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Churn Prediction of a Deal E-Commerce Website Customers
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Çevik, Müge; Küçükaydın, Hande
Today, there is a lot of deal e-commerce sites which are essentially marketplaces. They provide deals which are offered by merchandisers. Because of the nature of these sites there is no subscription model; customers continue because of price or interest or quality not because of subscription. It is normal to have some customers who stop buying, which is defined by "churn". Data mining is now a new technique to define "churned" customers and to have prediction who will churn and what should be against. In this project customers are clustered via unsupervised clustering technique for clusters as "newly purchased", "frequently purchased" and "mostly payed" and "churned". Random Forest Classifier is used to prove that the "churned" customer clusters have homogeneous character and also it has been proved that the "churned" labelled customers have actually no deal order after the observed time period. To recommend what should be done to regain the churned customers to the site the deal order history of these customers have been explored and the deal categories from which they have bought have been found.

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