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    An In-Depth Case Study of Volkswagen's AI Integration
    (CEUR-WS, 2024) Erdoğan, Ali Mert; Hiziroglu, Ourania Areta; Hiziroglu, Abdulkadir
    As Artificial Intelligence (AI) technologies have become increasingly integral to business operations and many firms aspire to generate business value with that, understanding the factors that facilitate or hinder successful implementation is crucial for organizations across industries. Using Volkswagen Group (VW) as a case study, the goal of this study is to comprehensively examine the AI implementations in a holistic manner, including enablers and inhibitors, utilization in terms of automation and augmentation, process-level impacts, and broader firm-level outcomes. This work not only contributes to the understanding of AI adoption within a major automotive player, but also serves as a resource for organizations by navigating through the complexities of AI implementation, offering practical insights and lessons learned from the case. © 2023 Copyright for this paper by its authors.
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    Business Analytics in Customer Lifetime Value: An Overview Analysis
    (Wiley Periodicals, Inc, 2025) Dogan, Onur; Hiziroglu, Abdulkadir; Pisirgen, Ali; Seymen, Omer Faruk
    In customer-oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied to gain deeper insights into customer behaviors and decision-making processes. Despite the recognized importance of CLV, there is a noticeable gap in comprehensive analyses and reviews of BA techniques applied to CLV. This study aims to fill this gap by conducting a thorough survey of the state-of-the-art investigations on CLV models integrated with BA approaches, thereby contributing to a research agenda in this field. The review methodology consists of three main steps: identification of relevant studies, creating a coding plan, and ensuring coding reliability. First, relevant studies were identified using predefined keywords. Next, a coding plan-one of the study's significant contributions-was developed to evaluate these studies comprehensively. Finally, the coding plan's reliability was tested by three experts before being applied to the selected studies. Additionally, specific evaluation criteria in the coding plan were implemented to introduce new insights. This study presents exciting and valuable results from various perspectives, providing a crucial reference for academic researchers and marketing practitioners interested in the intersection of BA and CLV.
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    Customer Behavior Analysis by Intuitionistic Fuzzy Segmentation: Comparison of Two Major Cities in Turkey
    (World Scientific Publ Co Pte Ltd, 2022) Dogan, Onur; Seymen, Omer Faruk; Hiziroglu, Abdulkadir
    The vast quantity of customer data and its ubiquity, as well as the inabilities of conventional segmentation tools, have diverted researchers in search of powerful segmentation techniques for generating managerially meaningful information. Due to its noteworthy practical use, soft computing-based techniques, especially fuzzy clustering, can be considered one of those contemporary approaches. Although there have been various fuzzy-based clustering applications in segmentation, intuitionistic fuzzy sets that have the complimentary feature have appeared in limited studies, especially in a comparative context. Therefore, this study extends the current body of the pertaining literature by providing a comparative assessment of intuitionistic fuzzy clustering. The comparison was carried out with two other well-known segmentation techniques, k-means and fuzzy c-means, based on transaction data that belong to Turkey's two major cities. Over 10,000 records of customers' data were processed for segmentation purposes, and the comparative approaches were presented. According to the results, the intuitionistic fuzzy clustering approach outperformed the other methods in terms of the clustering efficiency index being utilized. The validity of the segmentation structure obtained by the superior approach was ensured via nonsegmentation variables. The comparative assessment and the potential managerial implications could be considered as a contribution to the corresponding literature. This study also compares the effects of the different parameter values used in the proposed model.
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    Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment
    (Gazi Univ, 2023) Seymen, Omer Faruk; Olmez, Emre; Dogan, Onur; Orhan, E. R.; Hiziroglu, Abdulkadir
    Churn studies have been used for many years to increase profitability as well as to make customer -company relations sustainable. Ordinary artificial neural network (ANN) and convolution neural network (CNN) are widely used in churn analysis due to their ability to process large amounts of customer data. In this study, an ANN and a CNN model are proposed to predict whether customers in the retail industry will churn in the future. The models we proposed were compared with many machine learning methods that are frequently used in churn prediction studies. The results of the models were compared via accuracy classification tools, which are precision, recall, and AUC. The study results showed that the proposed deep learning-based churn prediction model has a better classification performance. The CNN model produced a 97.62% of accuracy rate which resulted in a better classification and prediction success than other compared models.
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    Health Big Data Modelling and Analytics
    (CRC Press, 2024) Hiziroglu, Abdulkadir; Pisirgen, Ali; Codal, Keziban Seckin
    This chapter delves into the multifaceted applications of health big data modelling and analytics across distinct categories, offering valuable insights into the transformative landscape of healthcare systems. The discussion encompasses health big data, health decision-making development, business process management for more efficient hospital operations, and analytics focused on medical diagnostics and disease/treatment monitoring. Health data modelling involves creating structured frameworks for organizing and managing healthcare data. These frameworks are crucial for efficient data storage, retrieval, and analysis within healthcare systems. Moreover, the concept of health big data extends beyond personal health records and includes vast amounts of information produced by the health sector. It explores opportunities and challenges associated with health big data analytics, highlighting its potential benefits in improving health services, early disease detection, and personalized medicine. Further exploration into artificial intelligence for health data modelling and analytics emphasizes the transformative impact of machine learning in healthcare, specifically in disease diagnosis, outcome prediction, and personalized treatment strategies. The selection of an appropriate AI model is referred crucial, considering factors such as accuracy, interpretability, scalability, and ethical considerations. Transparent and interpretable models, exemplified by decision trees, are recommended to foster trust among healthcare professionals. The chapter concludes by underscoring the paramount importance of addressing ethical and legal considerations, along with domain-specific requirements, to ensure the responsible and effective application of machine learning in healthcare, ultimately contributing to optimal patient care and the evolution of healthcare systems. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.
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    The Impact of Artificial Intelligence on Healthcare Industry: Volume 1: Non-Clinical Applications
    (CRC Press, 2024) Berktas, Mustafa; Hiziroglu, Abdulkadir; Erbaycu, Ahmet Emin; Er, Orhan; Kahyaoglu, Sezer Bozkus
    Healthcare and medical science are inherently dependent on technological advances and innovations for improved care. In recent times we have witnessed a new drive in implementing these advances and innovations through the use of Artificial Intelligence, in both clinical and non-clinical areas. The set of 2 volumes aims to make available the latest research and applications to all, and to present the current state of clinical and non-clinical applications in the health sector and areas open to development, as well as to provide recommendations to policymakers. This volume covers non-clinical applications. The chapters covered in this book have been written by professionals who are experts in the healthcare sector and have academic experience. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.