Name: Ecommerce Sales & Returns Dataset
Creator: Analytics Engineering
Published: 2026-09-05T16:52:34.922Z
License: https://creativecommons.org/licenses/by/4.0/

Download this dataset

Choose the version that fits your project. You do not need to download every file.

Analysis-ready dataDownload Excel workbookStart with prepared data for reporting, charts, and spreadsheet analysis.ecommerce-sales-v1.0.0-analysis.xlsx · 7.9 MB
Source tables for SQLDownload SQLite databaseOpen every related source table in one portable SQLite database.ecommerce-sales-v1.0.0.sqlite · 30.6 MB
Related source tablesDownload source tablesPractice joins, transformations, and data modeling with documented CSV files.ecommerce-sales-v1.0.0-source-csv.zip · 7.6 MB
Data-cleaning challengeDownload cleaning challengeInvestigate intentionally introduced data problems and compare with the unchanged release.ecommerce-sales-v1.0.0-cleaning-challenge.zip · 181.2 KB

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Preview the data

Explore a sample before downloading. Select a table to see its columns, row count, relationships, and what each row represents.

Customers

One row represents: One row per customer.

Rows in this table: 20,000
Connects to: Orders

This preview shows a sample, not the full download.

Sample rows from customers. This preview is not the full download.
customer_id	created_at	country_code	acquisition_channel	customer_segment
C000001	2023-01-01	DE	organic	consumer
C000002	2023-01-01	US	paid_search	small_business
C000003	2023-01-01	US	paid_social	consumer
C000004	2023-01-01	US	affiliate	small_business
C000005	2023-01-01	GB	organic	consumer

Customers data dictionary

Check identifiers, dates, numeric fields, and missing values before writing a query or building a chart.

Column	What it means	Data type	Key or requirement
customer_id	Stable customer key.	text	Required
created_at	Customer registration date.	date	Required
country_code	ISO-like market code.	text	Required
acquisition_channel	First-touch acquisition channel.	text	Required
customer_segment	Consumer, small_business, or enterprise.	text	Required

View the full data dictionary

customers

One row represents: One row per customer.

Column	What it means	Data type	Key or requirement
customer_id	Stable customer key.	text	Required
created_at	Customer registration date.	date	Required
country_code	ISO-like market code.	text	Required
acquisition_channel	First-touch acquisition channel.	text	Required
customer_segment	Consumer, small_business, or enterprise.	text	Required

products

One row represents: One row per product.

Column	What it means	Data type	Key or requirement
product_id	Stable product key.	text	Required
sku	Unique stock keeping unit.	text	Required
product_name	Display name.	text	Required
category	Merchandising category.	text	Required
unit_cost	Standard cost in USD.	decimal	Required
list_price	List price in USD.	decimal	Required

orders

One row represents: One row per placed order.

Column	What it means	Data type	Key or requirement
order_id	Stable order key.	text	Required
customer_id	Ordering customer.	text	Required
ordered_at	UTC order timestamp.	datetime	Required
order_status	completed, partially_refunded, refunded, or cancelled.	text	Required
currency	Transaction currency; USD in v1.	text	Required
subtotal	Sum of line net amounts.	decimal	Required
tax_amount	Calculated sales tax.	decimal	Required
shipping_amount	Shipping charge.	decimal	Required
order_total	Subtotal plus tax and shipping.	decimal	Required

order_items

One row represents: One row per order line.

Column	What it means	Data type	Key or requirement
order_item_id	Stable line key.	text	Required
order_id	Parent order.	text	Required
product_id	Purchased product.	text	Required
quantity	Units purchased.	integer	Required
unit_price	Price per unit at purchase.	decimal	Required
discount_amount	Line discount in USD.	decimal	Required
line_total	Quantity times unit price less discount.	decimal	Required

payments

One row represents: One row per payment attempt.

Column	What it means	Data type	Key or requirement
payment_id	Stable payment key.	text	Required
order_id	Order being paid.	text	Required
paid_at	UTC payment attempt timestamp.	datetime	Required
payment_method	card, paypal, or bank_transfer.	text	Required
payment_status	succeeded or failed.	text	Required
amount	Attempted amount in USD.	decimal	Required

refunds

One row represents: One row per refund event.

Column	What it means	Data type	Key or requirement
refund_id	Stable refund key.	text	Required
payment_id	Refunded successful payment.	text	Required
order_id	Refunded order.	text	Required
refunded_at	UTC refund timestamp.	datetime	Required
refund_reason	Customer-facing reason category.	text	Required
amount	Refunded amount in USD.	decimal	Required

About the data

This synthetic dataset follows a fictional online store from customer orders through payments and refunds. The tables let you examine purchasing behavior while keeping orders, line items, and money movements separate.

Start with one question—such as how many customers place another order—then add related tables as your analysis becomes more detailed. Use the relationship guide to check whether a join changes the number of rows or repeats an amount.

What one row represents: Varies by source table
Data covers: Start: 2023-01-01 · End: 2025-12-31
Dataset version: 1.0.0
Release published: 2026-09-05

How the tables connect

Generated from the same relationship metadata as the table documentation.

