Track: Big Data and Analytics

Abstract

Customer need is the most important factor in the formation of each market and business. It engages companies in order to meet their need by developing new products and services. Although it can make an appropriate attraction for company customers, a company needs to have a good understanding of their customer dynamic behavior. Based on this understanding, they can provide appropriate planning for customer’s retention. In banks, customers are key components of the banking business. All of their strategies and plans are organized to attract new customers, retain current customers, and ultimately enhance customer satisfaction. Meanwhile, customer churn is one of the most important problems for this business. It deprives a bank of various earnings and fee incomes. And more importantly, customer deposit is the main source of the incomes earned by a bank in the Islamic banking system. It may lead to the withdrawal part of a bank’s deposits. By considering the loss of these two sources of incomes, along with the possibility of increasing the reputational risk, can lead a bank to the brink of bankruptcy. The present study provides a model of customer churn prediction for retail customers of a commercial bank in Iran. By applying advanced data analysis techniques on transaction and operation data of the bank customers, appropriate classification of customers is presented in terms of the churn rate. So by executing suitable strategies for each category, it can be developed to reduce the amount of customer churn.  In this research, a private bank in Iran has been used to predict the customer's bank data. The results show that the jobs associated with food services including restaurants and fast food retailers, as well as the technical services, have the highest churn rate in the bank. After them, the sports centers, as well as the household, are in the next ranks of churn from the bank services. In contrast to counseling centers, kindergartens, and governmental organizations, respectively, were the lowest risky corporate customers of the bank. Also, in retail customers, clients aged 30-40 years had the largest churn in the bank’s services.