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VERİ MADENCİLİĞİ TEKNİKLERİ İLE BİR KOZMETİK MARKANIN AYRILAN MÜŞTERİ ANALİZİ VE MÜŞTERİ BÖLÜMLENMESİ

CHURN ANALYSIS AND CUSTOMER SEGMENTATION OF A COSMETICS BRAND USING DATA MINING TECHNIQUES

Journal Name:

  • Sigma Mühendislik ve Fen Bilimleri Dergisi

Publication Year:

  • 2008

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Data minig is the process of finding hidden and unknown patterns in huge amounts of data. Data mining has a wide application area such as marketing, banking and finance, medicine and manufacturing. Churn analysis and customer segmentation are two common application areas of data mining . Churn modeling is predicting which customers will leave the company. This allows companies to increase customer loyalty by producing special campaigns. Customer segmentation is the process of dividing customers into mutually exclusive groups. Segmenting customers enables companies to develop customized marketing programs for them. This study aims to determine the customer profile who likely to leave and the customer segments of a cosmetic brand and develop customized campaigns and marketing strategies. Clustering techniques used for segmentation and classification techniques used for determining the churners.
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Abstract (Original Language): 
Veri madenciliği, büyük veri kümeleri içindeki anlamlı bilgiyi ortaya çıkarma sürecidir. Veri madenciliğinin en yaygın kullanıldığı uygulama alanlarından biri, ayrılma eğilimi gösteren müşteri kesitini belirleme ve müşteri bölümlenmesidir. Ayrılma eğilimi gösteren müşteri kesitini belirleme, şirketlerin bu müşterilere özel pazarlama kampanyalarını geliştirmelerini sağlamaya yöneliktir. Müşteri bölümlenmesi ise benzer özellikler gösteren müşterileri gruplandırarak; gruplara özel, pazarlama programlarının geliştirilmesini sağlar. Yapılan çalışma, bir kozmetik markasının müşteri gruplarını ve ayrılma eğilimi gösteren müşteri kesitini belirleyerek; bu müşterilere özel pazarlama stratejileri geliştirilmesini hedeflemektedir. Bölümlenme için kümeleme teknikleri, ayrılacak müşteri kesitini belirlemek için sınıflama teknikleri kullanılmıştır.
FULL TEXT (PDF): 
PDF icon arastrmx_845_26_pp_43-57.pdf
  • 1
43-57
  • Turkish

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