Churn Rate Prediction in Telecommunications Companies

Customer churn is a central concern for companies operating in industries with low switching costs. Among all industries, the one that suffers most from this problem is the telecommunications sector, with an annual churn rate of approximately 30%. As operators grow, so does the volume of data, and understanding and interpreting this data is necessary for operators to understand why customer churn is happening. Through data science, machine learning, and artificial intelligence techniques, the possibilities of predicting customer churn have increased significantly. In this research, the proposed methodology consists of six phases. In its first phases, data preprocessing and feature analysis are performed. In the third phase, feature selection is performed. Then, the data were divided into two parts of training and testing, in the proportion of 80% and 20%, respectively. For the prediction process, the most popular prediction models were applied, i.e. logistic regression, vector machine, naive bays, random forest, decision trees, etc. In the training set, boosting and ensemble techniques were applied to achieve better model accuracy. In the training set, K­fold cross­validation was used to avoid overlapping models. The results are evaluated using the confusion matrix and the AUC curve. The Adaboost, Catboost and XGBoost classifiers obtained the highest accuracy in the range of 85% and 92%. The highest AUC score was 98% obtained by Random Forest and 93% XGBoost which outperformed the other models.O setor de telecomunicações é visto atualmente como um dos setores que mais cresce no mundo, com um desenvolvimento exponencial nos últimos anos afetando cerca de 90% da população em geral [BGM+20a]. Este crescimento tem sido alimentado pelos recentes avanços tecnológicos e novos serviços de telecomunicações, implicando diretamente no aumento dos dados que se tornaram um ativo de primeira classe para empresas, corporações e organizações. Apesar do vasto número de clientes, existem múltiplas empresas operando neste mercado oferecendo serviços similares a uma gama restrita de preços. Este fator junto com os custos reduzidos de mudança entre empresas justifica porque o setor de telecomunicações é um mercado tão competitivo, onde a rotatividade de clientes é uma preocupação central para as receitas das empresas. Contudo a taxa de churn pode ser visto como termômetro para a saúde da empresa. Uma forte concorrência entre empresas rivais e tarifas competitivas de múltiplos fornecedores são as principais razões para os clientes mudarem entre as operadoras de telecomunicações. Entretanto, outros fatores podem levar os clientes à rotatividade, tais como o aumento dos valores dos planos, atendimento deficiente ao cliente, tempos de conexão lentos, e­mails de marketing indesejados, e outros. Com base nestes fatores, a chave para mitigar este problema é prever os clientes que estão em risco de churn, ou em outras palavras, rotatividade. Ultimamente, muitos pesquisadores estão interessados em trabalhar várias técnicas para prever a rotatividade dos clientes de telecomunicações. A indústria de telecomunicações tem lutado com a ameaça de perder mais de 25% de seus clientes a cada ano, o que se acredita resultar em uma enorme perda de receita. Outro fator relevante é que adquirir um novo cliente custa entre 5 e 10 vezes mais do que manter um cliente com a empresa. Com base nisto, é essencial manter os assinantes existentes ou evitar a rotatividade dos clientes [MTMM13]. De acordo com Kortler, a redução da taxa de rotatividade em 5% aumenta o lucro de 25% para 85% para as empresas de [K +97]. Assim, tem havido uma demanda crescente para automatizar os processos utilizados e identificar a rotatividade dos clientes. Entretanto, este processo é tão caro que normalmente apenas 15% da receita obtida pelas empresas móveis é gasta em infra­estrutura de rede e TI, enquanto 15 a 20% da receita é usada na aquisição de clientes. Os modelos de rotatividade de clientes visam identificar os primeiros sinais de rotatividade e tentar prever os clientes que saem voluntariamente. Portanto, muitas empresas percebem que seus sistemas de banco de dados existentes são um de seus ativos mais valiosos e, de acordo com Abbasdimehr, [AST11] os dados internos que as empresas têm sobre seus clientes são uma ferramenta útil para prever clientes em risco. O problema é caracterizado da seguinte forma churn é calculado dividindo o número total de clientes pelo número total de clientes ativos em um determinado período. A rotatividade de clientes pode ser gerenciada de forma reativa ou pró­ativa. Na abordagem reativa, a empresa espera o pedido de cancelamento do cliente e depois oferece planos de retenção atraentes. Na abordagem pró­ativa, a probabilidade de rotatividade é prevista de acordo com os planos oferecidos aos clientes [Pen09]. No segundo caso, as abordagens baseadas no aprendizado de máquinas provaram ser altamente eficientes na estimativa da probabilidade de rotatividade do cliente[UI16, VDSC15, AJA19]. Alguns algoritmos usados nestas estratégias são regressão linear, SVM, árvores de decisão, floresta aleatória, e Naive Bayes. Ao construir uma estratégia baseada na aprendizagem da máquina, a análise e processamento de dados desempenha um papel significativo na melhoria da precisão da classificação. Muitas abordagens foram desenvolvidas por pesquisadores a fim de selecionar características que são úteis na redução da dimensionalidade dos dados, complexidade computacional e sobreajustes. Na previsão do churn, as características com maior grau de importância são extraídas do vetor de entrada, pois são úteis para prever os clientes que deixarão a empresa. A fim de resolver o problema acima, as seguintes técnicas de aprendizagem de máquina foram utilizadas neste trabalho: (1) Regressão logística, (2) Naive Bayes, (3) máquina vetorial de suporte, (4) Classificador floresta aleatória, (5) Decision Tree, (6) KNN, (7) e algoritmos de gradient boosting tais como AdaBoost, XGBoost, LGBM Classifier e CatBoost. O objetivo é fazer uma análise comparativa entre estes algoritmos para prever vários padrões de rotatividade dos clientes. Além disso, para uma melhor compreensão do conjunto de dados, os dados foram pré­processados para encontrar insights importantes e vetores de características. Depois de implementados os modelos são testados em mais dois datasets que servem como uma forma de avaliar melhor seu desempenho em dados desconhecidos

