Churn prediction is a useful tool to predict customer at churn risk. By accurate prediction of churners and non-churners, a company can use the limited marketing resource efficiently to target the churner customers in a retention marketing campaign. Accuracy is not the only important aspect in evaluating a churn prediction models. Churn prediction models should be both accurate and comprehensib...

in order to survive and sustain the competitive advantage in the competitive markets, many organizations have turned to relationship marketing by focusing on maximizing the life time value of their customers and churn management. in fact, more and more companies do realize that their most precious asset is the existing customer base. customer retention is a valuable strategy to ensure long term...

Churn prediction is an important factor to consider for Customer Relationship Management (CRM). In this study, statistical and data mining techniques were used for churn prediction. We use linear (logistic regression) and non-linear techniques of Random Forest and Deep Learning architectures including Deep Neural Network, Deep Belief Networks and Recurrent Neural Networks for prediction. This i...

Organizations always take part in competition and as the competition grows; they are more concern about their customers rather than products. Organization always focuses on customer’s behaviour to retain in market competition. Churn prediction models are developed to manage and control customer churn in order to retain existing customers. Churn prediction aims to predict profitable customers. T...

The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn. Keywords—Churn, Neural Networks, Regression,...

Customer churn prediction models aim to detect customers with a high propensity to attrite. Predictive accuracy, comprehensibility, and justifiability are three key aspects of a churn prediction model. An accurate model permits to correctly target future churners in a retention marketing campaign, while a comprehensible and intuitive rule-set allows to identify the main drivers for customers to...

The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn. Keywords—Churn, Decision Trees, Neural Netwo...

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting custome...

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...

We studied the problem of optimizing the performance of a DSS for churn prediction. In particular, we investigated the beneficial effect of adding the voice of customers through call center emails – i.e. textual information to a churn prediction system that only uses traditional marketing information. We found that adding unstructured, textual information into a conventional churn prediction mo...