Buy when? Survival machine learning model comparison for purchase timing

Due to advancements in information technology and the rapid rise of the Internet, the data revolution of the past several decades has caused businesses to create more data than they can utilize or understand (see Erevelles, S.; Fukawa, N.; Swayne, L., 2016; Seng, J.L.; Chen, T., 2010). The expansion in the volume of data, the variety of data kinds, and the scope of analysis has necessitated technological advancements beyond storage, transport, and processing (see Seng, J.L.; Chen, T., 2010) The data must be translated into information and knowledge in order to transfer knowledge into decision-making tools for enterprises. This data is used in marketing research to identify intriguing links between market segmentation in industrial, tourist, and other markets, customer lifetime value, loyalty and client segment, direct market, marketing campaign, and other applications (Tkáˇc, M.; Verner, R., 2016). With the application of Machine Learning (ML) methods (see Bahari, T.F.; Elayidom, M.S., 2015; Jessen, H.C.; Paliouras, G., 2001), it is currently anticipated that enormous volumes of stored data may be explored, and usable information extracted. ML are strategies that equip computers with the capacity to comprehend, using data and experiences similar to the human brain (Çelik, Ö., 2018).