MAKİNE ÖĞRENMESİ YÖNTEMLERİYLE MÜŞTERİ KAYBI ANALİZİ

Oğuz Kaynar, Murat Fatih TUNA, Yasin GÖRMEZ, Mehmet Ali DEVECİ
1.158 271

Öz


Müşteri davranışlarını inceleyerek ayrılması muhtemel müşterilere ait profilleri çıkarıp terk etme olasılığı yüksek müşterileri önceden tahmin etme işlemi müşteri kaybı analizi olarak adlandırılmaktadır. Yeni müşteri kazanma maliyetinin eldeki müşteriyi tutma maliyetinden daha yüksek olması, müşteri terk etme analizini stratejik karar verme ve planlama sürecinin vazgeçilmez bir parçası haline getirmiştir.  Hızla büyüyen ve rekabet ortamının her geçen gün artığı, müşterilerin kolaylıkla operatör değiştirdiği ve bu nedenle firmaların milyonlarca dolar zarar ettiği telekomünikasyon sektöründe, müşteri ayrılma analizi daha da önem kazanmaktadır. Müşteri kaybı analizi, rakip firmaya geçmeyi planlayan müşterileri önceden tahmin ederek, şirkete bu müşterilerin bağlılığını arttırmayı hedefleyen çeşitli kampanyalar ve politikalar geliştirme fırsatı sunar. Müşteri kaybı analizi için son yıllarda veri madenciliği ve yapay zekâ teknikleri sıkça kullanılmaya başlanmıştır. Bu çalışmada, telekomünikasyon sektöründe müşteri kaybını tahmin etmek için, Destek Vektör Makineleri (DVM), Yapay Sinir Ağları (YSA) ve Naive Bayes (NB) gibi çeşitli sınıflama yöntemleri yardımıyla bir analiz gerçekleştirilmiştir. Analiz, açık erişimli bir veri tabanından elde edilen, 4667 müşteriden oluşan ve her müşteri için 21 adet işlem kaydına ait özellikler ile müşterinin terk edip terk etmediğine dair sınıf bilgisi içeren bir veri seti üzerinde gerçekleştirilmiştir. Analiz sonucunda, sadık ya da terk eden müşterileri sınıflamada yapay sinir ağları, diğer makine öğrenmesi yöntemlerine göre daha başarılı olmuştur.


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