Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785

Browse

Filters

20181

Settings

Department: search.filters.department.Lisansüstü Eğitim Enstitüsü, Büyük Veri Analitiği Yüksek Lisans ProgramıInstitution Author: search.filters.institutionAuthor.Kıralı, Gülşen

Search Results

Now showing 1 - 1 of 1
  • Master Term Project
    A Case Study on Churn Prediction and Understanding Customer Behavior
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Kıralı, Gülşen; Tönük, Gökçe
    Churn prediction is essential for businesses as it helps to detect customers who have the potential to cancel a subscription to a product or service. Churn prediction techniques try to understand the certain customer behaviors and attributes which signal the risk andtiming of customer churn. Companies started to focus on retention activities more in the last years since holding current customer in the system is less costly when compared with acquiring new ones. In order to allocate costs to right customers, companies prefer to use the big part of these budget to potential churn customers which makes the accuracy of churn detection important for us. The objective of this project is to develop a machine learning algorithm that predicts potential churn customers that will not make any transactions in the following three months. While predicting churn, some customer segments and subsegments are created in order to understand the common behavior of potential churn customers. Common characteristics of loyal customers will also beinvestigated in order to determine churn prevention marketing activities for potential churn customers. Among all of the machine learning algorithm trials including Logistic Regression, Boosted Decision Tree, Support Vector Machines, Decision Forest, Decision Forest Regression and Neural Networks, Logistic Regression predicts with the highest accuracy and lowest number of False Negative which means model slightly mistaken unchurned customers.