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Credit Card Churn Prediction With Machine Learning Algorithms
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Konuksal, Serap; Ağralı, Semra
Credit card is one of the main products in banking sector and there is a big competition in credit card business. This competition makes retention of customers critical. To retain the customers, it is very important to interpret the customers that may churn. Targeting right customers with right offer is the main aim of Customer Relationship Management (CRM) in marketing. When the churn probability of customers is predicted, it is easier to retain the customers by proposing the retention offers directly to the ones with high churn probability. This will allow banks to manage their marketing budgets efficiently. In this project, a private bank’s credit card customer data is used. Data includes many different types of features of customers, such as number and type of transactions, credit card limits, feature usage, credit bureau information and demographic information. We develop a set of churn prediction models by implementing different machine learning algorithms. We compare these algorithms to find the best model with highest accuracy to be offered to the bank. We also share the main indicators that affect churn so that the bank can use them in retention activities.
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.

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