Customer churn prediction for web browsers

In the competitive web browser market, identifying potential churners is critical to decreasing the loss of existing customers. Churn prediction based on customer behaviors plays a vital role in customer retention strategies. However, traditional churn prediction algorithms such as Tree-based models cannot exploit the temporal characteristics of browser customers behaviors, while sequence models cannot explicitly extract the information between multiple behaviors. To meet this challenge, we propose a novel model named Multivariate Behavior Sequence Transformer (MBST) with two complementary attention mechanisms to explore the temporal and behavioral information separately. Furthermore, a Tree-based classifier is attached for churn prediction instead of using the multilayer perceptron. Extensive experiments on a real-world Tencent QQ browser dataset with over 600,000 samples demonstrate that the proposed MBST achieves the F-score of 82.72% and the Area Under Curve (AUC) of 93.75%, which significantly outperforms state-of-the-art methods in terms of churn prediction

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

Antecedents of ESG-Related Corporate Misconduct: Theoretical Considerations and Machine Learning Applications

The core objective of this cumulative dissertation is to generate new insights in the occurrence and prediction of unethical firm behavior disclosure. The first two papers investigate predictors and antecedents of (severe) unethical firm behavior disclosure. The third paper addresses frequently occurring methodological issues when applying machine learning approaches within marketing research. Hence, the three papers of this dissertation contribute to two recent topics within the field of marketing: First, marketing research has already focused intensively on the consequences of corporate misconduct and the accompanying media coverage. Meanwhile, the prediction and the process of occurrence of such threatening events have been examined only sporadically so far. Second, companies and researchers are increasingly implementing machine learning as a methodology to solve marketing-specific tasks. In this context, the users of machine learning methods often face methodological challenges, for which this dissertation reviews possible solutions. Specifically, in study 1, machine learning algorithms are used to predict the future occurrence of severe threatening news coverage of corporate misconduct. Study 2 identifies relationships between the specific competitive situation of a company within its industry and unethical firm behavior disclosure. Study 3 addresses machine learning-based issues for marketing researchers and presents possible solutions by reviewing the computer science literature

Telecommunication data monetization

The aim of the study was to find out what kind of telecommunication data monetization models are interesting and potential. The focus was on finding out what kind of business model trends there are already, how telco data can be collected and monetized, how mature telco data monetization is and how telco data monetization can be advanced by adopting already existing models or creating innovative ways to do business from scratch. Empirical part consisted of theme interviews and workshops on the topics. The study indicates that internal telco data monetization is quite mature and it has been developed for a long time but many of the external telco data monetization projects are in piloting and testing phase. Telecommunication data monetization is quite similar with other data monetization processes, so already existing effective and profitable models can be adopted and clear need for creating totally new business models was not found. Location telco data based insight was seen as the most valuable way to do external monetization while also IoT and sensor telco data as a value were seen potential in the future. In telco data monetization projects, one of the biggest key activities is to fulfill data privacy regulations and still keep the business profitable

Data Analysis Methods for Software Systems

Using statistics, econometrics, machine learning, and functional data analysis methods, we evaluate the consequences of the lockdown during the COVID-19 pandemics for wage inequality and unemployment. We deduce that these two indicators mostly reacted to the first lockdown from March till June 2020. Also, analysing wage inequality, we conduct analysis separately for males and females and different age groups.We noticed that young females were affected mostly by the lockdown.Nevertheless, all the groups reacted to the lockdown at some level

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Determinants of customer loyalty in Pakistan's Telecom sector: An exmination of differences between stayers and switchers

Customer loyalty has gained foremost attention among the practitioners and the academic researchers because of its proximity to organizational growth, profit and survival. Based on existing literature, this study has highlighted a few critical issues related to the telecommunication sector in Pakistan such as the decline in subscriber growth, decrease in average revenue, and the increasing trend of users switching among the telecom operators. The objective of the present study was to analyze the impact of perceived service quality, price fairness, justice to service and relational bonds on customer loyalty with the mediating role of customer satisfaction and the moderating role of corporate image. This study also investigated the perceptions of stayer- and switcher- users on the determinants of customer loyalty. The framework of the present study was based on the Oliver Four Stage Model, the Expectancy Confirmation Model and the Principles of Reciprocity. Data for the current study was collected from 539 prepaid subscribers based in four major capital cities of Pakistan through questionnaires by adopting the proportionate stratified random sampling. The collected data was analyzed by using SPSS version 23 and the Smart PLS Structure Equation Modeling (PLS-SEM). The findings of the study revealed that perceived service quality and customer satisfaction are the main driving forces to customer loyalty. Moreover, customer satisfaction successfully mediates perceived service quality, price fairness, justice to service recovery and relational bonds. However, corporate image does not moderate the relationship between customer satisfaction and customer loyalty. Meanwhile, perceived service quality and relational bonds to loyalty relationship are important to the stayer- users, while price fairness and justice to service recovery are important to switchers. The present study has also suggested some theoretical and practical contributions