# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 2 Literature Review  
Technological progress is crucial in determining who will be the market leader and to achieve better market performance [7]. Meanwhile, technological progress has already changed the competition and the game's rules in the telecom industry. In the past, telecom operators generally won customers through price competition. However, today's consumers pay more attention to differentiated and value-added services, which has increased switching costs while making consumers more loyal [8]. In the telecom section, technological progress could help companies identify customers with a high risk of churn and to establish a business strategy with customer retention as the core goal, which will make the companies healthier and allow for long-term operation [9]. Finally, the development of telecommunication technologies has also brought about more market competition and higher customer churn rates. The customer churn rate for telecom customers in the European market has reached 30%, while in Asia it has reached 60% [10]. Through a Bayesian belief network analysis, it was concluded that the average tariff amount will affect customer churn. The two other factors are the average call time and tariff type [11]. The tariff structure will affect customers' perceptions of value, affecting customer churn [12]. Through a multilayer perceptron (MLP) analysis of a sample of five thousand Jordanian telecom customers, it was concluded that the monthly tariff is the most significant factor affecting customer churn [13]. Tariffs for domestic calls are essential in predicting customer loss [14]. There are two types of pricing in the telecom section: two-part tariff and pay-per-use pricing. Compared with two-part tariffs, pay-per-use pricing can reduce the customer churn rate by 10.5% [12]. Through a discriminant analysis and t-test of one thousand Indian telecom customers, it was concluded that the tariff rates for calls and customer satisfaction with the telecom service offered are the two key factors determining customer churn [13]. As competition in the telecommunications market intensifies, providing tariff price promotions and differentiated services for the key customers will be an efficient method to avoid customer churn [15]. In the Korean market, the tariff rate is one of the critical factors determining customer churn. Tariffs and customer care services are the two main factors influencing customer satisfaction and churn, as shown using discriminant and regression analyses [16]. Service quality in the telecom industry refers to Internet signal quality. Good service quality will improve customer satisfaction and loyalty, lowering the risk of customer loss [17]. Additionally, it will also help to attract new customers. Through a factor analysis and regression analysis, it was concluded that tariffs and service quality are key factors in prepaid customer churn. Hence, companies need to monitor and improve their service quality [13]. Customer retention and loss are influenced by the customers' sociodemographic characteristics and satisfaction [13]. The customers' sociodemographic data, for example regarding gender, could be used to predict whether customers will be lost or not [18]. Age and gender will influence telecom customers' preferences and behavior. People aged less than thirty years value customer service quality, value-added services, and mobile service fees. The tariff is not a key factor in determining churn for this segment. However, those aged older than thirty years pay more attention to tariff pricing, which will largely influence their retention or loss [19]. Predicting customer churn is not an easy task, since customer behaviors are heterogeneous [20]. In the past, companies have tended to investigate customer churn using traditional methods such as surveys. However, the data mining approach has been proven to be an efficient and better solution [21]. Specifically, a customer churn prediction model could be established to understand the factors that lead to customer churn and to predict customer loss.