Exploratory Business Data Analysis of an Online Retail Dataset Using R

This study presents an exploratory business analysis of an online retail transactional dataset with the aim of extracting actionable insights related to sales performance, customer concentration, and product dynamics. Using R as the primary analytical tool, the dataset was cleaned and transformed to address missing values, cancelled transactions, and formatting inconsistencies. Monthly revenue trends were examined to identify seasonality patterns. A Pareto-based customer concentration analysis revealed that approximately 27% of customers accounted for 80% of total UK revenue, highlighting significant dependency on a relatively small customer segment. Product-level analysis identified high-revenue and high-volume items, revealing disparities between sales quantity and revenue contribution. The findings demonstrate how junior-level analytical workflows can generate meaningful business insights through descriptive statistics, visualization, and structured data preparation. This work provides a reproducible framework for exploratory retail analytics and illustrates the practical value of lightweight analytical approaches in operational decision-making.