Mücevherat Satin Alan Hanelerin Özellikleri: Makine Öğrenmesi Perspektifi

Yıllık olarak 300 milyar dolar’dan büyük ve büyümeye devam eden mücevherat pazarı, lüks tüketimde önemli bir yere sahiptir. Mücevherat pazarlaması ve tüketimi literatürde daha çok nedensellik perspektifinde, “insanlar neden mücevher alır?” benzeri sorularla ilgilenilmiştir. Bu çalışma, araştırma sorusu olarak “Mücevher satın alımı yapılan hanelerin karakteristik özellikleri nedir?” sorusuyla farklı bir perspektif sunmaktadır. Çalışmada Türk İstatistik Kurumundan (TÜİK) alınan üç yıllık hanehalkı harcama verileri kullanılmış ve kullanılan veriler enflasyondan arındırılmıştır. Ardından kümeleme analiziyle eldeki veri sınıflandırılmış, sonrasında ise CHAID algoritmasıyla karar ağacı oluşturulmuştur. Yazarların bilgisine göre bu metodoloji mücevherat pazarlaması alanında daha önce kullanılmamıştır. Çalışmanın sonuçlarına göre orta ve düşük gelirli hanelerde ağaç yapısı ve dallanmalar bulunmuş, mücevher satın alma olasılığı en yüksek ve en düşük hane tipleri belirlenmiştir: genç ve yüksek eğitimli hane reislerine sahip düşük ve orta gelirli hanelerin mücevher satın alma olasılığının daha yüksek olduğu görülürken en az olası grupların ise daha az eğitimli ve 60 yaş üstü hane reisinin bulunduğu haneler olduğu görülmüştür. Yüksek gelirli hanelerin mücevher satın alma davranışı açısından homojen olduğu görülmüştür.

Characteristics Of Jewelry Purchasing Households: A Machine Learning Perspective

With an ever-growing market, sized over $300 billion annually, jewelry consumption has a significant place in luxury consumption. Jewelry marketing and consumption are usually studied from a causal perspective, as the most frequent question can be defined as “why do people buy jewelry?” in literature. This study offers a different perspective as the research question of this paper is “what are the characteristics of the households who purchase jewelry in Turkey?”. Three years of household spending data was collected from the Turkish Statistical Institute (TÜİK) and inflation adjusted. Then data is classified using cluster analysis to detect major groups in data, and then the CHAID algorithm is used for conducting decision trees. To the best of the authors’ knowledge, the methodology is unique in jewelry marketing literature. Research findings show that lower and middle-income households show a tree form, and sub-branches are found and the most and the least likely household types are identified: lower and medium-income households with younger and higher educated house heads are more likely to purchase jewelry, and the least likely groups are those with house heads who are less educated and aged over 60, whereas higher-income households look homogenous in terms of jewelry buying behavior.

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