M.A. Theses

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https://digitalarchive.library.bogazici.edu.tr/handle/123456789/11754

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Browsing M.A. Theses by Author "Taşkın, Nazım."

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    Churn prediction in online payment sector using survival analysis
    (Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in Social Sciences, 2023., 2023) Özalpay, Gözde.; Taşkın, Nazım.
    Online payment systems are rapidly becoming a vital component of fintech world. With the entry of numerous firms into the sector, competition has become intense, and customers can easily switch providers without prior notice. As a result, churn has emerged as a critical topic for organizations seeking to remain competitive. The study focuses on the key factors that influence churn in an online payment company, specifically, a well-known payment service provider in Turkiye. One of the other objectives of the study is to find out which churn period and which input variables combination is more suitable with company’s business model and customer’s behavior. To arrive at this conclusion, we have developed a survival model that can answer both questions. We analyzed the customers who had been using the services and identified them as churn and non-churn based on three different methods. We examined various variables that could influence churn, including demographic factors, payment history, and usage patterns, to build three different models with different target variables. The results of these models were compared to determine which variables were most significant in predicting churn and which type of churn period is more suitable to answer the question in hand. All the statistical models exhibited similar performance indicators and variable importance rankings. The findings indicate that commission rate change, refund rate, payment count, volume, merchant type, merchant source name, and merchant sector were significant predictors of customer churn, while settlement period and working area of the merchant were not significantly associated with churn. Moreover, the second model, which defined the churn event as occurring within a one- month period, outperformed the other models. Additionally, the results suggest that the risk of customer churn increases after 25 months of doing business with the company.
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    Cyber insurance adoption in SMEs as a risk management tool in digitalization
    (Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in Social Sciences, 2022., 2022.) Özkeleş Yıldırım, Aslı.; Metin, Bilgin.; Taşkın, Nazım.
    Small – medium sized enterprises (SMEs) create the backbone of the Turkish economy. Digitalization is a key advancement for SMEs in order to create efficiency and open up new opportunities for innovation. However, digitalization makes SMEs vulnerable to cyber threats by opening an outlet to other systems. The lack of awareness of cyber protection and the increasing advancements in cyberattacks puts SMEs at risk of data breaches which in turn causes damage to the company. Cyber insurance is considered a risk management tool for the coverage of costs in the event of an unexpected cyber incident. Even though the coverages are beneficial for the insured, the cyber insurance market is far from reaching its full potential. The study aims to find the factors of cyber insurance adoption for SMEs and the effects of cyber insurance on digitalization through cyber readiness, organizational security performance and information and communication technologies (ICT) adoption. The model created for the study was based on technology-organization-environment (TOE) context extended with individual context and the post adoption effects of cyber insurance. A quantitative survey aimed towards SMEs was conducted to test the model. Methods for increasing adoption of cyber insurance among SMEs were suggested based on the model outcomes.
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    Using social media big data with machine learning to improve customer satisfaction
    (Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in Social Sciences, 2023., 2023) Demir, Hilal.; Taşkın, Nazım.
    With the increasing importance of customer relationship management and improving customer support in today's competitive business landscape, there is a growing need to leverage machine learning techniques for gaining insights, forecasts, and better decision-making. Sentiment analysis, in particular, has emerged as a powerful tool for improving customer support services. In this study, we explore the use of three gradient boosting algorithms, XGBoost, CatBoost, and LightGBM, for sentiment classification on Twitter data. We employ ensemble classifications to analyze the sentiment of the data and observe improvements in performance. Our results are compared to other two algorithms that are popularly used in the context of sentiment analysis and show that the ensemble classification of the three algorithms yields the highest accuracy and F1 score. By addressing the gap in understanding how different machine learning algorithms can be used to enhance customer support processes, this research aims to contribute to the improvement of customer satisfaction and loyalty. Specifically, the study aims to improve the accuracy of sentiment classification, thereby enabling businesses to better meet customer expectations for fast and efficient customer support.