Mobile Subscribers\u27 Willingness to Churn Under the Mobile Number Portability (MNP)

Mobile number portability (MNP) means that customers using mobile service can retain their telephone numbers when they change operators. Mobile carriers are concerned about which factors influence willingness to churn of customers. This study examined these concerns through three steps. The first is checking whether willingness to churn between the different situations, without MNP and with MNP differs. The second is to figure out how different factors influence willingness to churn in those situations. Finally, a classification model is proposed to categorize the subscribers who have willingness to churn or not. This study shows that the difference of willingness to churn before and after MNP. The results show that quality and price are the most influential factors in both cases. Inconvenience of changing numbers is additional factor in case of the unavailability of MNP. The results have implications particularly that mobile carriers should consider their quality of service and price more under the new situation

Not just the best years of my life: personal growth in higher education

Our conception of product affirmation depicts a product as “sculptor” of the consumer’s ideal self, similar to how a relationship partner can help us achieve our aspirations and goals. We performed two studies to look at the role of higher education as a product in affirming a consumer’s ideal self. We found that product affirmation for undergraduate students and alumni (with the university as the product that affirms the ideal self of the student/alumnus) leads to increases in the experience of various positive emotions, the acquisition of various positive traits, and positive evaluations of the university. Additionally, we found that product affirmation effects were more pronounced and robust in one’s personal ideal-self domain than in one’s professional ideal-self domain. Practical implications, study limitations, and future directions are discussed, as well as preliminary findings from a follow-up experiment using a sample of graduate students

The effect of friends’ churn on consumer behavior in mobile networks

We study how consumers decide which tariff plan to choose and whether to churn when their friends churn in the mobile industry. We develop a theoretical model showing conditions under which users remain with their carrier and conditions under which they churn when their friends do. We then use a large and rich anonymized longitudinal panel of call detailed records to characterize the consumers’ path to death with unprecedented level of detail. We explore the structure of the network inferred from these data to derive instruments for friends’ churn, which is typically endogenous in network settings. This allows us to econometrically identify the effect of peer influence in our setting. On average, we find that each additional friend that churns increases the monthly churn rate by 0.06 percent. The observed monthly churn rate across our dataset is 2.15 percent. We also find that firms introducing the pre-paid tariff plans that charge the same price to call users inside and outside the carrier help retain consumers that would otherwise churn. In our setting, without this tariff plan the monthly churn rate could have been as high as 8.09 percent. We perform a number of robustness checks, in particular to how we define friends in the social graph, and show that our results remain unchanged. Our paper shows that the traditional definition of customer lifetime value underestimates the value of consumers and, in particular, that of consumers with more friends due to the effect of contagious churn and, therefore, managers should actively take into account the structure of the social network when prioritizing whom to target during retention campaigns.info:eu-repo/semantics/acceptedVersio

Customer Churn Prediction in Telecom Sector: A Survey and way a head

© 2021 International Journal of Scientific & Technology Research. This work is licensed under a Creative Commons Attribution 4.0 International License.The telecommunication (telecom)industry is a highly technological domain has rapidly developed over the previous decades as a result of the commercial success in mobile communication and the internet. Due to the strong competition in the telecom industry market, companies use a business strategy to better understand their customers’ needs and measure their satisfaction. This helps telecom companies to improve their retention power and reduces the probability to churn. Knowing the reasons behind customer churn and the use of Machine Learning (ML) approaches for analyzing customers' information can be of great value for churn management. This paper aims to study the importance of Customer Churn Prediction (CCP) and recent research in the field of CCP. Challenges and open issues that need further research and development to CCP in the telecom sector are exploredPeer reviewe

