Abstract
The presented investigations deal with the implementation of data analytics over Turkey-based e-commerce company’s data repositories. The main objective is to hunt for classification of the customer’s behavior patterns. Artificial neural network (ANN) model was applied over customer’s dataset to forecast the customer’s purchasing patterns. The result would benefit the marketing department to recognize the targeted customers for specific campaigning activity. The efficiency of ANN models was also tested. The obtained results revealed that the neural network model using back-propagation technique has high accuracy toward customer prediction. The implementations were carried out in R programming environment.
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References
G. Sılahtaroğlu, H. Dönertaşli, Analysis and prediction of Ε-customers’ behavior by mining clickstream data, in 2015 IEEE International Conference on Big Data (Big Data) (IEEE, 2015), pp. 1466–1472
T.K. Das, A customer classification prediction model based on machine learning techniques, in 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (IEEE, 2015), pp. 321–326
B. Altunan, E.D. Arslan, M. Seyis, M. Birer, F. Üney-Yüksektepe, A data mining approach to predict E-Commerce customer behaviour, in The International Symposium for Production Research (Springer, Cham, 2018), pp. 29–43
S. Peker, A. Kocyigit, P.E. Eren, An empirical comparison of customer behavior modeling approaches for shopping list prediction, in 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (IEEE, 2018)
S. Senecal, P.J. Kalczynski, J. Nantel, Consumers’ decision-making process and their online shopping behavior: a clickstream analysis. J. Bus. Res. 58(11), 1599–1608 (2005)
G. Sılahtaroğlu, Predicting gender for online customer using artificial neural networks. Int. J. Adv. Electron. Comput. Sci. 2(10) (2015). ISSN: 2393-2835
N.S. Safa, N.A. Ghani, M.A. Ismail, An artificial neural network classification approach for improving accuracy of customer identification in e-commerce. Malays. J. Comput. Sci. 27(3), 171–185 (2014)
G. Suchacka, S. Stemplewski, Application of neural network to predict purchases in online store, in Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology–ISAT 2016–Part IV (Springer, Cham, 2017)
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Hambarde, K. et al. (2020). Augmentation of Behavioral Analysis Framework for E-Commerce Customers Using MLP-Based ANN. In: Borah, S., Emilia Balas, V., Polkowski, Z. (eds) Advances in Data Science and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-15-0978-0_4
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DOI: https://doi.org/10.1007/978-981-15-0978-0_4
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