Suchergebnisse

The transition of customers from one telecom operator to another has a direct impact on the company's growth and revenue. Traditional classification algorithms fail to predict churn effectively. This research introduces a deep learning model for predicting customers planning to leave to another operator. The model works on a high-dimensional large-scale data set. The performance of the model was measured against other classification algorithms, such as Gaussian NB, Random Forrest, and Decision Tree in predicting churn. The evaluation was performed based on accuracy, precision, recall, F-measure, Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) Curve. The proposed deep learning model performs better than other prediction models and achieves a high accuracy rate of 91%. Furthermore, it was noticed that the deep learning model outperforms a small size Neural Network for the customer churn prediction.

This article considers the importance of everyday encounters in underpinning sociality, focusing especially on the located and material aspects of social relations. Bringing together debates about social relations, place attachment and population turnover (or 'churn'), and using research carried out in the UK city of Leicester, in an inner city neighbourhood with high population turnover, the article investigates incidences of neighbourliness, probing what happens to social relations between neighbours in a place which could be considered to be unstable in terms of population, and where conviviality could be deemed to be under threat by wider structural economic forces. Three overlapping questions are addressed: how does neighbourliness manifest itself in such conditions, where is it practiced, and how do people relate to the material environment around them when the social landscape is apparently so unstable? Drawing on in-depth interviews with inner-city Leicester residents this article considers not only the narrated practice of social and neighbourly relations in a particular setting, but also how important place is to this practice.

The telecommunications industry is highly competitive, as operators engage in fierce attacks, especially in bundled services, to acquire new customers originating high churn rate. The objective of this paper is to gain a comprehensive understanding of the factors influencing the switching of operators for bundled services among telecom operators. The paper includes a quantitative study with 3,004 customers utilizing bundled services from a Portuguese telecom operator. Employing covariance-based structural equation modeling and logit regression, the research shows that internet service, television service, and the service provided by the contact center exert the greatest impact on loyalty to the operator. In contrast, landline service has an insignificant effect, while loyalty has a negative influence on customer churn. This study offers telecommunications managers insights for identifying the main factors to retain customers and curbing customer defection. Additionally, it provides a framework for assessing customer experience within bundled telecom services, which is useful for researchers, managers and marketing practitioners alike.