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

NEMISA Digital Skills Conference (Colloquium) 2023

The purpose of the colloquium and events centred around the central role that data plays today as a desirable commodity that must become an important part of massifying digital skilling efforts. Governments amass even more critical data that, if leveraged, could change the way public services are delivered, and even change the social and economic fortunes of any country. Therefore, smart governments and organisations increasingly require data skills to gain insights and foresight, to secure themselves, and for improved decision making and efficiency. However, data skills are scarce, and even more challenging is the inconsistency of the associated training programs with most curated for the Science, Technology, Engineering, and Mathematics (STEM) disciplines. Nonetheless, the interdisciplinary yet agnostic nature of data means that there is opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog

An Empirical Investigation of Big Data Analytics: The Financial Performance of Users versus Vendors

In the age of digitisation and globalisation, businesses have shifted online and are investing in big data analytics (BDA) to respond to changing market conditions and sustain their performance. Our study shifts the focus from the adoption of BDA to the impact of BDA on financial performance. We explore the financial performance of both BDA-vendors (business-to-business) and BDA-clients (business-to-customer). We distinguish between the five BDA-technologies (big-data-as-a-service (BDaaS), descriptive, diagnostic, predictive, and prescriptive analytics) and discuss them individually. Further, we use four perspectives (internal business process, learning and growth, customer, and finance) and discuss the significance of how each of the five BDA-technologies affect the performance measures of these four perspectives. We also present the analysis of employee engagement, average turnover, average net income, and average net assets for BDA-clients and BDA-vendors. Our study also explores the effect of the COVID-19 pandemic on business continuity for both BDA-vendors and BDA-clients

The research on customer structure characteristics and marketing measures of regional bank agency: a case from the Agricultural Bank of China

With intensified opening degree and increasingly fierce market competition of commercial banks, commercial banks innovate their products constantly and improve their service quality at the same time. The Agricultural Bank of China (ABC) is a state-owned commercial bank that has built branches in all county-level districts. Instead, branches of ABC in county-level have become the weakest links that reduce ABC’s competitive power. If the flaws in customer and market maintenance in the county-level branches are ever to be repaired, in my opinion, meeting customer perceived service quality and customer demands efficiently based on understanding of customer needs should be put in the first priority currently. Firstly, this part studies the customer segmentation of ** branch of Agricultural Bank of China. This thesis puts forward approach to segment bank customers based on the improved k-means clustering. The results show that the improvement algorithm effectively overcomes the defect that traditional k-means algorithm easily falls into local optimal value, increasing the accuracy of customer classification, and contributing to more reasonable clustering results. Secondly, this thesis uses the econometric panel data model to study the relationship between customer structure and bank performance. The results indicate that a good customer structure can bring benefits for banks and improve their competitiveness. Thirdly, this part analyzes different service quality requirements of different types customer in the ** branch of Agricultural Bank of China. We combine service quality evaluation theory and the background of Chinese commercial banks, establishing the SERVQUAL model for the ** branch. The Study has shown that the correlation coefficient between overall perception of service quality and customer satisfaction is positive; the overall perception of service quality and customer willingness to recommend are also positively correlated, but the degree of correlation is lower than the correlation between the overall perception of service quality and customer satisfaction; the correlation of overall perceived quality of service for all samples and willingness to accept the services of other banks correlation was not significant. At the same time, there is still a gap between the customer perceived service quality and customer expectation in the ** branch of Agricultural Bank of China. Finally, according to the results of customer structure classification and service quality survey of the ** branch of Agricultural Bank of China, the marketing strategies for different customer groups are proposed.Com o crescente grau de comercialização da indústria bancária chinesa e a entrada continua de bancos estrangeiros, a competição entre bancos está a tornar-se cada vez mais feroz, e as estratégias dos bancos comerciais com vista a ganhar vantagens competitivas muda gradualmente. Para além do lançamento de uma variedade de produtos financeiros, os bancos comerciais utilizam serviços diferenciados para poder dar resposta á procura do mercado diversificado de consumidores. Estes bancos estão igualmente a começar a entender que para os bancos gradualmente convergirem devem não só atingir uma vantagem competitiva através da oferta de produtos financeiros bem como serviços diferenciados de alta qualidade. Este meio tornou-se na única forma forma que o banco dispõe para poder vencer a sua competição. Portanto, para os bancos comerciais, estamos num período de inovação onde o aumento da qualidade de serviço é inevitável. O Agricultural Bank of China é um banco comercial do estado que possui uma filial em todas as regiões administrativas a nível de condado. Ligações e serviços, citadinos e urbanos tem sido a maior vantagem do Agricultural Bank of China, mas a situação actual não é favorável. A filial a nível de condado tem-se tornado na ligação pior e mais fraca da fundação deste banco. Ao mesmo tempo, bancos privados têm emergido em paridade com o rápido desenvolvimento dos instrumentos financeiros online e, o Agricultural Bank of China, como o representante dos bancos tradicionais está a enfrentar competição feroz. Em especial desvantagem no que toca a recursos ao consumidor e instrumentos online que os outros bancos oferecem. Os bancos comerciais tradicionais, estão desta forma confrontados com a perda de clientes bem como o elevado custo de adquirir novos clientes. O risco operacional do banco aumenta á medida que a estabilidade do mercado consumidor piora. Se querem mudar o status quo das filiais a nível de condado, necessitam entender a actual necessidade da qualidade de serviço ao cliente, analisar as características da procura do consumidor e estabelecer um mecanismo de ciclo virtuoso de mercado-consumidor-beneficio - são as maiores prioridades agora. Baseado nisto, este estudo usará marketing, processo de decisão da gerência, teoria e métodos, mineração de dados, técnicas estatísticas e métodos econométricos para analisar as características de procura do consumidor do Agricultural Bank of China. Primeiro, utilizar a análise de cluster de mineração de dados para efectuar uma estratificação analítica do grupo de consumidores do banco para manter a estrutura da procura dos consumidores e serviços; classificação da informação de procura dos consumidores, acesso ás tendências de procura dos consumidores do banco e tendência de produtos competitivos; na base de quantificar os requerimentos do consumidor, usamos o painel de dados econométricos para efectuar uma análise empírica sobre a estrutura de procura dos consumidores e a performance do Agricultural Bank of China

Managing buyer experience in a buyer–supplier relationship in MSMEs and SMEs

Monitoring buyer experience provides competitive advantages for suppliers as buyers explore the market before reaching a salesperson. Still, not many B2B suppliers monitor their buyers’ expectations throughout their procurement journey, especially in MSMEs and SMEs. In addition, the inductive research on evaluating buyer experience in buyer–supplier relationships is minimal, leaving an unexplored research area. This study explores antecedents of buyer experience during the buyer–supplier relationship in MSMEs and SMEs. Further, we investigate the nature of the influence of extracted precursors on the buyer experience. Firstly, we obtain the possible antecedents from the literature on buyer–supplier experience and supplier selection criteria. We also establish hypotheses based on transaction cost theory, resource-based view (RBV), and information processing view. Secondly, we employ an investigation based on the social media analytics-based approach to uncover the antecedents of buyer experience and their nature of influence on MSMEs and SME suppliers. We found that buyer experience is influenced by sustainable orientation, management capabilities (such as crisis management and process innovation), and suppliers’ technology capabilities (digital readiness, big data analytical capability)

Journal of Telecommunications and Information Technology, 2002, nr 3

kwartalni

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters