[[abstract]]"Customer churn prediction is one of the most important problems in customer relationship management (CRM). Its aim is to retain valuable customers to maximize the profit of a company. To predict whether a customer will be a churner or non-churner, there are a number of data mining techniques applied for churn prediction, such as artificial neural networks, decision trees, and support vector machines. This paper reviews some recent patents along with 21 related studies published from 2000 to 2009 and compares them in terms of the domain dataset used, data pre-processing and prediction techniques considered, etc. Future research issues are discussed.
Customer churn prediction models aim to detect customers with a high propensity to attrite. Predicti...
Churn prediction aims to detect customers intended to leave a service provider. Retaining one custom...
As markets have become increasingly saturated, companies have acknowledged that their business strat...
Abstract In today’s competitive world, organizations are in a constant struggle to retain their curr...
The rapid growth of the market in every sector is leading to a bigger subscriber base for service pr...
The socio economic growth of the country is mainly dependent on the services sector. The financial s...
Customer churn prediction models aim to detect customers with a high propensity to attrite. Predicti...
Customer churn prediction models aim to detect customers with a high propensity to attrite. Predicti...
Customer churn prediction models aim to detect customers with a high propensity to attrite. Predicti...
Telecommunication sector generates a huge amount of data due to increasing number of subscribers, ra...
Churn prediction aims to detect customers intended to leave a service provider. Retaining one custom...
According to the lack of a comprehensive literature review in the area of application of data mining...
Customer churn is one of the most important metrics for a growing business to evaluate. It is a busi...
Customer churn is one of the most critical issues faced by the telecommunications industry. In the t...
The expenses for attracting new customers are much higher compared to the ones needed to maintain ol...
Customer churn prediction models aim to detect customers with a high propensity to attrite. Predicti...
Churn prediction aims to detect customers intended to leave a service provider. Retaining one custom...
As markets have become increasingly saturated, companies have acknowledged that their business strat...
Abstract In today’s competitive world, organizations are in a constant struggle to retain their curr...
The rapid growth of the market in every sector is leading to a bigger subscriber base for service pr...
The socio economic growth of the country is mainly dependent on the services sector. The financial s...
Customer churn prediction models aim to detect customers with a high propensity to attrite. Predicti...
Customer churn prediction models aim to detect customers with a high propensity to attrite. Predicti...
Customer churn prediction models aim to detect customers with a high propensity to attrite. Predicti...
Telecommunication sector generates a huge amount of data due to increasing number of subscribers, ra...
Churn prediction aims to detect customers intended to leave a service provider. Retaining one custom...
According to the lack of a comprehensive literature review in the area of application of data mining...
Customer churn is one of the most important metrics for a growing business to evaluate. It is a busi...
Customer churn is one of the most critical issues faced by the telecommunications industry. In the t...
The expenses for attracting new customers are much higher compared to the ones needed to maintain ol...
Customer churn prediction models aim to detect customers with a high propensity to attrite. Predicti...
Churn prediction aims to detect customers intended to leave a service provider. Retaining one custom...
As markets have become increasingly saturated, companies have acknowledged that their business strat...