Customer Satisfaction, Switching Cost, and Customer Loyalty at Vodafone Ghana.

ABSTRACT The Ghanaian mobile telecommunications industry is rapidly expanding, with fierce rivalry among its three main operators: MTN, Vodafone, and AirtelTigo. Despite a significant number of service providers, customers are growing increasingly dissatisfied with the quality of services they receive. The purpose of the study is to examine the relationship between customer satisfaction, switching cost, and customer loyalty within Mobile Telecommunication Network (Vodafone) Ghana. The study adopted the quantitative research approach and descriptive research design. The study was then conducted among customers of Vodafone Ghana in the telecommunication industry in Ghana. The study used a sample of four hundred and fifty (450) customers of Vodafone Ghana. It was discovered that there are high levels of satisfaction among customers of Vodafone Ghana. Again, the study found that customers’ level of loyalty to the services of Vodafone is very good. It was found that customer satisfaction contributes to a significant and positive variance in customer loyalty in the Vodafone telecommunication firm in Ghana. Further, it was found that non-financial switching cost significant positive effect on customer loyalty. Finally, the results showed that financial moderates significantly the relationship between customer satisfaction and customer loyalty. However, the study found that this relationship was negative. Finally, the study found that non-financial switching costs moderated significantly and positively the link between customer satisfaction and customer loyalty. It was recommended that managers should consider implementing customer retention programs. These programs can include loyalty rewards, exclusive offers, and personalized incentives for long-term customers. By recognizing and rewarding customer loyalty, managers can reinforce positive behaviour and create a sense of appreciation among customers

Application of artificial neural network in market segmentation: A review on recent trends

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table

Customer-oriented risk assessment in Network Utilities

For companies that distribute services such as telecommunications, water, energy, gas, etc., quality perceived by the customers has a strong impact on the fulfillment of financial goals, positively increasing the demand and negatively increasing the risk of customer churn (loss of customers). Failures by these companies may cause customer affection in a massive way, augmenting the intention to leave the company. Therefore, maintenance performance and specifically service reliability has a strong influence on financial goals. This paper proposes a methodology to evaluate the contribution of the maintenance department in economic terms, based on service unreliability by network failures. The developed methodology aims to provide an analysis of failures to facilitate decision making about maintenance (preventive/predictive and corrective) costs versus negative impacts in end-customer invoicing based on the probability of losing customers. Survival analysis of recurrent failures with the General Renewal Process distribution is used for this novel purpose with the intention to be applied as a standard procedure to calculate the expected maintenance financial impact, for a given period of time. Also, geographical areas of coverage are distinguished, enabling the comparison of different technical or management alternatives. Two case studies in a telecommunications services company are presented in order to illustrate the applicability of the methodology

Review of Data Mining Techniques for Churn Prediction in Telecom

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models

APPLICATION OF PREDICTIVE ANALYTICS IN CUSTOMER RELATIONSHIP MANAGEMENT: A LITERATURE REVIEW AND CLASSIFICATION

This study is aimed to provide a comprehensive literature review and a classification scheme for application of predictive analytics and tools in customer relationship management (CRM). The application of predictive analytics in CRM is an emerging trend. PA methods help to analyze and understand customer behaviors and acquire and retain customers and also maximize customer value. Thus it facilitates CRM decisions making and supports development of CRM strategies in a customer-centric economy. This paper is aimed to present a comprehensive review of literature related to application of predictive analytics in CRM published in both academic and practitioner journals between 2003 and 2013

Importance-Performance (IPMA) Analysis of Loyalty in Indonesia Cellular Operator During COVID-19 Pandemic

Abstract. During the pandemic of Covid-19, customer loyalty becomes the crucial thing that needs to be concerned by cellular operators due to the internet usage is growing rapidly in the activities of work from home and study from home. In dealing with this, the company can find out from the user response regarding to their experience, satisfaction, switching barriers, and corporate image. User experience consists of functionality, social, monetary, trustworthiness, and perceived service quality. This research was conducted to 385 respondents of Indonesian cellular operators that was spread by internet survey. expert opinion was conducted before distributing questionnaires as many as 75 items with quota sampling technique. Based on Importance and Performance Matrix Analysis (IPMA) results, it was found that companies must concentrate first on trustworthiness, satisfaction, and corporate image. Then pay attention to monetary, switching barriers, and social. Variables that were considered low priority are functionality and perceived service quality, this is because the company's performance was very high compared to the level of importance of the user.Keywords: Corporate image, COVID-19, IPMA, loyalty, user experienceAbstrak. Saat pandemi Covid-19, loyalitas pelanggan menjadi hal krusial yang perlu diperhatikan oleh operator seluler karena penggunaan internet berkembang pesat dalam aktivitas bekerja dari rumah dan belajar dari rumah. Dalam menghadapi hal ini, perusahaan dapat mengetahui dari tanggapan pengguna mengenai pengalaman, kepuasan, peralihan hambatan, dan citra perusahaan mereka. Pengalaman pengguna terdiri dari fungsionalitas, sosial, moneter, kepercayaan, dan kualitas layanan yang dirasakan. Penelitian ini dilakukan terhadap 385 responden operator seluler Indonesia yang disebarkan melalui survei internet. Pendapat ahli dilakukan sebelum menyebarkan kuesioner sebanyak 75 item dengan teknik quota sampling. Berdasarkan hasil Importance and Performance Matrix Analysis (IPMA), ditemukan bahwa perusahaan harus berkonsentrasi terlebih dahulu pada kepercayaan, kepuasan, dan citra perusahaan. Kemudian perhatikan moneter, peralihan hambatan, dan sosial. Variabel yang dianggap prioritas rendah adalah fungsionalitas dan persepsi kualitas layanan, hal ini dikarenakan kinerja perusahaan sangat tinggi dibandingkan dengan tingkat kepentingan pengguna.Kata Kunci: Citra perusahaan, COVID-19, loyalitas, pengalaman penggun