The Role of Knowledge Economy in African Business

This paper assesses the role of knowledge economy (KE) in African business in 53 countries for the period 1996-2010. The four KE components of the World Bank are employed, notably: education, innovation, economic incentives & institutional regime and information & communication technology. The business indicators are classified into: starting, doing and ending business. Principal components analysis and panel instrumental variable fixed effect approaches are employed as empirical strategies. The findings which are broadly consistent with intuition and the predictions of economic theory suggest that KE policies will substantially boost the starting and doing of business in Africa. This is relevant in fighting unemployment and improving African competitiveness in global value chains. Policy implications for the relevance of each specific KE dimension in African business are discussed with particular emphasis on the theoretical underpinnings of the study. The investigation is original in its contribution at the same time to the scarce literature on African KE and the growing challenges of improving the business climate of the continent by means of KE

The impact of information systems and non-financial information on company success

This study aims to develop and evaluate a model that seeks to measure the impact of Accounting Information System Quality, Internal Control System Quality and Non-Financial Information Quality on company success (Decision-Making Success and Non-Financial Performance). This model is empirically tested with data obtained from the managers of 381 Portuguese companies. We use structural equation modelling in the analysis of causal relationships between different constructs. The results show that information and control systems quality (accounting and internal control) have a direct impact on Non-Financial Information Quality and an indirect impact on Decision-Making Success. The results also indicate that Quality Non-Financial Information does not contribute directly to Non-Financial Performance but contributes indirectly via DecisionMaking Success. The exploratory variables prove to be crucial for the companies’ Non-Financial Performance, accounting for its 62% variance. Previous research focuses primarily on financial information quality and financial performance. This study is the first to empirically prove that information and control systems contribute favourably to the transparency and value-relevance of non-financial information and, consequently, to business success.info:eu-repo/semantics/publishedVersio

IP pledges, open IP or patent pools? Developing taxonomies in the thicket of terminologies

Recently, a variety of organisations, including car and consumer electronics manufacturers, have applied so called IP Pledges. They facilitate access to Intellectual Property Rights (IPRs) for a large group of third parties through a reasonable price or, in some cases, even entirely free of charge. Despite growing research to better understand IP Pledges, its underlying terminology remains contradictory. With this paper we contribute to building an established ontology of IP Pledges through proposing a definition and a taxonomy to distinguish different types of IP Pledges. Applying an inductive research approach, we analyse 59 pledges through a secondary data analysis. Based on the analysis, we propose a threedimensional framework that can be used to distinguish eight types of IP Pledges. Extending this framework with examples from the literature we then propose an IP licensing taxonomy that can be used to distinguish IP licensing-strategies, including patent pools, cross-licenses, and trade secrets. Finally, we use this IP Licensing Framework to illustrate the paths IP owners take as they change their licensing strategies over time

