E-commerce & Marketing Dataset Generator

Your Sandbox for Mastering E-commerce Data Analysis. Create realistic, interconnected datasets for analysis.

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Data Preview

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The Ultimate E-commerce & Marketing Dataset Generator

Every e-commerce analyst knows the challenge: understanding the complete customer journey, from the first ad click to the final purchase, requires a complex, multi-faceted dataset. Finding realistic practice data that connects online transactions, shopping cart behavior, and promotional campaigns is nearly impossible.

That’s why we built the PNRao E-commerce & Marketing Data Generator.

This powerful tool creates a complete, enterprise-grade e-commerce dataset with a single click. Whether you’re an analyst practicing how to build a conversion funnel, a student learning about online retail, or a marketing manager building a business case for a new promotion, this generator provides the data you need. Stop working with simple sales lists and start analyzing the entire e-commerce lifecycle.

Why This Dataset is a Powerful Learning Tool

Learning e-commerce analytics with abstract numbers can be challenging. This generator provides a realistic context that makes complex concepts easier to grasp.

  • Builds a Narrative: You can step into the role of an E-commerce Analyst for the PNRao Company. Your task could be to identify where customers abandon their shopping carts, analyze which promotional codes are most effective, or perform an RFM analysis to find your most valuable online customers. This narrative-driven practice helps solidify your understanding.
  • Makes Concepts Concrete: When you see a Shopping Cart Funnel table linked to Online Transactions and Customers, abstract concepts become tangible. This makes metrics like Cart Abandonment Rate, Customer Lifetime Value (CLV), and Average Order Value (AOV) easier to understand because you can see how they are built from the ground up.
  • Encourages Realistic Analysis: The rich context inspires more creative and insightful analysis. You might build a dynamic customer journey dashboard in Power BI or create a report in Excel to analyze the profitability of different product categories. This allows you to practice not just the technical skills, but also the art of data storytelling.

Designed for Real-World E-commerce Analysis

This dataset isn’t just a random collection of tables. It has been carefully crafted to mirror the data structures you will find in actual e-commerce platforms like Shopify or Magento, making it the perfect tool to develop practical, job-ready skills.

  • Reflects Core E-commerce Operations: Every online store deals with customer sessions, shopping carts, orders, and promotions. The tables in this generator represent the core of what you will encounter professionally. By understanding how these tables relate to each other, you are learning the fundamental blueprint of e-commerce data.
  • Focus on a High-Demand Skillset: E-commerce analytics is a critical function for any online business. The skills you build here—analyzing conversion funnels, calculating customer acquisition cost, and performing cohort analysis—are directly transferable to the tasks and challenges you will face in a real data analytics or e-commerce job.
  • Covers a Wide Range of Concepts: The structure of this data is intentionally designed to be a comprehensive playground for learning. With this single resource, you can practice:
    • Excel/Sheets Functions: Use VLOOKUP or XLOOKUP to connect transaction data with customer details, SUMIFS and COUNTIFS to create summary reports, and PivotTables to analyze sales by product category.
    • Funnel Analysis: Use the Shopping Cart Funnel data to visualize where users drop off in the checkout process.
    • Data Cleaning: Use the “messy data” option to practice cleaning and validating customer records and order information.
    • Advanced Analysis: The interconnected tables are perfect for practicing relationship-building in Power BI or writing SQL JOIN queries to combine data from multiple sources for a complete analysis.

In short, learning with this dataset ensures you are not just practicing abstract concepts, but preparing yourself with the practical knowledge and experience valued by employers.

Tables Overview

The generator produces a wide array of datasets across different e-commerce functions. Each dataset has a unique structure, as detailed below.

Core Transactions & Funnel

Table Name Description Columns
Online Transactions The main fact table for all completed online orders. TransactionID, CustomerID, OrderDate, TotalAmount, PromotionID, ShippingMethod
Transaction Lines The detailed line items for each transaction. LineID, TransactionID, ProductID, Quantity, Price, Discount
Shopping Cart Funnel A log of customer steps through the checkout process. SessionID, CustomerID, EventTimestamp, FunnelStep (e.g., ViewProduct, AddToCart, Checkout, Purchase)

Customer Data

Table Name Description Columns
Customer Master A list of all unique online customers. CustomerID, FullName, Email, JoinDate, City, Country
Customer Segmentation RFM (Recency, Frequency, Monetary) scores for customers. CustomerID, RecencyScore, FrequencyScore, MonetaryScore, RFM_Segment

Product Catalog

Table Name Description Columns
Product Master A list of all products available for sale online. ProductID, ProductName, Category, UnitPrice, StockQuantity
Product Reviews Customer feedback and ratings for products. ReviewID, CustomerID, ProductID, Rating, ReviewDate

Marketing & Promotions

Table Name Description Columns
Promotions Master A list of all discount codes and promotions. PromotionID, PromotionName, DiscountPercent, StartDate, EndDate
Traffic Attribution Links transactions to the marketing source that drove them. AttributionID, TransactionID, TrafficSource, CampaignID

Website & Logistics

Table Name Description Columns
Website Sessions A log of all visits to the e-commerce website. SessionID, CustomerID, VisitDate, Device, PagesViewed, SessionDuration_sec
Shipping Details Shipping information for completed orders. ShipmentID, TransactionID, Carrier, ShipDate, DeliveryDate, ShippingCost

How to Use This App

  1. Select a Table to Preview: Use the “Select Table to Preview” dropdown menu to choose the type of data you want to inspect.
  2. Specify the Number of Transactions: Enter the desired number of online transactions. This is the main driver; a higher number will generate more related records in all other tables.
  3. Configure Advanced Options (Optional): Click on Advanced Options to expand the menu.
    • Date Range: Select a Start Date and End Date to constrain the generated data to a specific time period.
    • Data Quality: Choose the quality of your dataset (Clean, Slightly Messy, or Very Messy).
  4. Generate the Data: Click the “Generate All Datasets” button. The application will process your request and display a preview of your selected table.
  5. Download Your Data:
    • Preview: To quickly download just the data shown in the preview table, click “Download Preview (CSV)” or “Download Preview (Excel)”.
    • All Related Tables: Click “Download All Tables (Excel)” to download a single Excel file with every table on a separate, clearly named sheet. This is the best option for relational analysis.

How to Use This Data

  • For Data Analysts & BI Professionals:
    • E-commerce Dashboards: Use the Online Transactions, Customers, and Products tables to build an executive-level dashboard in Tableau or Power BI.
    • Customer Segmentation: Use the Customer Segmentation table to analyze the behavior of high-value customers versus at-risk customers.
    • Practice SQL: Load the tables into a database and practice writing complex JOIN queries to link promotions to sales and calculate campaign ROI.
  • For Students & Educators:
    • Create realistic case studies for marketing, business analytics, and e-commerce courses.
    • Use the data as a foundation for assignments on calculating key e-commerce metrics like conversion rate, average order value (AOV), and customer lifetime value (CLV).

Feedback & Suggestions

This tool is built for you. If you have an idea for a new e-commerce table or an improvement, please leave your feedback in the comments section below.