Consumers’ preference and satisfaction of GSM service providers among students of tertiary institutions in Lagos State, Nigeria

This study contributes to intellectual discuss on consumer preference and satisfaction for GSM service providers in the context of students of tertiary institution in Lagos State, Nigeria. Using primary data, the study provide answer to four research questions and tested four hypotheses with a view to establish the influence of consumers’ preference and satisfaction of GSM service providers in Nigeria. The findings revealed a significant positive relationship between service assurance, service empathy, service reliability, service responsiveness and customers’ satisfaction of GSM service provision among students of tertiary institution in Lagos State. Also, the study also indicated that all variables accounted for 64% of the variance in service satisfaction (R2 = .645; F = 20.400, P = .000). Nonetheless, service reliability (β = .267, t = 3.439, p = .001) was found to have the greatest influence on service satisfaction followed by service assurance (β = .170, t = 2.987, p = .005) and service responsiveness (β = .135, t = 2.056, p = .046). On the other hand, only service empathy was not found to have influence service satisfaction.Keywords: Consumer preference, customer satisfaction, assurance, responsiveness, reliabilit

Abandono en servicios - Una revisión bibliométrica

[EN] The purpose of this article is to identify the most impactful research on customer churn and to map the conceptual and intellectual structure of its field of study. Data were collected from the WoS database, comprising 338 articles published between 1995 and 2020. Several bibliometric techniques were applied, including analysis of co-words, co-citation, bibliographic coupling, and co-authorship networks. R software and the Bibliometrix/Biblioshiny package were used to perform the analyses. The results identify the most active and influential authors, articles, and journals on the topic. More specifically, through co-citations and bibliographic coupling, it was possible to map the oldest articles (retrospective analysis) and the current research front (prospective analysis). The retrospective analysis, based on co-citations, revealed that the foundations of this research field are constructs such as quality of service, satisfaction, loyalty, and changing behaviors. The prospective analysis, performed through bibliographic coupling, revealed that current research is embedded in predictive analysis, clusters, data mining, and algorithms. The results provide robust guidance for further investigation in this field.[ES] El objetivo de este artículo es identificar las investigaciones más impactantes sobre la pérdida de clientes y trazar la estructura conceptual e intelectual de su campo de estudio. Los datos han sido recogidos de la base de datos WoS, que comprenden 338 artículos publicados entre 1995 y 2020. Varias técnicas bibliométricas fueron aplicadas, incluyendo el análisis de co-palabras, cocitaciones, acoplamiento bibliográfico y redes de coautoría. Para realizar los análisis se utilizaron el software R y el Bibliometrix/Biblioshiny. Los resultados identifican los autores, artículos y revistas más influyentes y activos sobre el tema. Más específicamente, a través de las cocitaciones y el acoplamiento bibliográfico, fue posible mapear los artículos más antiguos (análisis retrospectivo) y la investigación más actual (análisis prospectivo). El análisis retrospectivo, basado en las cocitaciones, reveló que los fundamentos de este campo de investigación son constructos como la calidad del servicio, la satisfacción, la lealtad y el cambio de comportamientos. El análisis prospectivo, realizado a través del acoplamiento bibliográfico, reveló que la investigación actual está inmersa en el análisis predictivo, los conglomerados, la minería de datos y los algoritmos. Los resultados proporcionan una sólida orientación para seguir investigando en este campo

Improved Customer Churn and Retention Decision Management Using Operations Research Approach

