E-Commerce Shopping Cart Behaviour Analysis Using SQL
Group Category: Use Case
Product Category: Database Design & Development
Sub Category: PostgreSQL
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.
products
| CSVDescription:
This file holds detailed product-level information, enabling comprehensive catalog analysis and product performance tracking.
- Contains product IDs, names, pricing, stock levels, and subcategory links
- Supports SKU-level sales and margin analysis
- Useful for inventory planning, pricing strategy, and demand forecasting
- Enables filtering and grouping by category, brand, or availability
- Integrates with order and cart datasets for product journey mapping
users
| CSVDescription:
This CSV provides user-level demographic and registration data, foundational for customer profiling and segmentation.
- Includes user IDs, names, contact details, and registration timestamps
- Enables cohort analysis, regional breakdowns, and user lifecycle tracking
- Useful for personalizing experiences, understanding retention, and modeling value
- Supports linking to orders, carts, and behavioral datasets
- Complements churn, reactivation, and loyalty-based reporting
cart
| CSVDescription:
This CSV captures individual shopping cart sessions, providing insight into user activity and cart behaviors in an e-commerce environment.
- Includes cart IDs, user associations, and timestamps
- Enables session-based analysis of cart initiation and engagement
- Useful for identifying drop-off points, cart frequency, and timing patterns
- Supports segmentation of users based on cart activity behavior
- Complements conversion and abandonment tracking across the funnel
cart_items
| CSVDescription:
This CSV details the specific items added to shopping carts, allowing for item-level interaction analysis within user journeys.
- Contains cart IDs, product references, quantities, and timestamps
- Facilitates tracking of high-interest products and cart-building trends
- Useful for calculating cart composition, average item count, and product popularity
- Supports product recommendation engines and upsell targeting
- Integrates seamlessly with cart and product datasets for full funnel visibility
categories
| CSVDescription:
This file provides the top-level categorization of products, essential for organizing inventory and analyzing broad shopping trends.
- Includes category IDs and names
- Enables grouping of products for category-level sales and engagement metrics
- Useful for high-level reporting and catalog structuring
- Supports category filter design and hierarchical product mapping
- Complements subcategory and product tables for full taxonomy
orders
| CSVDescription:
This dataset captures completed order records, essential for revenue analysis, order frequency, and customer journey tracking.
- Includes order IDs, user IDs, timestamps, and status flags
- Facilitates time-based order trend analysis and purchase frequency modeling
- Useful for conversion funnel analysis, campaign impact, and cohort reporting
- Supports revenue tracking by user, date, and order type
- Complements order_items and user datasets for transactional profiling
order_items
| CSVDescription:
This CSV contains line-level details of completed orders, capturing the connection between products and transactions.
- Includes order IDs, product IDs, quantity, and pricing details
- Enables revenue calculations, item-level profitability, and fulfillment tracking
- Useful for understanding product-level performance in sales
- Supports basket analysis, return forecasting, and SKU-level trend modeling
- Pairs with orders and product datasets for complete purchase insights
subcategories
| CSVDescription:
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
$2.00 $1.29 35% OFF
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