Browsing by Author "Eren, P. Erhan"
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Conference Object Citation Count: 0An Empirical Comparison of Customer Behavior Modeling Approaches for Shopping List Prediction(Ieee, 2018) Peker, Serhat; Kocyigit, Altan; Eren, P. ErhanShopping list prediction is a crucial task for companies as it can enable to provide a specific customer a personalized list of products and improve customer satisfaction and loyalty as well. To predict customer behaviors, many studies in the literature have employed customer behavior modeling approaches which are individual-level and segment-based. However, previous efforts to predict customers' shopping lists have rarely employed these state-of-the-art approaches. In this manner, this paper introduces the segment based approach into the shopping list prediction and then presents an empirical comparison of the individual-level and the segment-based approaches in this problem. For this purpose, well-known machine learning classifiers and customers' purchase history are employed, and the comparison is performed on a real-life dataset by conducting a series of experiments. The results suggest that there is no clear winner in this comparison and the performances of customer behavior modeling approaches depend on the machine learning algorithm employed. The study can help researchers and practitioners to understand different aspects of using customer behavior modeling approaches in the shopping list prediction.Article Citation Count: 12A hybrid approach for predicting customers' individual purchase behavior(Emerald Group Publishing Ltd, 2017) Peker, Serhat; Kocyigit, Altan; Eren, P. Erhan; Software EngineeringPurpose - Predicting customers' purchase behaviors is a challenging task. The literature has introduced the individual-level and the segment-based predictive modeling approaches for this purpose. Each method has its own advantages and drawbacks, and performs in certain cases. The purpose of this paper is to propose a hybrid approach which predicts customers' individual purchase behaviors and reduces the limitations of these two methods by combining the advantages of them. Design/methodology/approach - The proposed hybrid approach is established based on individual-level and segment-based approaches and utilizes the historical transactional data and predictive algorithms to generate predictions. The effectiveness of the proposed approach is experimentally evaluated in the domain of supermarket shopping by using real-world data and using five popular machine learning classification algorithms including logistic regression, decision trees, support vector machines, neural networks and random forests. Findings - A comparison of results shows that the proposed hybrid approach substantially outperforms the individual-level and the segment-based approaches in terms of prediction coverage while maintaining roughly comparable prediction accuracy to the individual-level method. Moreover, the experimental results demonstrate that logistic regression performs better than the other classifiers in predicting customer purchase behavior. Practical implications - The study concludes that the proposed approach would be beneficial for enterprises in terms of designing customized services and one-to-one marketing strategies. Originality/value - This study is the first attempt to adopt a hybrid approach combining individual-level and segment-based approaches to predict customers' individual purchase behaviors.Conference Object Citation Count: 0A Methodology for Product Segmentation Using Sale Transactions(Ieee, 2018) Peker, Serhat; Kocyigit, Altan; Eren, P. ErhanThis paper presents a novel methodology for product segmentation using customers' transactions on products. The proposed methodology introduces FMC model, and utilizes this model's features and clustering algorithms to group products into segments. The applicability of the proposed approach has been demonstrated on data collected by a supermarket chain. The results show that the pro-posed methodology provides an efficient tool that can be used to identify different product segments and to gain valuable insights about these distinct groups. The resulting product segments can help managers in the inventory management and developing marketing strategies.Conference Object Citation Count: 1A Software Development Process Model for Cloud by Combining Traditional Approaches(Springer international Publishing Ag, 2015) Hacaloğlu, Tuna; Eren, P. Erhan; Mıshra, Alok; Mishra, Alok; Mıshra, Deepti; Information Systems Engineering; Software Engineering; Computer EngineeringEven though cloud computing is a technological paradigm that has been adopted more and more in various domains, there are few studies investigating the software development lifecycle in cloud computing applications and there is still not a comprehensive software development process model developed for cloud computing yet. Due to the nature of cloud computing that is completely different from the traditional software development, there is a need of suggesting process models to perform the software development systematically to create high quality software. In this study, we propose a new conceptual Software Development Life Cycle Model for Cloud Software Development that incorporates characteristics of different process models for traditional software development. The proposed model takes traditional model's specific characteristics into account and also considers cloud's specific nature i.e. advantages and challenges as well.