Prediction of Customers Churn in Telecommunication Industry

In the developed world, mobile markets have reached saturation on subscriber penetration and connections growth. The challenge for operators has evolved from attracting new customers to retaining existing ones. Various components have an impact on churn. Therefore, it is very important to understand the behaviour of the customers, encourage them in spending more and then predicting the future by preventing their attrition. As the industry is evolving, the biggest challenge for operators is to engage with consumers and retain their loyalty by delivering more competitive and innovative value-added services. While understanding consumer needs remains essential to improve customer retention, other emerging tariffs and services are likely to carry a long-term impact on churn (including national, international and roaming bundles tariffs and mobile services). The churn might be voluntary in cases they want to leave the network they actually are using, or involuntary churn in case of unpaid bills. The methodology used to do the right evaluations in order to achieve strong results in this field is very large and varied. The scope of this thesis is to identify and analyse different appropriate models that can help the data analysts to find the churners in Telecommunication industry. In this thesis we are going to discuss on two important topics in telecommunication markets and their respective predictive models, which tend to understand the customer behaviour towards different competitors: market share in telecommunication industry and customer churn

Agent based modelling and simulation: An examination of customer retention in the UK mobile market

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Customer retention is an important issue for any business, especially in mature markets such as the UK mobile market where new customers can only be acquired from competitors. Different methods and techniques have been used to investigate customer retention including statistical methods and data mining. However, due to the increasing complexity of the mobile market, the effectiveness of these techniques is questionable. This study proposes Agent-Based Modelling and Simulation (ABMS) as a novel approach to investigate customer retention. ABMS is an emerging means of simulating behaviour and examining behavioural consequences. In outline, agents represent customers and agent relationships represent processes of agent interaction. This study follows the design science paradigm to build and evaluate a generic, reusable, agent-based (CubSim) model to examine the factors affecting customer retention based on data extracted from a UK mobile operator. Based on these data, two data mining models are built to gain a better understanding of the problem domain and to identify the main limitations of data mining. This is followed by two interrelated development cycles: (1) Build the CubSim model, starting with modelling customer interaction with the market, including interaction with the service provider and other competing operators in the market; and (2) Extend the CubSim model by incorporating interaction among customers. The key contribution of this study lies in using ABMS to identify and model the key factors that affect customer retention simultaneously and jointly. In this manner, the CubSim model is better suited to account for the dynamics of customer churn behaviour in the UK mobile market than all other existing models. Another important contribution of this study is that it provides an empirical, actionable insight on customer retention. In particular, and most interestingly, the experimental results show that applying a mixed customer retention strategy targeting both high value customers and customers with a large personal network outperforms the traditional customer retention strategies, which focuses only on the customer‘s value.This work is funded by the Brunel Department of Information Systems and Computing (DISC

Business intelligence for sustainable competitive advantage: the case of telecommunications companies in Malaysia

The concept of Business Intelligence (BI) as an essential competitive tool has been widely emphasized in the strategic management literature. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well explained. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage.Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.This research uses combination of theoretical foundation of resource-based theory and diffusion of innovation theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. Qualitative field study then is carried out to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. A survey is carried out with sample of business analysts and decision makers in telecommunications firms and is analyzed by Partial Least Square-based Structural Equation Modeling.The findings revealed that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management will have better chance in realizing their dreams of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility and observability are also significant in ensuring BI success. It thus implied that the executives’ positive perceptions towards BI initiatives are deemed necessary. Moreover, the most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social and environmental issues.The BI model well explained how BI was deployed in Malaysian telecommunications companies. This study thus contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts

