Customer churn has been stated as one of the main reasons of profitability losses in the telecommunications industry. As such, it seems critical to have an a priori knowledge about the risk of a given customer to churn at any moment, in order to take preventive measures to avoid the defection of potentially profitable customers. This study intends to develop a duration model of the residential ...

In this paper, bagging and boosting techniques are proposed as performing tools for churn prediction. These methods consist of sequentially applying a classification algorithm to resampled or reweigthed versions of the data set. We apply these algorithms on a customer database of an anonymous U.S. wireless telecom company. Bagging is easy to put in practice and, as well as boosting, leads to a ...

There is a growing tendency for more companies to develop towards subscription business model. Under such trend, it important learn about the customer churn rate within business, from and adjust strategies accordingly. This paper aims predict in models using variety of machine learning algorithms. Through comparing results different algorithms, best algorithms can be identified so that provides...

Aggressive marketing campaigns to attract new customers only covers customer churn, resulting in neither growth nor profitability. Retaining current customers, increasing their lifetime value, and reducing customer churn rates, thereby allowing greater efforts and resources to be dedicated to capturing new customers are the goals of a commercial director. But how can that loss be detected in ti...

Churn prediction aims to identify subscribers who are about to transfer their business to a competitor. Since the cost associated with customer acquisition is much greater than the cost of customer retention, churn prediction has emerged as a crucial Business Intelligence (BI) application for modern telecommunication operators. The dominant approach to churn prediction is to model individual cu...

Accurately predicting customer churn using large scale time-series data is a common problem facing many business domains. The creation of model features across various time windows for training and testing can be particularly challenging due to temporal issues common to time-series data. In this paper, we will explore the application of extreme gradient boosting (XGBoost) on a customer dataset ...

Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. A new customer classification algorithm for electronic commerce enterprises is advanced based on analyzing customer characteristics and behaviors. First, based on consumer characteristics and be...

Customer defection is critically important since it leads to serious business loss. Therefore, investigating methods to identify defecting customers (i.e. churners) has become a priority for telecommunication operators. In this paper, a churn prediction framework is proposed aiming at enhancing the ability to forecast customer churn. The framework combine two heuristic approaches: Self Organizi...