Analyzing the customer purchase data of an online shopping store by data mining: A real case study in Iran

Nowadays, online shopping plays a vital role in providing services and delivering goods to customers in the context of business intelligence and e-commerce. This research analyzes the customer purchase data of an Iranian online shopping company in Tehran. Among the available datasets provided by the company, 200 thousand records of one week of transactions have been selected for the present study. Several classification methods (i.e., Random Forest, gradient-boosted trees, K-Nearest Neighbor (KNN), Naïve Bayes, Kernel Naïve Bayes, and Neural Networks) and clustering approaches have been applied to discover the knowledge and patterns. The results show that before balancing the dataset, the KNN algorithm with K=5 is the best classification method among the existing methods. However, after balancing, gradient-boosted trees outperform the other classification methods. For clustering methods, the results show that the K-Means algorithm with K=3 is more efficient regarding the average within centroid distance for each cluster. Finally, concluding remarks and suggestions for future studies are stated.