Gelişmiş Veri Madenciliği Teknikleri Yoluyla Müşteri Segmentasyonuna Derin Bir Bakış

Öz

Bu araştırma, bir market firmasının müşteri veritabanındaki müşteri profillerini veri madenciliği tekniklerini kullanarak detaylı bir şekilde ortaya çıkarmak için gerçekleştirdiği müşteri segmentasyon sürecini incelemektedir. Müşteri segmentasyonu, pazarlama stratejilerinin etkin bir şekilde uyarlanmasında kritik öneme sahiptir. Bu süreç, kişiselleştirilmiş müşteri profillerinin oluşturulmasına olanak tanıyarak daha verimli ve hedefe yönelik pazarlama çalışmalarına olanak sağlamaktadır. Çalışmada kullanılan veri seti, tanınmış bir market firmasının veri tabanından elde edilmiştir ve 29 farklı özelliğe sahip 2.240 veri noktası içermektedir. Bu özellikler müşteri demografisi, ürün bilgileri, satın alma kanalları ve promosyon yanıt verileri olmak üzere dört kategoride toplanmıştır. Çalışma, K-Means Kümeleme ve Aglomeratif Kümeleme gibi ileri kümeleme tekniklerini kullanarak müşteriler arasındaki anlamlı kalıpları ve gruplamaları keşfetmeyi amaçlamaktadır. Ayrıca, araştırmanın bir diğer amacı, dinamik pazar karmaşıklıklarına uyum sağlamanın ve tüketici davranışlarını değiştirmenin kritik bir yönü olan müşteri segmentasyonu için veri madenciliği ve makine öğrenmesi yöntemlerinin nasıl etkili bir şekilde kullanılabileceğini ortaya koymaktır. Araştırma kapsamında dört müşteri kümesi ortaya çıkmıştır. Bu kümeler, demografik bilgiler, satın alma davranışı ve promosyon faaliyetlerine verilen yanıtlar gibi çeşitli özelliklere dayalı olarak müşteriler arasındaki anlamlı gruplaşmaları ve kalıpları temsil etmektedir. Bulgular, müşteri profillerinin karmaşıklığını anlamak ve pazarlama stratejilerini buna göre ayarlamak için değerli bir çerçeve sunmaktadır.

Anahtar Kelimeler

• Müşteri Segmentasyonu
• Veri Madenciliği
• Tüketici Davranışı
• Pazarlama Stratejileri
• Kümeleme Yöntemleri

A Deep Dive Into Customer Segmentation Through Advanced Data Mining Techniques

Öz

This study examines how data mining techniques are used to segment customers to reveal complex customer profiles in a grocery store's database. Customer segmentation is crucial to effectively tailor marketing strategies. This procedure makes it easier to create customized customer profiles, making it possible to create more targeted and effective marketing campaigns. The dataset used in the study was obtained from the database of a well-known grocery company and contains 2.240 data points with 29 different features. These features are grouped into four categories: customer demographics, product information, purchase channels and promotional response data. The study attempts to identify meaningful patterns and groupings among customers using advanced clustering techniques such as K-Means Clustering and Agglomerative Clustering. Another goal of the research is to demonstrate how data mining and machine learning techniques can be effectively applied to customer segmentation, a critical component of adapting to the ever- changing complexity of the market and changes in customer behavior. Within the scope of the research, four customer clusters emerged. Clusters represent meaningful subsets and trends among customers, encompassing a range of features such as demographics, purchasing patterns, and responses to marketing campaigns. The findings provide a useful framework for understanding the complexity of customer profiles and adapting marketing strategies accordingly.

Anahtar Kelimeler

• Customer Segmentation
• Data Mining
• Consumer Behavior
• Marketing Strategies
• Clustering Methods

Kaynakça

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