Technology changes both people and opportunities. Both goods and services and the way they reach them change. Developing technology has eliminated time and space barriers which are the biggest constraints of trade. Customers can purchase 24/7. This situation greatly increased the trade volume. Companies need to offer special promotions to their customers in order to achieve a growth which is directly proportional to the increasing trade volume. Such solutions can be determined by data mining techniques. In this study, 12330 online shopping data were analyzed and data mining classification algorithm was applied to the analyzed data. As a result, it was found that the most important variables affecting the purchasing behavior of the customers are the number of product information pages visited, the month visited, the previous shopping behavior of the visitor. After the classification process, it was seen that increasing the number of informative pages about the products would have a positive effect. In additon, it was found that it is important to organize campaigns for special days and weekends. Furthermore, it was detected that special offers should be presented to customers in May and October by using predictive data mining practices.
Data Mining, Decision Tree, Online Shopping Behaviors
|Author :||Şengül CAN - Mustafa Gerşil|
|Number of pages:||350-360|