mobile telecommunication

mobile telecommunication

 

Rephrasing the attached work

Introduction Background Over the last two decades, we have seen mobile telecommunication become the dominant medium of communication. In many countries, especially developed ones, the market has reached a degree of saturation, so that each new customer must be won over from the competitors. At the same time, public regulations and the standardisation of mobile communication now allow customers to easily move from one carrier to another, resulting in a very fluid market. The cost of winning a new customer is far greater than the cost of preserving an existing one (Hadden et al, 2007) To compete with other providers, a telecommunication company needs to have very good knowledge of its own customers, understanding what each of them wants and needs (MacDonald and Dunbar, 1998). Customer churn is a grievous issue for the telecom industry, sometimes leading to a domino effect. It only takes one highly influential customer to switch to another network provider before large numbers of customers follow in their wake, if they do not find what they are looking for. They certainly want competitive pricing, value for money and above all, high quality service. Customer churning is directly related to customer satisfaction. It is a known fact that the cost of customer acquisition is far greater than the cost of customer retention; that makes retention a crucial business prototype. In fact, for most companies, the customer acquisition cost is higher than the cost of retaining an existing customer, sometimes as much as fifteen times more expensive (Gillen, 2005). Therefore, the goal of a successful churn project is to increase customer loyalty and, consequently, increase company revenue. The biggest challenge face in this area with massive data lies in interpreting business patterns in order to develop models. Since it is still an evolving area, it is difficult, for example, even to thoroughly define a problem. A large amount of data is produced from structured, semi-structured and unstructured sources, which makes it very difficult to manage information about a customer’s usage. Data is available about service usage over time: information about outgoing and incoming traffic or the number of text or multimedia messages in cellular phones is often available in monthly aggregations (Khatibi et al, 2002). The nature of the data available and consideration of the results suggest that a high degree of customer satisfaction does not always translate into loyalty. There appears to be a gap: while the ability to capture and store vast amounts of data has grown at an unprecedented rate, the technical capacity to aggregate and analyse these disparate volumes of information is only now catching up. Big Data is getting larger and larger day by day, and the data explosion continues. In order to draw conclusions about valuable customer experiences – gathered from large amounts of structured and unstructured data from multiple sources and in different formats – proper structures and tools are required. To obtain the maximum business impact, this process requires the proper combination of people, processes and analytics. To form and improve lasting relationships, platforms that utilise Big Data need to employ more tactical approaches to customer retention, loyalty and relationships. Solutions should be focused not on whether we can prompt the customer to make the next purchase, but on how we can maintain the loyalty of the customer. It is not about the customer’s total number of transactions or how much profit the customer is generating, but about how long the customer will stay, and how to demonstrate the customer’s value to the organisation, so that the customer won’t go to the competitor if the competitor provides a lower best price. The main objective of this research is to identify the role applied Big data analytics in telecom operators which can understanding the most influential elements to building customer loyalty, acquisition and retention by developing close relationships with customers based on a deep understanding of their behavior, needs and expected behavior. the future. The information can be converted into a real-time executable view,Raise the company to respond in real time to behavioral changes in the customer mentality, or respond quickly to threats on the competitive horizon. Rationale of the Study IFor the rationale of the study In the competitive communications industry, public policies and unified mobile communications allow customers to move easily from one carrier to another, leading to a fluidic market. Performance prediction, or the task of identifying customers likely to stop using the service, is an important and profitable concern for the telecommunications sector. The objective of this thesis is to study and analyze customer expectations based on the heterogeneous behavior of lowsatisfaction clients and the impact of external communication (social influence) on quality of service and the perspective of users with the help of Big Data analyzes. Aim of the Study The aim of the study is to critically evaluate the impact of Big Data on the overall performance of the churn rate, by using a ‘tribal’ customer model. This model is appropriate because there are people who have a high degree of influence on others due to their large social networks, and who are well connected to different (online) groups in the telecom sector in Oman. This study seeks to explore the use of the technology by the telecom provider, and the level to which its implementation can prevent customer churn action in real time by enhancing customer satisfaction, and thereby generate additional revenue. Objectives of the Study The churn rate has been a major factor in changing the landscape of many businesses, and this is true as well in the telecom industry. It is an established fact that Big Data can have a direct positive impact on the way telecom providers behave and grow. However, the available records (billing information, etc.) produce a huge amount of data. It is challenging for telecom operators to deal with this surge in data volume. However, this challenge can be transformed into an opportunity by efficiently utilizing Big Data and Big Data analytics. This study investigates the different factors that determine Big Data’s influence on customer loyalty, acquisition and retention within Omantel. It is important to understand how one customer can have an influence on others due to a large social network. Understanding customer behaviour or monitoring customers through sentiment analysis is a major enabler of Big Data. In the end, recommendations will be provided – based on the goals and objectives – to help Omantel to understand the different features