E-commerce Returns Automation Workflow Log

This dataset provides a detailed log of e-commerce return events and the associated automation workflow steps, including timestamps, actors, outcomes, and exception handling. It enables supply chain managers and process engineers to analyze return processing efficiency, identify manual interventions, and optimize automation strategies for reduced costs and improved customer experience.

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

Analyzing bottlenecks and delays in the e-commerce returns process

Sample rows

preview · 8 of 120 rows · all 21 columns
return_event_idstringreturn_location_countrystringstep_duration_secondsfloatreturn_location_citystringorder_idstringcustomer_idstringproduct_idstringreturn_reasonstringreturn_initiated_atdatetimereturn_statusstringworkflow_stepstringstep_executed_atdatetimestep_typestringstep_actor_idstringstep_outcomestringexception_detailsstringreturn_location_streetstringreturn_location_statestringreturn_location_postal_codestringrefund_amountfloatrefund_issued_atdatetime
RET-10001blank12.5blankORD-30021CUST-2011PRD-5021Size too small2024-05-03T10:23:11initiatedlabel_generated2024-05-03T10:24:00automatedsystem_rmasuccessblankblankblankblankblankblank
RET-10002blank24.8blankORD-30022CUST-2012PRD-5022Wrong color2024-05-07T14:34:55pending_pickuppickup_scheduled2024-05-07T14:36:10automatedsystem_pickupsuccessblankblankblankblankblankblank
RET-10003USA1800AtlantaORD-30023CUST-2013PRD-5023Defective item2024-05-10T09:40:23in_transitpickup_confirmed2024-05-10T10:10:05manualEMP-0012successblank535 Commerce BlvdGA30318blankblank
RET-10004USA1499.7Los AngelesORD-30024CUST-2014PRD-5024Item not as described2024-05-15T11:20:15receivedpackage_received2024-05-15T11:45:00automatedsystem_warehousesuccessblank1200 W 7th StCA90017blankblank
RET-10005USA1678.1San FranciscoORD-30025CUST-2015PRD-5025Changed mind2024-05-18T15:32:08inspectedmanual_review2024-05-18T16:00:00manualEMP-0045successblank420 Market StCA94111blankblank
RET-10006USA1852New YorkORD-30026CUST-2016PRD-5026Duplicate order2024-05-20T13:14:25approvedrefund_processed2024-05-20T13:45:00automatedsystem_refundsuccessblank10 Hudson YardsNY1000178.992024-05-20T14:10:00
RET-10007USA1799.5PhoenixORD-30027CUST-2017PRD-5027Incorrect item shipped2024-05-22T08:45:13completedreturn_completed2024-05-22T09:15:00automatedsystem_rmasuccessblank2200 N Central AveAZ8500449.52024-05-22T09:30:00
RET-10008blank8.2blankORD-30028CUST-2018PRD-5028Wrong size2024-05-23T17:05:49initiatedlabel_generated2024-05-23T17:06:00automatedsystem_rmasuccessblankblankblankblankblankblank

What the 120 rows show

from the 120-row sample

Canada (return location country) stands out: mean step_duration_seconds is 59.8, against 929.6 for the rest.

  • 42.1median step_duration_seconds
  • 2step types
  • 3step outcomes
  • 8return statuses
  • 15workflow steps
  • 20return location states
Mean step_duration_seconds by return_location_country98 rows
05001,000929.6USA63 rows59.8Canada35 rows
step_duration_seconds120 rows, in bands of 250
04590841003911101101,2502,500step_duration_seconds →

Median 42.1, from 5.6 to 2,378.

return_location_city98 rows with a value · 22 left blank
  1. Quebec City13
  2. Phoenix12
  3. Montreal11
  4. New York7
  5. Atlanta6
  6. San Francisco6
  7. Ottawa6
  8. Dallas5
  9. Toronto5
  10. Los Angeles4
21 columns by typefrom the column list below
  • string 16
  • float 2
  • datetime 3

Columns

21 columns in three groups
blueprint · 21 columns
columntypedescriptionexample
Text 16 columns
return_event_idstringUnique identifier for each return event in the workflow log.uniqueRET-10001
order_idstringUnique identifier for the original e-commerce order associated with the return.ORD-30021
customer_idstringUnique identifier for the customer initiating the return.CUST-2011
product_idstringUnique identifier for the product being returned.PRD-5021
return_reasonstringReason provided by the customer for returning the product.optionalSize too small
return_statusstringCurrent status of the return event in the workflow.8 valuesinitiated
workflow_stepstringName of the automation or manual step executed (e.g., 'label_generated', 'pickup_scheduled', 'manual_review').label_generated
step_typestringIndicates whether the step was automated or manual.automated · manualautomated
step_actor_idstringIdentifier for the system, user, or employee who executed the step (system name or employee ID).optionalsystem_rma
step_outcomestringOutcome of the workflow step (e.g., 'success', 'failure', 'exception').success · failure · exception · optionalsuccess
exception_detailsstringDetailed description of any exception or error encountered during the workflow step.optionalPayment gateway timeout.
return_location_streetstringStreet address where the returned item was received or processed.optional535 Commerce Blvd
return_location_citystringCity where the returned item was received or processed.optionalAtlanta
return_location_statestringState or province where the returned item was received or processed.optionalGA
return_location_postal_codestringPostal code for the return processing location.optional30318
return_location_countrystringCountry where the return was processed.2 countries · optionalUSA
Numbers 2 columns
step_duration_secondsfloatTime taken (in seconds) to complete this workflow step.0 or more · optional12.5
refund_amountfloatAmount refunded to the customer for this return event.0 or more · optional78.99
Dates and times 3 columns
return_initiated_atdatetimeTimestamp when the return process was initiated.2024-05-03T10:23:11
step_executed_atdatetimeTimestamp when this workflow step was executed.2024-05-03T10:24:00
refund_issued_atdatetimeTimestamp when the refund was issued.optional2024-05-20T14:10:00

Use it for

  • median step du…42.1120 rowsmean step duration se…929.6USA59.8Canada

    An e-commerce dashboard

    Step_duration_seconds by return_location_country and a breakdown of return_location_city. Excel, Power BI or Tableau.

  • Why do the 35 Canada rows have a mean step_duration_seconds of 59.8?

    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 return events with order_id, customer_id and product_id to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 21 columns, your size and your rules. See 20 rows before you pay.

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This dataset120 rows21 columns
Yours1,000 rows21 columnsreturn_location_street: UK only

blueprint · e-commerce-returns-automation-workflow-log

Behind this dataset

Same schema. As many rows as you need.

These 120 rows came out of a blueprint — 21 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 row represents a unique product return event.
  • Workflow stage must map to predefined automation steps (e.g., initiated, validated, approved, processed, shipped).
  • Manual intervention flag is required if automation fails or exceptions occur.
  • Timestamp sequence must reflect actual event flow.
  • Return reason codes must use a standardized taxonomy.
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
e-commerce-returns-automation-workflow-log

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