Kişiselleştirilmiş Satış Stratejileri için Veri Madenciliği: Bir Kümeleme ve İlişkilendirme Analizi Yaklaşımı

Year 2026, Volume: 14 Issue: 2 , 567 - 576 , 19.04.2026

Selçuk Alp , Ebru Geçici , Umut Rıfat Tuzkaya , Ayhan Boyacıoğlu , Yunus Taştutan

https://doi.org/10.29130/dubited.1609964

https://izlik.org/JA42YB75AL

Abstract

Günümüz işletmeleri için satış personelinin performansını değerlendirmek ve satış stratejilerini optimize etmek zorunludur. Bu bağlamda, satışları artırmak için satıcıların ilgili satış özelliklerine göre birleştirilmesi de dâhil olmak üzere stratejiler geliştirmek için çeşitli yaklaşımlar kullanılmaktadır. Makine öğrenmesi yaklaşımı olan kümeleme, satış verilerinden çıkarımlar elde etmek için bir araç olarak kullanılmaktadır. Elde edilen sonuçlar ise daha sonra gelecekteki satış planlamasını ve önceliklerin belirlenmesi için kullanılmaktadır. Bunu başarmak için, satıcılar başlangıçta belirli kriterlere (satış hacmi, ürün bilgisi vb.) göre benzer özelliklere sahip olanlar bir arada olacak şekilde gruplara (kümelere) ayrılır. Bu, her kümedeki satıcıların ortak güçlü ve zayıf yönlerinin belirlenmesini sağlar. Örneğin, yüksek satış hacmi ve müşteri memnuniyeti puanlarına sahip bir kümedeki satıcılar yeni ürünlerin piyasaya sürülmesinde öncü bir rol üstlenebilirken, düşük performans gösteren bir kümedeki satıcıların bulunduğu bölgede hangi ürünlerin tercih edilebileceğini ve bu ürünlerin satışlarını artırmak için hangi önlemlerin alınabileceğini araştırmak faydalı olabilir. Kümelenmiş satıcıların satış performanslarını inceleyerek, farklı uygulamalar için en çok satan ürünler arasındaki ilişkileri belirlemek mümkündür. Bu yaklaşım, birlikte satılan ürünlerin, birbirlerinin satışlarını teşvik eden ürünlerin ve farklı müşteri segmentlerine hitap eden ürünlerin belirlenmesini sağlar. Kümeleme analizinin ardından, bir ilişki analizi, ürünler arasındaki karşılıklı ilişkilerin daha kapsamlı bir şekilde incelenmesini sağlar. Bu analizin sonuçları, belirli müşteri profilleri arasında ürün tercihlerinin belirlenmesinde kullanılabilir. Yukarıda belirtilen bilgiler dikkate alındığında, daha etkili ürün önerileri ve kişiselleştirilmiş pazarlama stratejileri formüle edilmiştir. Belirlenen kümeler içindeki satışların incelenmesi, ilgili bilgilerin ortaya çıkarılmasını sağlamıştır.

Keywords

Satış Stratejileri , Kümeleme , RFM Analizleri , Birliktelik Kuralı Analizleri

Data Mining for Personalized Sales Strategies: A Clustering and Association Analysis Approach

https://izlik.org/JA42YB75AL

Abstract

Contemporary businesses must evaluate the performance of their sales personnel and refine their sales strategies. In this context, a variety of approaches are employed to develop strategies, including combining sellers based on their respective sales characteristics, to increase sales. Clustering, a machine learning approach, is used to derive inferences from sales data. The results are then used to inform future sales planning and determine priorities. To achieve this, the sellers are initially grouped (clustered) by similar characteristics based on specific criteria (such as sales volume and product information). This enables the identification of the typical strengths and weaknesses of sellers within each cluster. To illustrate, while sellers in a cluster with high sales volume and customer satisfaction scores may assume a pioneering role in the introduction of new products, it may be beneficial to investigate which products could be preferred in the region where sellers in a low-performing cluster are located, and what measures could be taken to increase sales of these products. By examining the sales performance of clustered sellers, it is possible to ascertain the relationships among the best-selling products across different applications. This approach enables the identification of products sold in conjunction, products that stimulate each other's sales, and products that appeal to disparate customer segments. Following the cluster analysis, an association analysis enables a more comprehensive investigation of the interrelationships among products. The results of this analysis permit the identification of product preferences among specific customer profiles. Based on the information mentioned above, more effective product recommendations and personalized marketing strategies can be formulated. An examination of sales within the identified clusters reveals pertinent information.

Keywords

Sales strategies , Clustering , RFM analysis , Association rule analysis

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.

Supporting Institution

This research received no external funding.

Thanks

The author/authors do not wish to acknowledge any individual or institution.

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