Context:

The e-commerce industry is highly dynamic, with customer preferences and market trends constantly evolving. To stay ahead, companies must continuously analyse their sales, product, and customer data to identify key performance indicators (KPIs) and patterns that can influence strategic decisions. This project focuses on utilizing SQL to explore and analyse various datasets—demographic, transaction, and product data—to uncover actionable insights. These insights will be pivotal in understanding customer behaviour, optimizing product offerings, and enhancing overall business performance.

 Objective:

The primary objective of this analysis is to harness the power of SQL to explore e-commerce data and uncover patterns, trends, and key performance indicators that contribute to profitable growth. Specific goals include:

Analyzing customer demographics to identify segments that drive the most value. Investigating sales data to determine revenue drivers and optimize product offerings. Evaluating product performance to understand trends and customer preferences. Calculating KPIs such as average basket size, customer retention rates, and sales per product category.

Approach: Using SQL in Big Query, the analysis will involve querying the provided datasets to extract relevant metrics, perform aggregations, and generate detailed reports. This approach will enable a comprehensive understanding of the data, facilitating the identification of opportunities for improving customer engagement, optimizing product strategies, and driving overall business growth.  

Data Dictionary: