Analysis on User Activity in E-Commerce Website for Performance Evaluation and Decision Making Using Big Data Analytics

The purpose of this study is to analyze how Big Data Analytics can help managers in their decision making process, specifically in e-commerce / web-based business. This study took the dataset from one website that implements e-commerce functions in its website. The data taken would be the data of user activities in the website such as page visit, add product to cart, and buy online. To analyze the dataset, several algorithm will be used, such as Association Rule Mining Algorithm (APRIORI), K-Means Clustering, and Pearson's Correlation Coefficient. The data will be processed and will be used to show details in users behavior in the website to see the specific pattern that can be useful for decision making. For example, which product is visited and bought the most, how many pages are visited before buying the product, how many users repurchase the product, and which retailer is mostly used by users. This paper would also identify whether the current Big Data implementation on the company can be improved, and identify if it is a good investment for the company to improve the Big Data implementation system.