From Transactions to Trends: An Integrated Analysis of Customer Shopping Behavior

This paper presents a comprehensive analysis of customer shopping behavior through the examination of 3,900 transactional records derived from a retail dataset. The study employs a multi-tool analytical framework integrating Python for data preprocessing and feature engineering, SQL for structured business-oriented querying, and Power BI for the development of interactive dashboards and visualizations. The investigation encompasses five primary dimensions: demographic influences on purchasing decisions, spending patterns across various segments, product preferences and performance metrics, subscription behavior, and discount utilization patterns. Key findings include statistically notable revenue differentials by gender and age group, identification of top-rated and best-selling product categories, and the delineation of distinct customer loyalty segments. The integrated analytical approach yielded actionable business insights, culminating in strategic recommendations including subscription growth initiatives, evidence-based discount policy reforms, and targeted marketing strategies. The research demonstrates the practical value of combining multiple data analytics tools to derive meaningful intelligence from transactional retail data.