Yazar "Seymen, Ömer Faruk" seçeneğine göre listele

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    Customer churn prediction using deep learning
    (Springer Science and Business Media Deutschland GmbH, 2021) Seymen, Ömer Faruk; Doğan, Onur; Hızıroğlu, Abdülkadir
    Churn studies have been used for years to achieve profitability and to establish a sustainable customer-company relationship. Deep learning is one of the contemporary methods used in churn analysis due to its ability to process huge amounts of customer data. In this study, a deep learning model is proposed to predict whether customers in the retail industry will churn in the future. The model was compared with logistic regression and artificial neural network models, which are also frequently used in the churn prediction studies. The results of the models were compared with accuracy classification tools, which are precision, recall and AUC. The results showed that the deep learning model achieved better classification and prediction success than other compared models. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    An empirical assessment of customer lifetime value models within data mining
    (Univ Latvia, 2018) Hızıroğlu, Abdülkadir; Şişci, Merve; Cebeci, Halil Ibrahim; Seymen, Ömer Faruk
    Customer lifetime value has been of significant importance to marketing researchers and practitioners in specifying the importance level of each customer. By means of segmentation which could be carried out using value-based characteristics it is indeed possible to develop tailored strategies for customers. In fact, approaches like data mining can facilitate extraction of critical customer knowledge for enhanced decision making. Although the literature has several analytical lifetime value models, comparative assessment of the existing models especially within the context of data mining seems a missing component. The aim of this paper is to compare two different customer lifetime value models within data mining. The evaluation was carried out within the context of customer segmentation using a database of a company operating in retail sector. The results indicated that two models yield the same segmentation structure and no statistical differences detected on the select control variables. However, the remaining model produced rather different segmentation results than their peers and it was possible to identify the most lucrative model according to the statistical analyses that were carried out on the select control variables.
  • Küçük Resim
    Öğe
    Segmentation of retail consumers with soft clustering approach
    (Springer, 2021) Doğan, Onur; Hızıroğlu, Abdülkadir; Seymen, Ömer Faruk
    Defining customer requirements in a huge amount of data of the digital era is crucial for companies in a competitive business environment. Customer segmentation has been attracted to a great deal of attention and has widely been performed in marketing studies. However, boundary data which are close to more than one segment may be assigned incorrect classes, which affects to make the right decisions and evaluations. Therefore, segmentation analysis is still needed to develop efficient models using advanced techniques such as soft computing methods. In this study, an intuitionistic fuzzy clustering algorithm were applied to customer data in a supermarket according to the amount spent in some product groups. The data represent 33-month customer shopping data in a supermarket for eight product groups. The results indicate the intuitionistic fuzzy c-means based customer segmentation approach produces more reliable and applicable marketing campaigns than conditional fuzzy c-means and k-means segmentation method. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.