yyu jinas Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 1300-5413 2667-467X Van Yuzuncu Yıl University Algorithms and Calculation Theory Numerical Computation and Mathematical Software Data Structures and Algorithms Algoritmalar ve Hesaplama Kuramı Sayısal Hesaplama ve Matematiksel Yazılım Veri Yapıları ve Algoritmalar Customer Behavior Segmentation Using Fuzzy Clustering and Classification Techniques Bulanık Kümeleme ve Sınıflandırma Teknikleri ile Müşteri Davranış Segmentasyonu https://orcid.org/0000-0002-1731-9559 Kutlu Fatih VAN YÜZÜNCÜ YIL ÜNİVERSİTESİ, FEN FAKÜLTESİ, MATEMATİK BÖLÜMÜ https://orcid.org/0000-0003-2015-3576 Göleli Kübra VAN YÜZÜNCÜ YIL ÜNİVERSİTESİ FEN FAKÜLTESİ https://orcid.org/0009-0008-5691-9143 Demir Hanım VAN YÜZÜNCÜ YIL ÜNİVERSİTESİ FEN FAKÜLTESİ https://orcid.org/0009-0000-6664-4990 Erdiz Gülcan VAN YÜZÜNCÜ YIL ÜNİVERSİTESİ FEN FAKÜLTESİ 12 24 2025 30 3 990 1008 07 05 2025 11 11 2025 Copyright © 1995, Yuzuncu Yil University Journal of the Institute of Natural and Applied Sciences 1995 Yuzuncu Yil University Journal of the Institute of Natural and Applied Sciences

This study focuses on the classification of customer response behavior toward marketing campaign offers. The analysis utilizes the Customer Personality Analysis (CPA) dataset, which is publicly available on the Kaggle online data-sharing platform. The target variable, Response, indicates whether the customer accepted the most recent campaign offer (1 = accepted, 0 = not accepted). Since the proportion of positive responses is approximately 15% of all observations, the dataset exhibits a pronounced class imbalance. For this reason, performance evaluation prioritizes the macro-F1 metric rather than overall accuracy, as macro-F1 provides a more balanced representation of the minority class. The methodological framework involves the application of the Fuzzy C-Means (FCM) clustering algorithm to obtain membership degrees for each instance. These membership values are subsequently integrated into two classification models. In the FCM+FSVM model, the membership degrees are utilized as instance weights influencing the decision boundary. In the FCM+FKNN model, the same membership degrees are incorporated as adaptive weighting factors in the neighborhood-based voting mechanism. FCM hyperparameters are optimized using a genetic algorithm, while classifier hyperparameters are determined through random search. Comparative experiments including logistic regression, KNN, RBF-SVM, random forest, and gradient boosting demonstrate that the FCM+FSVM model achieves the highest performance in both overall classification accuracy and minority class recognition.

Bu çalışmada, pazarlama kampanyalarına yönelik müşteri yanıt davranışlarının sınıflandırılması ele alınmıştır. Analizlerde, Kaggle çevrimiçi veri paylaşım platformunda açık erişimli olarak sunulan Customer Personality Analysis (CPA) veri seti kullanılmıştır. Veri setindeki hedef değişken Response, müşterinin son kampanya teklifini kabul etme durumunu ifade etmektedir (1 = kabul etti, 0 = kabul etmedi). Pozitif sınıfın toplam gözlem sayısına oranının yaklaşık %15 düzeyinde olması, belirgin bir sınıf dengesizliği problemine işaret etmektedir. Bu nedenle, model başarımının değerlendirilmesinde yalnızca doğruluk ölçütü yerine, azınlık sınıfını daha dengeli biçimde temsil eden makro-F1 metriği dikkate alınmıştır. Yöntemsel çerçevede ilk olarak bulanık c-Ortalamalar (FCM) algoritması uygulanarak her örnek için kümelere ilişkin üyelik dereceleri elde edilmiştir. Daha sonra bu üyelik dereceleri, FCM+FSVM yapısında örnek ağırlığı olarak sınıflandırma sürecine dahil edilmiş; FCM+FKNN yapısında ise komşuluk katkı katsayısı olarak kullanılmıştır. FCM hiperparametreleri genetik algoritma ile optimize edilirken, sınıflandırıcılara ilişkin hiperparametreler rastgele arama yöntemiyle belirlenmiştir. Deneysel çalışmalarda lojistik regresyon, KNN, RBF-SVM, rastgele orman ve gradyan artırma gibi yöntemlerle karşılaştırma yapılmış ve FCM+FSVM modelinin hem genel sınıflandırma başarımı hem de azınlık sınıfını tanıma yeteneği açısından en yüksek performansı sergilediği görülmüştür.

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