Connecting the dots: uncovering the technology scouting process

This dissertation analyses the challenges of internal and external knowledge sourcing processes in large multinational corporations. I identify the cognitive and behavioural mechanisms emerging in the initiation of a knowledge transfer— i.e. the initial stage of recognising opportunities for knowledge transfer and acting on these opportunities—and I examine how they impact actual patterns of intra- and inter-firm knowledge flows. The gist of this dissertation consists of two studies: an in-depth qualitative study and a quantitative study both set in the telecommunications service providers industry. The qualitative paper sheds new light on the process that specialised units in large multinational corporations (viz. technology scouting units) use to recognise and act on opportunities to transfer external knowledge. While the qualitative study uncovers the technology scouting process, the quantitative paper focuses on the outcomes of this process. More precisely, I examine how much certain knowledge properties that emerged from the qualitative study (e.g. knowledge “proven-ness” and knowledge dissonance) explain why do firms (fail to) act on opportunities to transfer external technologies. The data collection effort for those studies has extended over two years, involved more than 50 semi-structured interviews with managers in Silicon Valley, Europe and Asia and access to a proprietary database containing detailed information on 137 technologies assessed by the scouting units of a large European telecommunication services provider between January 2003 and December 2005

Deep Neural Networks and Tabular Data: Inference, Generation, and Explainability

Over the last decade, deep neural networks have enabled remarkable technological advancements, potentially transforming a wide range of aspects of our lives in the future. It is becoming increasingly common for deep-learning models to be used in a variety of situations in the modern life, ranging from search and recommendations to financial and healthcare solutions, and the number of applications utilizing deep neural networks is still on the rise. However, a lot of recent research efforts in deep learning have focused primarily on neural networks and domains in which they excel. This includes computer vision, audio processing, and natural language processing. It is a general tendency for data in these areas to be homogeneous, whereas heterogeneous tabular datasets have received relatively scant attention despite the fact that they are extremely prevalent. In fact, more than half of the datasets on the Google dataset platform are structured and can be represented in a tabular form. The first aim of this study is to provide a thoughtful and comprehensive analysis of deep neural networks' application to modeling and generating tabular data. Apart from that, an open-source performance benchmark on tabular data is presented, where we thoroughly compare over twenty machine and deep learning models on heterogeneous tabular datasets. The second contribution relates to synthetic tabular data generation. Inspired by their success in other homogeneous data modalities, deep generative models such as variational autoencoders and generative adversarial networks are also commonly applied for tabular data generation. However, the use of Transformer-based large language models (which are also generative) for tabular data generation have been received scant research attention. Our contribution to this literature consists of the development of a novel method for generating tabular data based on this family of autoregressive generative models that, on multiple challenging benchmarks, outperformed the current state-of-the-art methods for tabular data generation. Another crucial aspect for a deep-learning data system is that it needs to be reliable and trustworthy to gain broader acceptance in practice, especially in life-critical fields. One of the possible ways to bring trust into a data-driven system is to use explainable machine-learning methods. In spite of this, the current explanation methods often fail to provide robust explanations due to their high sensitivity to the hyperparameter selection or even changes of the random seed. Furthermore, most of these methods are based on feature-wise importance, ignoring the crucial relationship between variables in a sample. The third aim of this work is to address both of these issues by offering more robust and stable explanations, as well as taking into account the relationships between variables using a graph structure. In summary, this thesis made a significant contribution that touched many areas related to deep neural networks and heterogeneous tabular data as well as the usage of explainable machine learning methods

A systems thinking approach to the planning of rural telecommunications infrastructure.

Thesis (Ph.D.)-University of Natal, Pietermaritzburg, 2001.The research reported in this thesis is concerned with the provision of telecommunications infrastructure to rural areas in developing countries. The primary focus is to improve the current practice in the planning of such infrastructure. An in depth analysis of the critical issues that characterise rural telecommunications in developing countries revealed that the rural telecommunications system is not just a technological system but a complex system of people and technology interdependent on other systems/subsystems. A systems approach lead to a conceptual model of The Rural Telecommunications System as an open complex sociotechnical system. Consequently the planning of rural telecommunications infrastructure requires an approach that addresses such complexity. Critical systems thinking was chosen as the overall systems thinking approach for the development of a systemic planning framework for rural telecommunications infrastructure, that accommodates the system of problems inherent in the complex sociotechnical rural telecommunications system. The framework was built on the principles of Multimethodology and consists of Interactive Planning as a general orientation, mixed with Interpretive Structural Modelling and Critical Systems Heuristics. The framework is enhanced by the inclusion of current techniques from Systems Engineering practice, and softer techniques such as rich pictures. A case study based on the Mapumulo rural area in KwaZulu Natal was used for the practical validation of the framework

