Infrastructure sharing model to connect the unconnected in rural areas

One of the major problems the telecommunication industry faces in providing connectivity to the unconnected, particularly in rural and remote areas, is the lack of infrastructure in these areas. Indeed, deploying a network in an isolated area can be more expensive for an operator than in an urban area, while the return on investment is not possible. This is the primary cause of the coverage divide. To remedy this, in this work, we propose a techno-economic analysis of infrastructure sharing. First, we develop a mathematical model of the overall cost of extending a mobile network in rural areas. Different scenarios involving infrastructure sharing at varying levels of deployment are then presented. Then, using the models proposed in each scenario, we make a case study to deduce the most economically advantageous scenario for operators to extend their networks to remote areas. This case involves the sharing of passive infrastructure and also the sharing of active resources in a cloud-RAN. Based on the proposed model, our simulation results show that while passive sharing is beneficial, active sharing using cloud-RAN as technology increases this benefit. This work also indicates and highlights the technical constraints to be respected in the sharing for this scenario.

Classification methods comparison for customer churn prediction in the telecommunication industry

The need for telecommunication services has increased dramatically in schools, offices, entertainment, and other areas. On the other hand, the competition between telecommunication companies is getting tougher. Customer churn is one of the areas that each company gains more competitive advantage. This paper proposes a comparison of several classification methods to make a prediction whether the customers cancel the subscription to a telecommunication service by highlighting key factors of customer churn or not. The comparison is non-trivial due to the urgent requirements from the telecommunication industry to infer the most appropriate techniques in analyzing their customer churn. This comparison is often of huge commercial value. The result shows that Artificial Neural Network (ANN) can predict churn with an accuracy of 79%, Support Vector Machine (SVM) with 78% accuracy, Gaussian Naïve Bayes, and K-Nearest Neighbor (KNN) with 75% accuracy, while Decision Tree with 70% accuracy. Moreover, the technique with the highest F-Measure is Gaussian Naïve Bayes with 65% and the technique with the lowest one is Decision Tree with 49%. Hence, ANN and Gaussian Naïve Bayes are two methods with high recommendation to predict the customer churn in the telecommunication industry.

Corporate Reputation and Customer Satisfaction in the Telecommunication Industry in Nigeria

Despite the widespread belief that corporate reputation is a valuable intangible asset that helps firms to compete favourably in the telecommunication industry, the scientific evidence is deficient on how corporate reputation influence customer satisfaction in the study area. The study focused on the relationship between corporate reputation and customer satisfaction. The survey research design method was employed in the study. The research instrument was a validated structured questionnaire. The reliability of the questionnaire was estimated by assessing the internal consistency of the items representing each construct. Cronbach’s alpha was used to establish the reliability of the constructs. The correlation and multiple regression analyses were used in the study. The sample size of 135 respondents was administered with the questionnaire. The finding of the analyses revealed that trustworthiness (B = 0.251, P<.001); corporate social responsibility (B = 0.283, P<.001); credibility (B = 0.198, P<.001) and reliability (B = 0.219, P<.001) were respectively significant and exhibited significant positive effects on customer satisfaction. The major conclusion that emerged from the findings is that corporate reputation is a multifaceted construct whose dimensions have significant positive relationships with marketing performance outcome of customer satisfaction in the mobile telecommunications services industry. In the competitive context, managers should apply the dimensions of corporate reputation that is appropriate for their target segment and develop their customer satisfaction strategy.

Long-Term Relationship Building and Customer Switching Behaviour in the Mobile Telecommunication Industry in Nigeria

This paper examined long-term relationship building and customer switching behavior among mobile telecommunication operators in Nigeria. The population of this study includes all mobile telecommunication company and their subscribers in Edo State, Nigeria. The samples for this study were drawn from Mobile Network Service Providers and their subscribers. For service providers, three (3) out of the nine (9) mobile network operators were selected (Nigeria Communication Commission NCC, 2020). Morgan and Krejcie's (1970) sampling methods were implemented to choose a sample of 322 mobile telecom subscribers Godden (2004). The paper employed regression analysis to analyze and test the hypothesis formulated. The result showed that long-term relationship building (Two-way Communication, Customer retention Strategy, Customer Loyalty, and Relational Bond) had a positive statistically significant relationship with customer switching behavior among mobile telecommunication operators (Network service providers) at p>0.05. The research recommends among others that players of the mobile telecommunication Industry (mobile network service providers) should embrace the application of long-term relationship-building to achieve, sustain and retain customers' usage of their products, this will enable them to compete better with other players in the industry. Also, they should engage in relational bonding aimed at creating attachment with the customer leading to customer retention and subsequently profitability.