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

Churn Prediction in Online Newspaper Subscriptions

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn today's connected world, people turn more to the web to be informed of the news. Newspapers in an online environment profit from employing subscription models. Despite that Portugal remains one of the countries with higher levels of trust in news, readers present a low propensity to subscribe. Hence, online newspapers' existing customers are a valuable asset. Therefore, it is in the best interest of such businesses to monitor these customers to identify potential churn signs down the line. Customer churn prediction models aim to identify customers most prone to attrite, allowing businesses that leverage them to improve their customer retention campaigns' efficiency and reduce costs associated with churn. Two different research approaches, namely prediction power and comprehensibility, have been at the core of churn prediction literature. Businesses need accurate models to target customers' right subset. However, many models are black-boxes and present reduced interpretability. On the other hand, understanding what drives customers to churn can support managers in making better-informed decisions. This project report presents the development of a plan to tackle churn prediction in a Portuguese newspaper with an online subscription model using Machine Learning methods. The models' performance was evaluated in two experiments. One experiment assessed the performance for all types of subscriptions and another considered only non-recurring subscriptions. The results of the first experiment were tempered by an unplanned marketing campaign that run simultaneously with the experiment on top of the contrasting contexts in which the model was trained and evaluated. On the other hand, the second experiment's results suggest that for non-recurring subscriptions, a phone call from the call centre proved to be an adequate retention measure for probable churning subscribers. Additionally, models' predictors were analysed and it was found that users with lower fidelity rates and few subscriptions present a higher propensity to cancel their subscriptions. The same occurs with users whose product is annual or longer-lasting. These findings shed light on how to minimize churn and improve reader engagement. Based on the models' results, and predictors' analysis, the newspaper decided to implement a re-engagement newsletter to keep users engaged and prevent future churn

Systematic Literature Review on Customer Switching Behaviour from Marketing and Data Science Perspectives

This paper systematically examines the literature review in the field of customer switching behavior. Based on the literature review, it can be concluded that customer switching behavior is a topic that has been widely researched, with a focus on various industries, particularly banking and telecommunications. Research trends in this area have shown a positive direction in recent years, and the amount of research being done in marketing and data science is relatively balanced. In marketing, correlational studies are predominant, with a focus on identifying relationships between customer satisfaction, price-related variables, attractiveness of alternatives, service failure, quality, and switching costs to switching behavior. The PPM model is also gaining popularity as an important development for switching behavior because it considers both push and pull factors. Data science research has shown promising results in predicting customer switching behavior, with each research paper achieving good predictive accuracy. However, research gaps spanning the fields of marketing and data science need to be addressed to provide a comprehensive understanding of the drivers of customer switching behavior. Overall, the literature review shows that customer switching behavior is an important concern for businesses, and further research in this area is essential to gain a better understanding of customer behavior and develop effective strategies to retain customers

Applicability of Recurrent Neural Networks to Player Data Analysis in Freemium Video Games

We demonstrate the applicability and practicality of recurrent neural networks (RNNs), a machine learning methodology suited for sequential data, on player data from the mobile video game My Singing Monsters. Since this data comes in as a stream of events, RNNs are a natural solution for analyzing this data with minimal preprocessing. We apply RNNs to monitor and forecast game metrics, predict player conversion, estimate lifetime player value, and cluster player behaviours. In each case, we discuss why the results are interesting, how the trained models can be applied in a business setting, and how the preliminary work can serve as a foundation for future research. Finally, as data on video game players is typically proprietary and confidential and results of research often go unpublished, this thesis serves to contribute to the literature on game user research

Automated network optimisation using data mining as support for economic decision systems

The evolution from wired voice communications to wireless and cloud computing services has led to the rapid growth of wireless communication companies attempting to meet consumer needs. While these companies have generally been able to achieve quality of service (QoS) high enough to meet most consumer demands, the recent growth in data hungry services in addition to wireless voice communication, has placed significant stress on the infrastructure and begun to translate into increased QoS issues. As a result, wireless providers are finding difficulty to meet demand and dealing with an overwhelming volume of mobile data. Many telecommunication service providers have turned to data analytics techniques to discover hidden insights for fraud detection, customer churn detection and credit risk analysis. However, most are illequipped to prioritise expansion decisions and optimise network faults and costs to ensure customer satisfaction and optimal profitability. The contribution of this thesis in the decision-making process is significant as it initially proposes a network optimisation scheme using data mining algorithms to develop a monitoring framework capable of troubleshooting network faults while optimising costs based on financial evaluations. All the data mining experiments contribute to the development of a super–framework that has been tested using real-data to demonstrate that data mining techniques play a crucial role in the prediction of network optimisation actions. Finally, the insights extracted from the super-framework demonstrate that machine learning mechanisms can draw out promising solutions for network optimisation decisions, customer segmentation, customers churn prediction and also in revenue management. The outputs of the thesis seek to help wireless providers to determine the QoS factors that should be addressed for an efficient network optimisation plan and also presents the academic contribution of this research

Churn prediction models tested and evaluated in the Dutch indemnity industry

Due to global developments customer churn is getting a growing concern to the insurance industry. Technological improvements like the internet makes it much easier for customer to compare their policies, obtain new offers or even churn from one provider to another. The insurance industry therefore has become a heavily competitive market in which insurance companies have to compete to protect and expand their customer base in order to maintain or expand their market position. Thus, retaining customers is becoming more and more important and therefore finding customers who are most likely to leave is a central aspect. Many different techniques are available to identify customers who are most likely to leave, however which technique can be used best is often not clear. Research clarifies that the characteristics of the industry and/or dataset which is used are mostly assessing related to performance. In advance it is impossible to determine the best suited technique to use if previous research in which performance was tested has not been published. This study presents a data mining methodology in which the four most used prediction techniques in literature are tested and evaluated using a real life voluminous insurance company dataset to determine which technique performs best. Using the same dataset makes results comparable and clears out which technique performs best based on the insurance data domain characteristics

Toward a model of customer experience

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Retaining high-value and profitable customers is a major strategic objective for many companies. In mature mobile phone markets where growth has slowed, the defection of customers from one network to another has intensified and is strongly fuelled by poor Customer Experience. Trends in the service economy suggest that experience can be exploited as a means of supplying the basis of a new economic offering, ignited in part by the shift that is taking place in the analysis of people’s interaction with digital products. In this light, the research describes a strategic approach to the use of Information Systems as a means of improving Customer Experience. Using Action Research in a mobile telecommunications operator, a Customer Experience Monitoring and Action Response model (CEMAR) is developed that evaluates disparate customer data, residing across many systems, builds experience profiles and suggests appropriate contextual actions where experience is poor. The model provides value in identifying issues, understanding them in the context of the overall Customer Experience (over time) and dealing with them appropriately. The novelty of the approach is the synthesis of data analysis with an enhanced understanding of Customer Experience which is developed implicitly, in real-time and in advance of any instigation by the customer.Royal Academy of Engineerin