Researcher:
Ürküp, Çağan

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Master Student

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Çağan

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Ürküp

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Ürküp, Çağan

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20181

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Researcher Of Publications: search.filters.isAuthorOfPublication.7993519f-2dd6-4326-ab5a-705a5274d8c3

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    PublicationOpen Access
    Customer mobility signatures and financial indicators as predictors in product recommendation
    (Public Library of Science, 2018) Bozkaya, Burçin; Department of Industrial Engineering; Ürküp, Çağan; Salman, Fatma Sibel; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 178838
    The rapid growth of mobile payment and geo-aware systems as well as the resulting emergence of Big Data present opportunities to explore individual consuming patterns across space and time. Here we analyze a one-year transaction dataset of a leading commercial bank to understand to what extent customer mobility behavior and financial indicators can predict the use of a target product, namely the Individual Consumer Loan product. After data preprocessing, we generate 13 datasets covering different time intervals and feature groups, and test combinations of 3 feature selection methods and 10 classification algorithms to determine, for each dataset, the best feature selection method and the most influential features, and the best classification algorithm. We observe the importance of spatio-temporal mobility features and financial features, in addition to demography, in predicting the use of this exemplary product with high accuracy (AUC = 0.942). Finally, we analyze the classification results and report on most interesting customer characteristics and product usage implications. Our findings can be used to potentially increase the success rates of product recommendation systems.