E Commerce Shopping Cart Behaviour Analysis Using SQL
Group: Use Case
|Product Category: Database Design & Development
|Sub Category: PostgreSQL
About this Product
Discover shopping behavior trends and improve your SQL skills with real e-commerce data.
Business Overview:
E-Commerce Shopping Cart Behaviour Analysis Using SQL helps you explore how customers behave while shopping online. Using real data from a simulated e-commerce platform, you’ll analyze things like which products are added to carts the most, how often carts are abandoned, and which items generate the most revenue. It’s made for people who want to use SQL to answer business questions and understand customer habits better.
Product Highlights:
- A comprehensive PDF guide featuring 6 practical SQL use cases
Includes sample outputs to show expected analytical results. - Built on a rich realistic e-commerce dataset of over 25000 rows with relational tables (users, products, carts, orders, and more).
- Perfect for SQL learners, analysts, BI professionals, and e-commerce teams focused on optimization and insight generation.
- Valuable addition to portfolios showcasing data analysis and business insight skills
Learning Outcomes:
By working through this product, you’ll be able to:
- Write complex useful SQL queries to analyze shopping patterns.
- Spot trends like cart abandonment and high-revenue products.
- Understand how customers interact with an online store.
- Use data to answer business questions and support decisions.
- Build confidence in analyzing real-life datasets with SQL.
Resources
Description:
This csv contains dataset that defines the mid-level classification of products, linking them to broader categories and allowing deeper catalog segmentation.
- Includes subcategory IDs, names, and related category IDs
- Enables mid-tier product grouping for reporting and filtering
- Useful for drilling into niche segments and merchandising analytics
- Supports catalog hierarchy building and navigation logic
- Complements categories and products tables for enriched taxonomy
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