The relevance of operations research cannot be overemphasized, as it provides the best possible results in any given circumstance, through analysis of operations and the use of scientific method thus, this paper explore the combination of two operations research models (analytic hierarchy process and Markov chain) for solving subscribers’ churn and retention problem peculiar to most service firms. A conceptual model for unraveling the problem customer churn and retention decision management was proposed and tested with data on third level analysis of AHP for determining appropriate strategies for customer churn and retention in the Nigeria telecommunication industries. A survey was conducted with 408 subscribers; the sample for the study was selected through multi-stage sampling. Two analytical tools were proposed for the analysis of data. These include: Expert Choice/Excel Solver (using Microsoft Excel) and Windows based Quantitative System for Business (WinQSB). This paper plays important role in understanding various strategies for effective churn and retention management and the ranking of churn and retention drivers in order of importance to stakeholders` decision-making. The study provided a framework for understanding the application of AHP and Markov chain for modeling, analysing and proffering solution to problem of churn and retention. The study recommends organizational strategies (corporate, business and functional) that reverse the churn alternatives with high priority and equally strengthen service delivery on high priority retention alternatives in order to ensure firms sustainable competitive advantage. An erratum to this article has been published as https://doi.org/10.5195/emaj.2017.131

Research trends in customer churn prediction: A data mining approach

This study aims to present a very recent literature review on customer churn prediction based on 40 relevant articles published between 2010 and June 2020. For searching the literature, the 40 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to six main dimensions: Reference; Areas of Research; Main Goal; Dataset; Techniques; outcomes. The research has proven that the most widely used data mining techniques are decision tree (DT), support vector machines (SVM) and Logistic Regression (LR). The process combined with the massive data accumulation in the telecom industry and the increasingly mature data mining technology motivates the development and application of customer churn model to predict the customer behavior. Therefore, the telecom company can effectively predict the churn of customers, and then avoid customer churn by taking measures such as reducing monthly fixed fees. The present literature review offers recent insights on customer churn prediction scientific literature, revealing research gaps, providing evidences on current trends and helping to understand how to develop accurate and efficient Marketing strategies. The most important finding is that artificial intelligence techniques are are obviously becoming more used in recent years for telecom customer churn prediction. Especially, artificial NN are outstandingly recognized as a competent prediction method. This is a relevant topic for journals related to other social sciences, such as Banking, and also telecom data make up an outstanding source for developing novel prediction modeling techniques. Thus, this study can lead to recommendations for future customer churn prediction improvement, in addition to providing an overview of current research trends.info:eu-repo/semantics/acceptedVersio

Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms

Customer churn is a serious problem, which is a critical issue encountered by large businesses and organizations. Due to the direct impact on the company's revenues, particularly in sectors such as the telecommunications as well as the banking, companies are working to promote ways to identify the churn of prospective consumers. Hence it is vital to investigate issues that influence customer churn to yield appropriate measures to diminish churn. The major objective of this work is to advance a model of churn prediction that helps telecom operatives to envisage clients that are most probable to be subjected to churn. The experimental approach for this study uses the machine learning procedures on the telecom churn dataset, using an improved Relief-F feature selection algorithm to pick related features from the huge dataset. To quantify the model's performance, the result of classification uses CART and ANN, the accuracy shows that ANN has a high predictive capacity of 93.88% compared to the 91.60% CART classifie

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

An Overview and Examination of the Indian Services Sector

India’s service sector has grown rapidly since the 1990s. Domestic demand for services has increased as incomes have risen, triggering the expansion of industries such as banking, education, and telecommunications. Exports have also increased rapidly, led by information technology and business process outsourcing (IT-BPO). India’s ability to offer low-cost, high-quality IT-BPO services has made it a world leader in this industry. However, employment in services has not grown as quickly as output. The majority of India’s jobseekers are low-skilled, but demand for workers is growing fastest in higher-skill industries. The supply of highly-skilled workers has not kept pace with demand, causing wages to increase faster for these workers than for lower-skilled ones. India’s government has supported the growth of service industries through a mix of deregulation, liberalization, and incentive programs, such as the Software Technology Parks of India. Nevertheless, burdensome regulations, poor infrastructure, and foreign investment restrictions continue to affect service firms’ ability to do business. USITC analysis suggests that additional liberalization would lead to an increase in India’s imports of services