of Big Data, providing them with a model suitable for Oman telecom customers. This work will: ● Study the current situation with regard to the role of customer churn in Omantel, including their analytics for building customer loyalty, acquisition and retention. ● Assess the challenges faced by the telecom company with regard to data capture from various sources. ● Finding methods to segment the most profitable customers by using Big Data analyses ● Propose a Big Data platform that can improve the customer experience, enhance customer satisfaction and generate additional revenue. Research Questions RQ1. What role does Big Data play in churn prediction? RQ2. To which extent can Big Data determine the most valuable customers and prevent customer churn? RQ3. Which kind of information is needed for accurate churn prediction? RQ4. How can Big Data be used to identify customer segments who have a high degree of impact on others due to their large social network, and who are well connected to different groups? Scope of the Study Building on a rationale based on past literature regarding the impact of Big Data on the telecom industry, this study examines how Big Data is used to understand customer behaviour in the telecom service provider sector of Oman. This research reviews and identifies how Big Data will help to build customer loyalty, acquisition and retention within Omantel. It will analyse growth levels within Omantel with consideration of the segment customers who influence others due to large social networks and who are well connected, and therefore affect churn among Telecoms customers in Oman. They will also benefit from the study, as their case will be used in developing a solution. It should be noted that the telecom sector is a very rapidly developing one, encompassing telecom service providers. Oman Telecommunications Company (Omantel) is the first telecommunications company in Oman and is the primary provider of internet services in the country, making it a suitable case study. Motivation: In the competitive telecommunications market, customers want competitive pricing, value for their money and above all, a high-quality service. Today’s customers wouldn’t hesitate to switch providers if they don’t find what they are looking for. Consequently, it has become crucial for telecom providers to control churn, the loss of customers switching from one provider to another. Customer churning is directly related to customer satisfaction. Since the cost of winning a new customer is far greater than the cost of retaining an existing one, the mobile carriers have now shifted their focus from customer acquisition to customer retention. It is essential to put in place a sustainable and robust strategy for churn retention to preserve a customer’s lifetime value. One of the metrics used by telecommunication companies to determine their relationship with customers is “churn” . After substantial research in the field of churn prediction, Big Data analytics, using data mining techniques , was found to be an efficient method for identifying churn. These techniques are usually applied to predict customer churn by building models, conducting pattern classification and learning from historical data . However, most of these techniques provide a prediction as to whether customers will or will not churn, but only a few tell us why they churn. The most generally utilised model for anticipating customer churn is the binary classification model. The customers can be grouped into two classes: going to churn, or not. Numerous strategies and calculations are used to solve this problem; for example, grouping trees, neural networks and hereditary algorithms . So far, churn has been mostly studied based on network parameters that are analysed with the data sets. Very few studies have been done about user feelings and ratings in relation to churn; they are based mostly on user data. Also, the published methods and related work have not addressed the end user’s perspective in order to identify a better model to prevent valuable customers from churning. This research uses surveys in order to uncover the truth of the user experience. We want to ask random end users what they think about the quality of the network and calls, and analyse their risk of churning from the present network service provider to another. Also, previous analysis has been based on operators’ user data volume usage. This new analysis of the churn shows us that data based on observations from the users’ perspective gives us better results and prediction models when compared with the operators’ point of view. Social, Legal and Ethical Implications of the Study The study has made no negative impact or produced any social, legal or ethical issues. The study evaluates how the big data can help telecom company to prevent customer churn. There was adequate information given to the five respondents of the study regarding the purpose and need for the research. Clarification was given regarding the use of the research; the study was carried out exclusively for academic purposes and the opinions collected from the participants will not be published or shared anywhere other than with the concerned university. The participants were told about any risks, side effects and discomforts involved in the study and the study was done with the full consent of the participants. Proper preparation was made to protect the participant and his or her contribution to the research. The purpose of the research, prospective research benefits and the need for the research was duly explained to the participants and they were given the freedom to withdraw from the research as well. Respondents in the study gave informed consent and voluntary participation. They were fully informed regarding the procedures and risks involved in the study and their consent to participate in the research was acquired with the intention that no ethical issue should arise from the study. The research did not mean to put the participants in the situation where they might be at risk because of their participation in the study. The principle of anonymity was maintained throughout the study, permitting the participants to openly express feelings and opinions regarding the topics mentioned in the study. The participants were assured of confidentiality, and no breach of confidentiality was made through the study. The research also did not have any moral ramifications as it does not deal with any sensitive areas. The research has demonstrated respect for privacy; this is protected throughout the research in that no personal information like name, age, income or marital status was asked from the participants. This study can be a strong foundation for more research based on the topic. …
Purchase answer to see full attachment

Support