E-Commerce Purchase Sequences Dataset

This dataset provides detailed, timestamped records of e-commerce user sessions, capturing every step of the shopper journey from browsing and searching to cart actions and purchases. Each event is linked to session, user, and product information, enabling comprehensive analysis for personalization, recommendation systems, and user behavior modeling. The dataset is ideal for developing and benchmarking algorithms that require sequential purchase and interaction data.

  • last updated 22 Jul 2025
  • by GoMask
The brief that made it

Developing and evaluating product recommendation algorithms

Sample rows

preview · 8 of 200 rows · all 24 columns
session_idstringdevice_typestringpricefloatproduct_categorystringuser_idstringsession_start_timedatetimesession_end_timedatetimebrowserstringip_addressstringcountrystringevent_timestampdatetimeevent_typestringpage_urlstringproduct_idstringquantityintegerorder_idstringorder_totalfloatpayment_methodstringshipping_street_addressstringshipping_citystringshipping_statestringshipping_postal_codestringshipping_countrystringsearch_querystring
sess_00001desktopblankblankuser_1012023-04-01T09:08:21Z2023-04-01T10:21:15ZChrome192.168.1.11US2023-04-01T09:08:33Zlogin/loginblankblankblankblankblankblankblankblankblankblankblank
sess_00001desktopblankblankuser_1012023-04-01T09:08:21Z2023-04-01T10:21:15ZChrome192.168.1.11US2023-04-01T09:09:05Zsearch/searchblankblankblankblankblankblankblankblankblankblankwireless headphones
sess_00001desktop129.99Electronicsuser_1012023-04-01T09:08:21Z2023-04-01T10:21:15ZChrome192.168.1.11US2023-04-01T09:10:10Zadd_to_cart/product/elec104elec1041blankblankblankblankblankblankblankblankblank
sess_00001desktop129.99Electronicsuser_1012023-04-01T09:08:21Z2023-04-01T10:21:15ZChrome192.168.1.11US2023-04-01T10:20:00Zpurchase/checkoutelec1041ord_1001129.99credit_card123 Maple AveBostonMA02118USblank
sess_00001desktopblankblankuser_1012023-04-01T09:08:21Z2023-04-01T10:21:15ZChrome192.168.1.11US2023-04-01T10:21:12Zlogout/logoutblankblankblankblankblankblankblankblankblankblankblank
sess_00002mobileblankblankuser_2352023-04-02T13:20:00Z2023-04-02T14:03:20ZSafari10.0.0.21AU2023-04-02T13:20:33Zsearch/searchblankblankblankblankblankblankblankblankblankblankrunning shoes
sess_00002mobile79.99Fashionuser_2352023-04-02T13:20:00Z2023-04-02T14:03:20ZSafari10.0.0.21AU2023-04-02T13:22:01Zadd_to_cart/product/fash007fash0072blankblankblankblankblankblankblankblankblank
sess_00002mobile79.99Fashionuser_2352023-04-02T13:20:00Z2023-04-02T14:03:20ZSafari10.0.0.21AU2023-04-02T14:00:00Zpurchase/checkoutfash0072ord_1002159.98paypal55 George StSydneyNSW2000AUblank

What the 200 rows show

from the 200-row sample
  • 40.0median price
  • 4payment methods
  • 7event types
  • 10browsers
  • 13countries
  • 1median quantity
device_type200 rows by value
0459085desktop43%58mobile29%48tablet24%9other5%
price93 rows, in bands of 2,000
045909010100108,00014kprice →

Median 40.0, from 3.0 to 13,000.

product_category93 rows with a value · 107 left blank
  1. Electronics17
  2. Fashion13
  3. Toys9
  4. Books9
  5. Sports9
  6. Groceries8
  7. Accessories8
  8. Garden7
  9. Appliances7
  10. Vintage6
24 columns by typefrom the column list below
  • string 18
  • integer 1
  • float 2
  • datetime 3

Columns

24 columns in three groups
blueprint · 24 columns
columntypedescriptionexample
Text 18 columns
session_idstringUnique identifier for each web session.uniquesess_00001
user_idstringUnique identifier for the shopper (anonymized if necessary).user_101
device_typestringType of device used during the session (e.g., desktop, mobile, tablet).desktop · mobile · tablet · other · optionaldesktop
browserstringWeb browser used during the session.10 browsers · optionalChrome
ip_addressstringIP address of the user during the session.optional192.168.1.11
countrystringCountry from which the session originated.optionalUS
event_typestringType of event (e.g., page_view, add_to_cart, purchase, search, logout).8 valueslogin
page_urlstringURL of the page where the event occurred.optional/login
product_idstringUnique identifier for the product involved in the event (if applicable).optionalelec104
product_categorystringCategory of the product involved in the event (if applicable).10 categories · optionalElectronics
order_idstringUnique identifier for the purchase order (if event_type is purchase).optionalord_1001
payment_methodstringPayment method used for the purchase (if applicable).6 values · optionalcredit_card
shipping_street_addressstringStreet address for shipping (if event_type is purchase).optional123 Maple Ave
shipping_citystringCity for shipping (if event_type is purchase).optionalBoston
shipping_statestringState or province for shipping (if event_type is purchase).optionalMA
shipping_postal_codestringPostal code for shipping (if event_type is purchase).optional02118
shipping_countrystringCountry for shipping (if event_type is purchase).11 countries · optionalUS
search_querystringSearch query entered by the user (if event_type is search).optionalwireless headphones
Numbers 3 columns
quantityintegerQuantity of the product involved in the event (if applicable).1 or more · optional1
pricefloatPrice of the product at the time of the event (if applicable).0 or more · optional129.99
order_totalfloatTotal amount for the order (if event_type is purchase).0 or more · optional129.99
Dates and times 3 columns
session_start_timedatetimeTimestamp when the web session started.2023-04-01T09:08:21Z
session_end_timedatetimeTimestamp when the web session ended.2023-04-01T10:21:15Z
event_timestampdatetimeTimestamp of the event within the session.2023-04-01T09:08:33Z

Use it for

  • median price40.093 rowsmean price by device …342.8desk…58.6mobi…169.8tabl…2.9kother

    An e-commerce dashboard

    Price by device_type and a breakdown of product_category. Excel, Power BI or Tableau.

  • What pattern can you find in 200 rows?

    A class exercise

    Hand out the rows and one question. Everyone works from the same 200 rows.

  • A software demo

    Believable sessions with user_id, session_start_time and session_end_time to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 24 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows24 columns
Yours1,000 rows24 columnsip_address: UK only

blueprint · e-commerce-purchase-sequences-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 24 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Each session linked to a unique customer ID
  • Products purchaseable within multiple categories
  • Cart abandonments are included as events
  • Timestamps reflect realistic shopping hours
Rows
Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
e-commerce-purchase-sequences-dataset

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