Determinants of Financial Performance in Internet Service Providers in Kenya

Due to the increase in competition and increasing market liberalization, firms in the telecommunications industry are facing a threat to the profit sustainability. Furthermore, customers’ preferences are dangerously volatile and satisfaction of their diverse needs can prove arduous and the heightened customer awareness results in search for better alternative offerings in the market. As a result, customers may voluntarily switch from one vendor to the other. The study adopted a descriptive study design. The population was ISP firms in Kenya from which 10 large ISPs were selected. A total of 50 questionnaires were mailed to the managers of these firms. Primary data was collected through structured questionnaires. Data was analyzed using descriptive analysis and regression analysis. The study found that before customer make a decision to purchase or stay with a service provider, they considered service uptime, network coverage, and customer service. The regression results showed that product pricing, customer service, and service uptime had negative but insignificant effects on firm performance while parent shareholding and network coverage had positive effects on firm performance. The study concludes that the factors influencing customer staying in a provider were service uptime, customer service, and network coverage. The study also concludes that product pricing, customer service, parent shareholding, network coverage, and service uptime do not have a significant effect on firm performance. Thus the financial performance of ISPs in Kenya is not influenced by the churn factors.  The study recommends that Internet Service Providers in Kenya should ensure that they enhance the level of service uptime as this was a major factor that customers considered before making a decision to purchase an ISP product or to stay with the same ISP. The study also recommends that Internet Service Providers should have large network coverage and not just limit themselves to a small or specific area to cover. Third, the study recommends that Internet Service Providers should invest in a modern and efficient customer care service that can provide solutions to customers who have issues with their internet. This was an important purchase decision factor by customers. Keywords: Financial Performance, Internet Service Providers, Keny

Three dimensional modelling of customer satisfaction, retention and loyalty for measuring quality of service

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The aim of this thesis is to propose a model that explains the relationship between customer satisfaction, retention and loyalty based on service quality attributes. The three elements of satisfaction, retention and loyalty towards products represent ongoing challenges for the corporate financial performance. Customer behaviour analysis (known as business intelligence or customer relationship management or customer experience management) has become a major factor in the corporate decision making and strategic planning processes. Prevailing logic dictates that by improving service attributes one should expect better customer satisfaction levels. Consequently, improved satisfaction levels should increase the probability of customer retention and degree of loyalty. Substantial research work has been dedicated to explain the importance of customer behaviour measurement for industry. However, there is little evidence that there has been an overall integrating empirical research that relates the three elements of satisfaction, retention and loyalty with respect to service quality attributes. Empirical data collected from the UK mobile telecommunication for this research shows that such an objective model that is capable of capturing this three dimensional relationship will contribute towards more robust decision making and better strategic planning. The proposed thesis extracts the data about key service attributes from a combination of literature review, surveys, and interviews from the UK mobile telecommunication industry. Responses were analysed using multiple regression, regression analysis with dummy variables, logistic regression, logistic regression with dummy variables and structural equation modelling (SEM) to test variables and their interrelationships. This study makes a step forward and contributes to the body of knowledge as it: (a) highlights the role of service attribute performance towards customer satisfaction, consequently identifies attributes that affect satisfaction and dissatisfaction of customers, (b) maps the relationship between attribute importance and attribute performance, (c) optimise resource allocation process using importance-performance analysis (IPA), (d) classifies customers with respect to the role and length of relationship they have with the company (switching probability), and (e) describes the interrelationship between customer satisfaction, retention and loyalty. The novelty of the research lies in: (a) establishment of a framework that links service attribute performance to customer satisfaction and then to customer future intentions (customer retention and customer loyalty), and (b) provision of a model that could assist key decision makers in prudent usage of resources for maximum profitability. This dissertation presents a novel approach methodology and modelling construct for customer behaviour analysis. For proof of concept it presents a case study in the mobile telecommunication industry. It is worth noting that in this research work Customer Retention is interpreted as probability of switching between service providers. Customer Loyalty is interpreted as referral (word-of-mouth) activity by existing customers

Effects of Free Riders and Incentive Discrimination on Customer Acquisition and Retention Resource Allocation

How should a company best allocate its spending between acquisition and retention? Under what condition should a company devote resources and money to analytics? The above questions are just examples of more general issues concerning many companies when managing their customer acquisition and retention programs. To answer the above questions, I will conduct a study on the allocation of financial resources between incentives that target different types of customers, and the allocation of resources between incentives and analytics spending. This research first distinguishes between customers and acquisition, between incentive and price discount, and between acquisition and retention. It then proposes a new concept, “free rider”, in a customer acquisition and retention context. Building on the free-rider concept, two mathematical models are formulated to examine the optimal allocation between acquisition incentive, retention incentive, and analytics spending. Closed-form solutions are reached for both models and the results are interpreted in the context of marketing practice. The conditions leading to different patterns of optimal solutions of analytics spending, acquisition incentives, and retention incentives are discussed. Specifically, the detailed conditions under which the optimal acquisition incentives is zero or non-zero, the optimal retention incentives is zero or non-zero, and the optimal analytics spending is zero or non-zero, are provided. Factors determining the ceiling for acceptable level of cost of analytics are also examined