Mobile remittances as sociotechnical networks : an Actor-Network Theory case study

The use of formal remittance channels between developing countries is highly variable. In particular, take-up of digital retail payment channels to facilitate remittance formalisation is often sparse and subject to resistance. Through the case of an emergent mobile phone-based international money remittance channel, this study sought to understand how a service provider utilises legitimation strategies to create a stable channel (network) and enhance user uptake. Prior studies, mainly influenced by a cognitive tradition, tend to explain uptake of mobile payments, mobile banking and mobile money services either through features of the technology such as interface design or through individual motivational structures. There is a paucity of research on how such Information Communication Technology innovations are created and deployed in developing countries’ settings, which may shed light on why they may result in failure or success. Research that traces core processes involved in the formation of such emergent and complex sociotechnical networks including identifying primary actors and their relationships is sorely lacking. Thus, assuming Actor-Network as a theoretical base, this study sought to theorise about how a mobile remittance network was created along the South-Africa-to-Zimbabwe corridor; identify actors and their interests in that network; trace what associations exist in that network; how relationships evolve; how actors are enrolled in the network; how parts of the network form a whole network; and, how the network attained temporary stability or, conversely, why the network may be unstable. Interestingly, ANT assumes context to be emergent and that actors and their relations is all that is needed to understand phenomena. As a consequence of this radical ontological stance, the lens is criticised for overly focusing on micro- (individual) level actor interactions and neglecting the existence of context thus under-exploring how broader social structures, their role and interests influence local action and the stability of sociotechnical networks. Challenging this assumption emerged as an area of potential theoretical contribution. My contribution to theory was to demonstrate that sociotechnical network creation takes more than human and technological actors and their actions, as the ANT assumes. I argued for a pragmatic application of the ANT. This entails taking institutions seriously. I augmented ANT with institutional legitimation strategies from the Legitimacy Perspective to foreground the influence of social structures. I argue that social structures are active non-human actors in which interests have been inscribed that should not be obscured or downplayed. At the macro-level of analysis, the analysis reveals that a hybrid of argumentation, manipulation, selection and adaptation strategies helped to account for the concealed but important social, cultural, political and historical actors that facilitate or constrain the four stages of translation (Problematisation, Interessement, Enrolment and Mobilisation) during the network building process to achieve desired stability. My thesis demonstrates that if the focal actor (service provider) finds a way of communicating (such as using symbolic management) with heterogeneous actors (often with contradictory interests) that resonates with the target potential allies’ norms, values and standards, enrolment and stability of a sociotechnical network may be facilitated and enhanced. Working in combination, the findings of ANT and the Legitimacy Perspective offered some rich perspectives that deepen our current understanding of sociotechnical network. The study highlights that sociotechnical networks are also products of institutional settings in which they are immersed hence the need to foreground the highly contextualised character of network creation. Wider context in which sociotechnical networks are created and immersed consist of actors on a higher level of analysis which should be viewed as other parts of the network. In addition to their relational nature, networks are not only emergent but are also historically-shaped. Likewise, the study is also significant in that it brings to the fore the significance of politics and power at both the micro and macro-levels of analysis, which provides a basis for practitioners to understand why some sociotechnical networks stabilise (i.e. are eventually employed as remittance channels) while others fail to scale-up. I envisage this case study to provoke debate about the size of the opportunity for international remittance service providers and how far they should go to seize it using emergent digital mechanisms.Thesis (PhD)--University of Pretoria, 2019.Gordon Institute of Business Science (GIBS)PhDUnrestricte