Orderscustomer_idCustomerscustomer_id

many-to-one

Order itemsorder_idOrdersorder_id

many-to-one

Order itemsproduct_idProductsproduct_id

many-to-one

Paymentsorder_idOrdersorder_id

many-to-one

Refundspayment_idPaymentspayment_id

many-to-one

Refundsorder_idOrdersorder_id

many-to-one

Start your first analysis

Open a download, inspect the table, and answer one question. Each example identifies the file and dataset version it uses.

Start with Excel

Open the workbook and use its single prepared sheet. Build a monthly PivotTable from the order-level reporting data.

Download Excel

File used: ecommerce-sales-v1.0.0-analysis.xlsx
Dataset version: 1.0.0
File size: 7.9 MB

01Open "ecommerce-sales-v1.0.0-analysis.xlsx" and select the "Ecommerce Sales & Returns Datas" sheet.
02Check that ordered_at is a date and net_revenue is numeric.
03Place ordered_at in Rows, group it by month, and place net_revenue in Values as a sum.
04Label the result net payment value by order month: this prepared field includes tax and shipping and subtracts recorded refunds.

What you can build

Finished output from this release

Net payment value by order month

This checked query subtracts recorded refunds from successful payments and groups the result by the original order month. A later refund is attributed back to that order month. Because successful payments cover the order total, this measure includes tax and shipping; it is not payment-date cash flow or merchandise net sales.

order_month	net_payment_value
2023-01	1630650.17
2023-02	1537253.34
2023-03	1816861.47
2023-04	1683330.66
2023-05	1655911.04
2023-06	1581325.51

View the checked SQL

WITH paid AS (
  SELECT order_id, SUM(amount) AS paid_amount
  FROM payments
  WHERE payment_status = 'succeeded'
  GROUP BY order_id
),
refunded AS (
  SELECT order_id, SUM(amount) AS refunded_amount
  FROM refunds
  GROUP BY order_id
)
SELECT substr(o.ordered_at, 1, 7) AS order_month,
       ROUND(SUM(COALESCE(p.paid_amount, 0) - COALESCE(r.refunded_amount, 0)), 2) AS net_payment_value
FROM orders o
LEFT JOIN paid p USING (order_id)
LEFT JOIN refunded r USING (order_id)
GROUP BY order_month
ORDER BY order_month;

Order activity report

Compare order counts over time and by status. Explain how cancellations change the story before describing a period as stronger or weaker.

Repeat-purchase analysis

Identify customers with more than one qualifying order and measure the interval between purchases. State how you define a qualifying order.

Payment and refund reconciliation

Compare recorded payments and refunds with the associated orders. Explain which events belong in your calculation and which dates you use.

Ecommerce reporting model

Create separate models for orders, order items, customers, and money movements, then document how each one can be aggregated safely.

Questions about this dataset

Can I build a sales dashboard with this dataset?

Yes. Choose the prepared files for a starting point, or build your own reporting tables from the sources. Define your measures clearly so order value, collected payments, and refunds are not presented as interchangeable totals.

Why did my total increase after a join?

Check whether one record on the left matches several records on the right. An order can have multiple items, so repeating an order-level amount on those item rows can inflate a later sum.

Source, license, and versions

This dataset represents a fictional business. Its records and patterns were generated for education and testing. Use it to practice analytical methods, not to estimate real-world revenue, churn, conversion rates, or market conditions.

Before you interpret the results

This is synthetic educational data generated to model a fictional business. It does not represent a real company or establish industry benchmarks.
All monetary values are USD.
No PII is included.

Source, license, and version

Use the version number when sharing your work so others can reproduce it with the same files. Coverage dates describe the records; the release date describes when this package was published.

Maintainer: Analytics Engineering
License: CC BY 4.0
Version: 1.0.0
Published: 2026-09-05T16:52:34.922Z

Cite this dataset

Analytics Engineering. (2026). Ecommerce Sales & Returns Dataset (Version 1.0.0) [Data set]. https://www.analyticsengineering.com/datasets/ecommerce-sales.

Found an issue?

Tell us which dataset, version, and file you used, and what you expected to happen. A small example helps us investigate.

Report a dataset issue →

Keep learning

Start by keeping order activity, successful payments, and refunds separate. Define the measure and reporting date before joining or aggregating.

This exercise uses a smaller teaching example, not the full downloadable dataset.

Ecommerce Sales & Returns Dataset

Download this dataset

Preview the data

Customers

Customers data dictionary

customers

products

orders

order_items

payments

refunds

About the data

How the tables connect

Start your first analysis

Start with Excel

What you can build

Net payment value by order month

Order activity report

Repeat-purchase analysis

Payment and refund reconciliation

Ecommerce reporting model

Questions about this dataset

Source, license, and versions

Before you interpret the results

Source, license, and version

Cite this dataset

Found an issue?

Keep learning

Try the ecommerce net-sales exercise

Analyze ecommerce sales and refunds with SQL

Build the dimensional model