In most industries where switching costs are prevalent, the landscape of activities is painted of Customers attrition or churn (Clients who want to switch or change their suppliers for various reasons). This phenomenon is ubiquitous in the telecommunication industry and every aspect related to it, leads to believe that it's steeply growing. As the market is fiercely competitive, and the number of prepaid customers is increasing, it is vital that companies proactively tackle the defection of their customers by determining behaviors that might ultimately create churn. In this paper we proposed a hybrid learning model to predict churn in mobile telecommunication networks. Experiments were carried out using WEKA a Machine Learning tool; along with a real dataset from an Asian mobile operator to evaluate the performance of the model. The results show that the new hybrid model is more accurate than single methods.

Purpose -More and more e-commerce web sites are using online customer reviews (OCRs) for customer segmentation. However, for durable products, customer purchases, and reviews only once for a long time, as while the product review score may highly affected by service factors or be "gently" evaluated. Existing regression or machine learning-based methods suffer from low accuracy when applied to the OCRs of durable products on e-commerce web sites. The purpose of this paper is to propose a new approach for customer segment analysis base on OCRs of durable products. Design/methodology/approach -The research proposes a two-stage approach that employs latent class analysis (LCA): the feature-mention matrix construction stage and the LCA-based customer segmentation stage. The approach considers reviewers' mention on product features, and the probability-based LCA method is adopted upon the characteristics of online reviews, to effectively cluster reviewers into specified segmentations. Findings -The research finding is that, using feature-mention instead of feature-opinion records makes segment analysis more effective. The research also finds that, LCA method can better explain the characteristics of the OCR data of durable products for customer segmentation. Practical implications -The research proposes a new approach to durable product review mining for customer segmentation analysis. The segment analysis result can provide supports for new product design and development, repositioning of existing products, marketing strategy development and product differentiation. Originality/value -A new approach for customer segmentation analysis base on OCRs of durable products is proposed.

Customer Churn is a pesky problem that continues to haunt telecommunication companies. Social influence analysis has recently been introduced in churn prediction, motivated by the fact thatsome users can churn due to accumulated churn influence that they’ve received from other churners.We’ve collected call data records of about 10 thousands mobile phone users of one the largest mobile network operators in China,and have built a Multi-relational call network which is a graph constructed by mobile phone users considered as nodes and the interactive calls between them considered as the relationships or edges. We’veapplied Linear Threshold (LT) to model the diffusion of churners influence in theobtainedsocial network, in order to analyze the relevance of social affinities in the diffusion of churn influence.The results indicate that churn influence diffusion depends not only on the number of initial churners but also on the existingaffinities between users.