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ÖZ-DÜZENLEMELİ HARİTA AĞLARI İLE K-ORTALAMA KÜMELEME ANALİZİNİN KARŞILAŞTIRILMASI: TÜKETİCİ PROFİLLEME ÖRNEĞİ

COMPARING SELF ORGANIZING MAPS WITH K-MEANS CLUSTERING: AN APPLICATION TO CUSTOMER PROFILING

Journal Name:

  • Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

Publication Year:

  • 2013

Key Words:

Keywords (Original Language):

Author NameUniversity of Author
Abstract (2. Language): 
Due to the increasing competitiveness in shopping environments, profiling consumers becomes critical for retailers and their managers. However, researchers investigated the consumer profiling based on either the shopping motivations, or the shopping values, or the consumers’ decision making styles separately mostly by using K-means clustering method in the literature. In this sense, a research investigating the shopping motivations and values, and consumers’ decision making styles together with a new clustering method, which overcomes the limitations of the K-means clustering method, is warranted in the literature. In this study, we used an Artificial Intelligence based technique, called Self Organizing Map (SOM), to profile consumers based upon their shopping motivations and values, and decision making styles. Our results also demonstrated that SOM’s total within cluster variance is smaller than K-means’s, indicating that SOM clustering is better than the K-means when the sample is non-normally distributed. The paper profiles clusters on demographics and ethnic group membership to examine similarities and differences among cluster members.
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Abstract (Original Language): 
Alışveriş ortamlarında artan rekabet nedeniyle tüketici profilleme, perakendeciler ve yöneticiler için kritik hale gelmiştir. Ancak araştırmacılar, tüketici profillemeyi alışveriş motivasyonları, alışveriş değerleri veya tüketicilerin karar verme stilleri bazında ayrı ayrı ve çoğunlukla K-ortalama kümeleme yöntemini kullanarak araştırmışlardır. Bu anlamda, tüketicilerin alışveriş motivasyon ve değerlerini ve karar verme stillerini K-ortalamalar kümeleme yönteminin kısıtlarının üstesinden gelen yeni bir kümeleme yöntemi kullanarak araştıran bir çalışmanın literatürde önemi büyüktür. Bu çalışmada, tüketicilerin alışveriş motivasyon ve değerleri ile birlikte karar verme stillerine dayalı profilini çıkarmak için Öz-düzenlemeli Harita Ağları olarak adlandırılan yapay zeka tabanlı bir teknik kullanılmıştır. Elde edilen sonuçlar, Öz-düzenlemeli Harita Ağlarının K-ortalama yöntemine göre daha iyi olduğunu göstermektedir. Bu araştırma, küme üyeleri arasındaki benzerlik ve farklılıkları incelemek amacıyla demografik ve etnik grup üyelikleri temelinde tüketici profillerini oluşturur.
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  • Turkish

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