Implications of Mandatory Registration of Mobile Phone Users in Africa

Sub-Saharan Africa ranks among the top regions in terms of growth in the number of mobile phone users. The success of mobile telephony is attributed to the opening of markets for private players and lenient regulatory policy. However, markets may be increasingly saturated and new regulations introduced across Africa could also have a negative impact on future growth. Since 2006, the majority of countries in the region have introduced mandatory registration of users of prepaid SIM cards with their personal identity details. This potentially increases the costs of using mobile telephony. I present a fixed effects model for the estimation of the impact of mandatory registration on mobile penetration growth, which is based upon a panel dataset of 32 countries in Sub-Saharan Africa for the years 2000 to 2010. The results show that the introduction of mandatory registration depresses growth in mobile penetration.Telecommunication, government policy, consumer protection, privacy

Churn prediction methods evaluation and implementation for telecom industry

With the rapid growth in the telecom market, there is an emerging trend to focus on customer retention, which is a critical factor for designing future customer incentive strategies to help a company manage customer relationships. Our main contribution is to build an effective churn prediction system for a telecom company providing real-time communication to predict whether a customer may cease to do business with the company, i.e., stop using the service provided by the company to make phone calls. Due to the dynamic market environment, developing such a system is challenging, as it should not involve frequent retraining processes, leading to a high computational cost. Many different techniques are available to identify customers who are most likely to leave, however, which technique is the most suitable and applicable in practice is not clear because the performance of prediction methods depends heavily on the characteristics of the data. In our thesis, we implemented and evaluated two methods, namely MLP (Multilayer Perceptron) and WTTE-RNN (Weibull Time To Event Recurrent Neural Network), and the model evaluation is based on accuracy and computational cost. We conducted experiments on the real-world dataset containing customer call activity records, experimental results demonstrate that the model performance of MLP is better than the WTTE-RNN, achieving a higher AUC, precision, and Recall. Considering the computational cost, the WTTE-RNN takes more time than the MLP as the WTTE-RNN needs to be retrained, it cannot be directly applied for new data. Furthermore, a detailed feature engineering process was presented in our project, especially how to extract temporal call behavior from raw data. A user-friendly interface was implemented, in order to let users better use our churn prediction system

Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms

Customer churn is a serious problem, which is a critical issue encountered by large businesses and organizations. Due to the direct impact on the company's revenues, particularly in sectors such as the telecommunications as well as the banking, companies are working to promote ways to identify the churn of prospective consumers. Hence it is vital to investigate issues that influence customer churn to yield appropriate measures to diminish churn. The major objective of this work is to advance a model of churn prediction that helps telecom operatives to envisage clients that are most probable to be subjected to churn. The experimental approach for this study uses the machine learning procedures on the telecom churn dataset, using an improved Relief-F feature selection algorithm to pick related features from the huge dataset. To quantify the model's performance, the result of classification uses CART and ANN, the accuracy shows that ANN has a high predictive capacity of 93.88% compared to the 91.60% CART classifie

Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining

An Efficient Hybrid Classifier Model for Customer Churn Prediction

Customer churn prediction is used to retain customers at the highest risk of churn by proactively engaging with them. Many machine learning-based data mining approaches have been previously used to predict client churn. Although, single model classifiers increase the scattering of prediction with a low model performance which degrades reliability of the model. Hence, Bag of learners based Classification is used in which learners with high performance are selected to estimate wrongly and correctly classified instances thereby increasing the robustness of model performance.  Furthermore, loss of interpretability in the model during prediction leads to insufficient prediction accuracy.  Hence, an Associative classifier with Apriori Algorithm is introduced as a booster that integrates classification and association rule mining to build a strong classification model in which frequent items are obtained using Apriori Algorithm. Also, accurate prediction is provided by testing wrongly classified instances from the bagging phase using generated rules in an associative classifier. The proposed models are then simulated in Python platform and the results achieved high accuracy, ROC score, precision, specificity, F-measure, and recall

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

Performance evaluation of different machine learning methods applied on churn database

The growth of data and its storage is becoming more and more important every day. However, occasionally this information is gathered but never used, or perhaps it is improperly gathered, making the extraction of the insides difficult. As a result, while beginning any project, choosing the analysis method is just as crucial as choosing the design of the data collection strategy. Most of the time, we only focus on the analysis of the data and do not consider how it was gathered or whether the fields were actually valuable or just added noise to what we were searching for. For this reason, a trustworthy data set has been chosen for this project. The data came from a telecom company, which, like other modern businesses, collects a lot of data. However, in this case, the data was published on the machine learning web competition Kaggle, where participants competed to build the best model to predict consumer behaviour. One of the key considerations in optimizing any organization's income is preventing customer churn. It happens when customers quit utilizing a company's goods or services, and is also referred to as customer attrition. The main goal of this master's thesis is to analyse a Churn database and categorise the clients in order to determine whether they are likely to leave the company. To do this, two machine learning techniques will be used in the current document. Extreme Gradient Boosting and Random Forest. In order to achieve high performance, the Random Forest (RF) method creates a large number of low-performance models and combines them. In this case, the lower-performance method is called Decision Tree, so it will be explained in more detail in the following document. Similar work is done by eXtreme Gradient Boosting (XGB), although it builds new models based on earlier findings. Both are quite effective predictor models, even with unbalanced data, as will be demonstrated in the next document. This adds another level of complexity that the algorithms must overcome to execute effectively. Different performance indicators will be provided and examined in order to determine which one is the greatest indicator to choose the best model during the process of determining the best model. Sensitivity, Specificity, Precision, F1 Score, and Geometric Mean are a few of the markers that are listed. Additionally, their trends for the various parameter values of the examined models will be shown and analysed. The strong performance of these machine learning algorithms will once more be supported in this thesis. the affirmation of the significance and practical use of these methodologies, as in the case of this project, to comprehend processes and behaviours. All fields can benefit from the information gleaned, and a successful application will undoubtedly yield financial rewards. The two machine learning applied algorithms' default and best models are finally shown, and their advantages and disadvantages will be evaluated while taking into account the many scenarios that exist. This thesis will demonstrate the good performance of both models, with XGB significantly outperforming RF. It will also demonstrate that while XGB performs better on precision and RF has better results on sensitivit

Improved Customer Churn and Retention Decision Management Using Operations Research Approach

The relevance of operations research cannot be overemphasized, as it provides the best possible results in any given circumstance, through analysis of operations and the use of scientific method thus, this paper explore the combination of two operations research models (analytic hierarchy process and Markov chain) for solving subscribers’ churn and retention problem peculiar to most service firms. A conceptual model for unraveling the problem customer churn and retention decision management was proposed and tested with data on third level analysis of AHP for determining appropriate strategies for customer churn and retention in the Nigeria telecommunication industries. A survey was conducted with 408 subscribers; the sample for the study was selected through multi-stage sampling. Two analytical tools were proposed for the analysis of data. These include: Expert Choice/Excel Solver (using Microsoft Excel) and Windows based Quantitative System for Business (WinQSB). This paper plays important role in understanding various strategies for effective churn and retention management and the ranking of churn and retention drivers in order of importance to stakeholders` decision-making. The study provided a framework for understanding the application of AHP and Markov chain for modeling, analysing and proffering solution to problem of churn and retention. The study recommends organizational strategies (corporate, business and functional) that reverse the churn alternatives with high priority and equally strengthen service delivery on high priority retention alternatives in order to ensure firms sustainable competitive advantage. An erratum to this article has been published as https://doi.org/10.5195/emaj.2017.131