E-Commerce Cart Abandonment Patterns

This dataset provides detailed records of e-commerce shopping cart creation, abandonment events, and recovery outcomes, including user segments, device types, and recovery methods. It enables retailers to analyze cart abandonment patterns, identify key reasons for lost sales, and measure the effectiveness of recovery campaigns across different customer groups and channels.

  • opened 7 times
  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Analyzing cart abandonment rates and patterns across user segments

Sample rows

preview · 8 of 200 rows · all 18 columns
cart_idstringuser_segmentstringnum_itemsintegercart_abandonedbooleanbrowserstringuser_idstringcart_created_atdatetimecart_abandoned_atdatetimecart_recoveredbooleancart_recovered_atdatetimerecovery_methodstringtotal_cart_valuefloatdevice_typestringcountrystringsession_idstringabandonment_reasonstringrecovery_successfulbooleanrecovered_order_idstring
CART10001VIP14falseChromeUSR5012024-05-01T09:12:34Zblankblankblanknone3200.5desktopUnited StatesSESS_A001blankblankblank
CART10002guest2trueSafariUSR5022024-05-01T10:15:48Z2024-05-01T10:27:12Zfalseblanknone24.95mobileBrazilSESS_A002high shipping costfalseblank
CART10003new1trueEdgeUSR5032024-05-01T11:23:06Z2024-05-01T11:28:22Ztrue2024-05-01T15:19:17Zretargeting_ad9.99desktopSpainSESS_A003account issuetrueORD29001
CART10004returning6falseSafariUSR5042024-05-03T13:48:41Zblankblankblanknone4880tabletCanadaSESS_A004blankblankblank
CART10005VIP18trueBraveUSR5052024-05-04T09:36:14Z2024-05-04T09:40:02Ztrue2024-05-04T13:02:55Zemail5500.8desktopGermanySESS_A005technical glitchtrueORD29002
CART10006guest3trueSamsung InternetUSR5062024-05-05T14:12:07Z2024-05-05T14:17:29Zfalseblanknone32.99mobileNigeriaSESS_A006payment issuefalseblank
CART10007returning3trueEdgeUSR5072024-05-06T15:41:55Z2024-05-06T15:53:08Ztrue2024-05-07T08:37:21Zsms80tabletUAESESS_A007timeouttrueORD29003
CART10008other0falseblankUSR5082024-05-07T17:29:42Zblankblankblanknone0otherSingaporeSESS_A008blankblankblank

What the 200 rows show

from the 200-row sample

VIP (user segment) stands out: mean num_items is 15.2, against 2.6 for the rest.

  • 56%cart_abandoned = true
  • 2.5median num_items
  • 4device types
  • 5recovery methods
  • 7abandonment reasons
  • 18countries
Mean num_items by user_segment200 rows
010203.8new37 rows4.0return…40 rows15.2VIP43 rows1.9guest44 rows0.69other36 rows
num_items200 rows, in bands of 2.5
050100100273071044477012.525num_items →

Median 2.5, from 0 to 25.

browser174 rows with a value · 26 left blank
  1. Chrome62
  2. Safari33
  3. Edge27
  4. Opera11
  5. UC Browser10
  6. Firefox9
  7. Internet Explorer8
  8. Brave7
  9. Samsung Internet7
18 columns by typefrom the column list below
  • string 10
  • integer 1
  • float 1
  • datetime 3
  • boolean 3

Columns

18 columns in four groups
blueprint · 18 columns
columntypedescriptionexample
Text 10 columns
cart_idstringUnique identifier for each shopping cart instanceuniqueCART10001
user_idstringUnique identifier for the user who created the cartUSR501
user_segmentstringSegment or category of the user (e.g., new, returning, VIP)new · returning · VIP · guest · other · optionalVIP
recovery_methodstringMethod used to recover the cart (e.g., email, SMS, retargeting ad)6 values · optionalnone
device_typestringType of device used to create the cart (e.g., desktop, mobile, tablet)desktop · mobile · tablet · other · optionaldesktop
browserstringBrowser used by the user when creating the cart9 browsers · optionalChrome
countrystringCountry from which the cart was createdoptionalUnited States
session_idstringIdentifier for the user's session during cart creationoptionalSESS_A001
abandonment_reasonstringReason for cart abandonment if captured (e.g., high shipping cost, payment issue)7 reasons · optionalhigh shipping cost
recovered_order_idstringOrder ID generated if the cart was recovered and converted to a saleoptionalORD29001
Numbers 2 columns
total_cart_valuefloatTotal monetary value of the cart at the time of abandonment or checkout0 or more3200.5
num_itemsintegerTotal number of items in the cart0 or more14
Dates and times 3 columns
cart_created_atdatetimeTimestamp when the cart was created2024-05-01T09:12:34Z
cart_abandoned_atdatetimeTimestamp when the cart was abandoned (null if not abandoned)optional2024-05-01T10:27:12Z
cart_recovered_atdatetimeTimestamp when the cart was recovered (null if not recovered)optional2024-05-01T15:19:17Z
True or false 3 columns
cart_abandonedbooleanIndicates whether the cart was abandoned (true) or completed (false)false
cart_recoveredbooleanIndicates whether the abandoned cart was later recovered and converted to a saleoptionalfalse
recovery_successfulbooleanIndicates if the recovery attempt resulted in a completed purchaseoptionalfalse

Use it for

  • cart abandoned56%112 of 200 rowsmean num items by use…3.8new4.0retu…15.2VIP1.9guest

    An e-commerce dashboard

    The cart_abandoned rate, num_items by user_segment and a breakdown of browser. Excel, Power BI or Tableau.

  • Why do the 43 VIP rows have a mean num_items of 15.2?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable carts with user_id, user_segment and cart_created_at to fill a screen in front of a buyer.

Not quite right?

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This dataset200 rows18 columns
Yours1,000 rows18 columnscountry: UK only

blueprint · e-commerce-cart-abandonment-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 18 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
  • Cart ID and user session tracked
  • Product details and price logged
  • Abandonment event flagged
  • Recovery conversion outcome annotated
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
e-commerce-cart-abandonment-patterns

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