosyal Araştırmalar Dergisi / 2026 / Cilt: 11 Sayı: 2

SEGMENTATİON OF CREDİT CARD CUSTOMERS USİNG THE KMEANS ALGORİTHM: A DATA MİNİNG APPLİCATİON

KREDI KARTI MÜŞTERILERININ KMEANS ALGORITMASI ILE SEGMENTASYONU: BIR VERI MADENCILIĞI UYGULAMASI

Uğur Berdibek(BİNGÖL ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ)0000-0003-3202-8342
Harun Reşit Gürçay(MARMARA ÜNİVERSİTESİ, BANKACILIK VE SİGORTACILIK ENSTİTÜSÜ, BANKACILIK ANABİLİM DALI, BANKACILIK (DR))0000-0001-8291-0785
Pages
—
Publication Date
2026-07-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings reveal that the customer base is not homogeneous but consists of subgroups that differ significantly in terms of risk and profitability.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In today's banking sector, customer-oriented strategies have become a fundamental element for achieving sustainable competitive advantage.
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Key Findings & Data:The dataset used in the research was obtained from the Kaggle repository and contains 6-month financial transactions of 8950 active credit card users.
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Conclusion & Impact:The findings reveal that the customer base is not homogeneous but consists of subgroups that differ significantly in terms of risk and profitability.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Müşteri segmentasyonu · K-Means kümeleme · Kredi kartı

Abstract

In today's banking sector, customer-oriented strategies have become a fundamental element for achieving sustainable competitive advantage. This study aims to segment credit card customers based on their financial behaviors. The dataset used in the research was obtained from the Kaggle repository and contains 6-month financial transactions of 8950 active credit card users. The CRISP-DM methodology was adopted in the data mining process, and analyses were conducted using Orange Data Mining software. Missing values were imputed with mean values, and variables were standardized using Z-score normalization. By applying the K-Means clustering algorithm, customers were divided into 4 distinct segments: Passive Customers, Risky Borrowers, VIP/Loyal Customers, and Low-Profile Customers. The clustering quality was evaluated using Silhouette analysis, yielding a reasonable cluster structure. The findings reveal that the customer base is not homogeneous but consists of subgroups that differ significantly in terms of risk and profitability. This study contributes to the development of data-driven personalized marketing strategies for banks.

Özet

Günümüz bankacılık sektöründe müşteri odaklı stratejiler, sürdürülebilir rekabet avantajı elde etmenin temel unsuru haline gelmiştir. Bu çalışmada, kredi kartı müşterilerinin finansal davranışlarına göre segmentasyonu gerçekleştirilmiştir. Araştırmada Kaggle veri havuzundan elde edilen ve 8950 aktif kredi kartı kullanıcısının 6 aylık finansal hareketlerini içeren veri seti kullanılmıştır. Veri madenciliği sürecinde CRISP-DM metodolojisi temel alınmış, analizler Orange Data Mining yazılımı ile gerçekleştirilmiştir. Eksik veriler ortalama ile doldurulmuş ve değişkenler Z-skor normalizasyonu ile standartlaştırılmıştır. K-Means kümeleme algoritması uygulanarak müşteriler 4 farklı segmente ayrılmıştır: Pasif Müşteriler, Riskli Borçlular, VIP/Sadık Müşteriler ve Düşük Profilli Müşteriler. Kümeleme kalitesi Silhouette analizi ile değerlendirilmiş ve makul düzeyde bir küme yapısı elde edilmiştir. Bulgular, müşteri tabanının homojen olmadığını ve risk ile karlılık açısından birbirinden farklı alt gruplardan oluştuğunu ortaya koymuştur. Çalışma, bankaların veriye dayalı kişiselleştirilmiş pazarlama stratejileri geliştirmelerine katkı sağlamaktadır.

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Cite This Article

Berdibek, U., Gürçay, H. R. (2026). SEGMENTATİON OF CREDİT CARD CUSTOMERS USİNG THE KMEANS ALGORİTHM: A DATA MİNİNG APPLİCATİON. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, Cilt: 11 Sayı: 2. https://doi.org/10.29106/fesa.1879511

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