Kümelemelerin Karşılaştırılması: Bir Mağaza Segmentasyonu Uygulaması

Bu çalışma, kümelemelerin karşılaştırılması ölçülerinden biri olan çiftleri sayma tekniklerini (Rand Endeksi, Düzeltilmiş Rand Endeksi ve Fowlkes Mallows Endeksi gibi) incelemektedir. Bu çalışmanın amacı bahsi geçen tekniklerin özelliklerini tartışmak ve tekniklerin pazarlama alanındaki bir uygulamasını göstermektir. Uygulama olarak, zincir mağazalara sahip olan bir perakendecinin süpermarket mağazaları iki farklı yaklaşımla, kümeleme analizi kullanılarak segmentlere ayrılmıştır. İlk yaklaşımında mağazalar bulundukları yerin ve potansiyel müşterilerinin sosyoekonomik özelliklerine göre, ikinci yaklaşımda ise mağazalar kendi müşterilerinin satın alma davranışlarına göre segmentlere ayrılmıştır. Müşteri satın alma davranışları, sosyoekonomik faktörlerden güçlü bir şekilde etkilendiği için, bu çalışmanın beklentisi iki kümelemenin görüş birliğinde olması yönündedir. Analizler sonucunda Rand Endeksi iki kümeleme arasında bir görüş birliğinin olduğunu gösterse de, Fowlkes-Mallows Endeksi zayıf bir görüş birliğine, Düzeltilmiş Rand Endeksi ise görüş birliğinin olmadığına işaret etmektedir. 

Comparing Clusterings:A Store Segmentation Application

This study focuses on one of the clustering comparison measures, pair counting techniques such as Rand Index, Adjusted Rand Index and Fowlkes Mallows Index. The aim is discussing their properties and showing a marketing application of the techniques. For an application, a retail chain company’s supermarket stores are segmented with clustering analysis by two approach. The first clustering approach is segmenting stores based on socioeconomic factors and the second approach is based on purchasing behaviors of customers. Since consumer purchases are influenced strongly by socioeconomic factors, this study expects to find an agreement between two clusterings. The results show that while Rand Index value indicates an agreement, Fowlkes-Mallows Index value has found a weak agreement and Adjusted Rand Index value could not find any agreement between two clusterings.

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