Management of innovative development the economic entities: collective monograph

The authors of the book have come to the conclusion that it is necessary to effectively use modern approaches the management of innovative development the economic entities in order to increase the efficiency of activity, to ensure competitiveness, to intensify innovation activity. Basic research focuses on assessing the competition of economic entities, internal control in organizations, analysis of credit risk, diagnostics of sources of funding for innovation, assessment of social innovation and human development factors. The research results have been implemented in the different models of reengineering business process, development of alternative agriculture, the digital economy, knowledge management. The results of the study can be used in decision-making at the level the economic entities in different areas of activity and organizational-legal forms of ownership, ministries and departments that promote of development the economic entities on an innovative basis. The results can also be used by students and young scientists in modern concepts and mechanisms for management of innovative development the economic entities in the context of efficient use the resource potential and improvement of innovation policy

RegTech and Predictive Lawmaking: Closing the RegLag Between Prospective Regulated Activity and Regulation

Regulation chronically suffers significant delay starting at the detectable initiation of a “regulable activity” and culminating at effective regulatory response. Regulator reaction is impeded by various obstacles: (i) confusion in optimal level, form and choice of regulatory agency, (ii) political resistance to creating new regulatory agencies, (iii) lack of statutory authorization to address particular novel problems, (iv) jurisdictional competition among regulators, (v) Congressional disinclination to regulate given political conditions, and (vi) a lack of expertise, both substantive and procedural, to deploy successful counter-measures. Delay is rooted in several stubborn institutions, including libertarian ideals permeating both the U.S. legal system and the polity, constitutional constraints on exercise of governmental powers, chronic resource constraints including underfunding, and agency technical incapacities. Therefore, regulatory prospecting to identify regulable activity often lags the suspicion of future regulable activity or its first discernable appearance. This Article develops the regulatory lag theory (RegLag), argues that regulatory technologies (RegTech), including those from the blockchain technology space, can help narrow the RegLag gap, and proposes programs to improve regulatory agency clairvoyance to more aggressively adapt to changing regulable activities, such as by using promising anticipatory approaches

Telecommunication Economics

This book constitutes a collaborative and selected documentation of the scientific outcome of the European COST Action IS0605 Econ@Tel "A Telecommunications Economics COST Network" which run from October 2007 to October 2011. Involving experts from around 20 European countries, the goal of Econ@Tel was to develop a strategic research and training network among key people and organizations in order to enhance Europe's competence in the field of telecommunications economics. Reflecting the organization of the COST Action IS0605 Econ@Tel in working groups the following four major research areas are addressed: - evolution and regulation of communication ecosystems; - social and policy implications of communication technologies; - economics and governance of future networks; - future networks management architectures and mechanisms

Selecting the best model for predicting a term deposit product take-up in banking

In this study, we use data mining techniques to build predictive models on data collected by a Portuguese bank through a term savings product campaign conducted between May 2008 and November 2010. This data is imbalanced, given an observed take-up rate of 11.27%. Ling et al. (1998) indicated that predictive models built on imbalanced data tend to yield low sensitivity and high specificity, an indication of low true positive and high true negative rates. Our study confirms this finding. We, therefore, use three sampling techniques, namely, under-sampling, oversampling and Synthetic Minority Over-sampling Technique, to balance the data, this results in three additional datasets to use for modelling. We build the following predictive models: random forest, multivariate adaptive regression splines, neural network and support vector machine on the datasets and we compare the models against each other for their ability to identify customers that are likely to take-up a term savings product. As part of the model building process, we investigate parameter permutations related to each modelling technique to tune the models, we find that this assists in building robust models. We assess our models for predictive performance through the use of the receiver operating characteristic curve, confusion matrix, GINI, kappa, sensitivity, specificity, and lift and gains charts. A multivariate adaptive regression splines model built on over-sampled data is found to be the best model for predicting term savings product takeup

A German Digital Grand Strategy: Integrating Digital Technology, Economic Competitiveness, and National Security in Times of Geopolitical Change

This report systematically outlines the state of play in digital policy and Berlin's current policy approach. It provides 48 recommendations for strengthening Germany's efforts to build a confident, high-performing European digital economy embedded in an open, democratic, and rules-based digital order