E-Commerce Returns Automation Tracker

This dataset provides a comprehensive, step-by-step record of e-commerce product returns, tracking workflow automation, ESG (Environmental, Social, Governance) touchpoints, carbon impact, and operational efficiency metrics. It enables deep analysis of return processes, bottleneck identification, and sustainability initiatives, supporting data-driven improvements in reverse logistics and supply chain management.

  • last updated 2 Feb 2026
  • by GoMask
The brief that made it

Return process optimization and bottleneck analysis

Sample rows

preview · 8 of 85 rows · all 23 columns
return_idstringroute_typestringcarbon_impact_kgco2efloatis_automatedbooleanprocessing_center_statestringorder_idstringcustomer_idstringproduct_idstringproduct_namestringreturn_reasonstringreturn_initiated_datedatetimereturn_received_datedatetimecurrent_statusstringworkflow_stepstringesg_touchpointstringprocessing_time_hoursfloatbottleneck_flagbooleanbottleneck_reasonstringrefund_amountfloatrefund_issued_datedatetimeprocessing_center_idstringprocessing_center_citystringprocessing_center_countrystring
RET10001restock2.4trueTXORD5001CUST203PRD9007Wireless EarbudsDefective item2024-03-20T12:41:00Z2024-03-22T09:23:00Zreceiveditem received at centercarbon_tracking44.7falseblank49.992024-03-23T15:18:00ZPC-102DallasUSA
RET10002recycle3.7falseMIORD5022CUST178PRD9003Bluetooth SpeakerUnwanted gift2024-04-02T09:28:00Z2024-04-05T13:44:00Zinspectedinspection completerecycling76.3trueInspection queue backlog0blankPC-110DetroitUSA
RET10003restock2.2trueFLORD5008CUST221PRD9009Fitness TrackerNot as described2024-03-17T17:45:00Z2024-03-20T08:10:00Zapprovedapproval processedcarbon_tracking62.4falseblank24.952024-03-20T18:22:00ZPC-103MiamiUSA
RET10004restock1.1trueAZORD5012CUST199PRD9011Yoga MatWrong color2024-03-23T10:04:00Z2024-03-25T15:33:00Zrestockeditem restockednone53.8falseblank15.992024-03-25T17:02:00ZPC-105PhoenixUSA
RET10005restock3trueTXORD5035CUST175PRD9001SmartwatchDid not fit2024-04-14T11:12:00Z2024-04-16T10:38:00Zreceiveditem received at centercarbon_tracking47.4falseblank109.992024-04-16T15:44:00ZPC-102DallasUSA
RET10006restock0.9trueCAORD5041CUST209PRD9017Phone CaseWrong item sent2024-04-18T16:50:00Z2024-04-21T14:10:00Zapprovedapproval processednone69.3falseblank9.992024-04-21T15:33:00ZPC-109San DiegoUSA
RET10007restock2.8trueFLORD5052CUST214PRD9018Running ShoesSize too small2024-04-25T09:15:00Zblankin_transitpickup scheduledcarbon_tracking24.1falseblank0blankPC-103MiamiUSA
RET10008landfill4.2falseWAORD5064CUST193PRD9004Laptop BackpackDamaged item2024-05-01T13:27:00Z2024-05-03T16:20:00Zinspectedinspection startedlandfill_avoidance51.8trueManual inspection required39.992024-05-04T10:16:00ZPC-106SeattleUSA

What the 85 rows show

from the 85-row sample

Restock (route type) stands out: 58 of its 64 rows have is_automated = true, against 8 of 21 for the rest.

  • 78%is_automated = true
  • 1.6median carbon_impact_kgco2e
  • 5esg touchpoints
  • 10current statuses
  • 17workflow steps
  • 44.9median processing_time_hours
Is automated rate by route_typeis_automated = true
0%50%100%91%restock58 of 6454%recycle7 of 1320%donate1 of 50%landfill0 of 3
carbon_impact_kgco2e85 rows, in bands of 1
01530252823621036carbon_impact_kgco2e →

Median 1.6, from 0.20 to 5.2.

processing_center_state85 rows · top 10 of 31 values
  1. TX7
  2. FL7
  3. AZ7
  4. IL7
  5. MI6
  6. WA6
  7. CO6
  8. CA5
  9. MA4
  10. NC4
23 columns by typefrom the column list below
  • string 15
  • float 3
  • datetime 3
  • boolean 2

Columns

23 columns in four groups
blueprint · 23 columns
columntypedescriptionexample
Text 15 columns
return_idstringUnique identifier for each return transactionuniqueRET10001
order_idstringIdentifier for the original order associated with the returnORD5001
customer_idstringUnique identifier for the customer initiating the returnCUST203
product_idstringUnique identifier for the returned productPRD9007
product_namestringName of the returned productWireless Earbuds
return_reasonstringReason provided by the customer for returning the itemDefective item
current_statusstringCurrent status of the return in the workflow10 valuesreceived
workflow_stepstringCurrent automation step or process stage (e.g., label generated, pickup scheduled, inspection, etc.)item received at center
esg_touchpointstringESG (Environmental, Social, Governance) touchpoint relevant to this step (e.g., carbon tracking, recycling, donation, landfill avoidance)5 values · optionalcarbon_tracking
route_typestringFinal disposition route for the returned item (e.g., restock, recycle, donate, landfill)restock · recycle · donate · landfillrestock
bottleneck_reasonstringDescription of the bottleneck cause, if any7 reasons · optionalInspection queue backlog
processing_center_idstringIdentifier for the processing center handling the returnoptionalPC-102
processing_center_citystringCity of the processing centeroptionalDallas
processing_center_statestringState or region of the processing centeroptionalTX
processing_center_countrystringCountry of the processing centeroptionalUSA
Numbers 3 columns
carbon_impact_kgco2efloatEstimated carbon impact of this return in kilograms of CO2 equivalent0 or more · optional2.4
processing_time_hoursfloatTotal time taken (in hours) to process the return from initiation to current status0 or more · optional44.7
refund_amountfloatAmount refunded to the customer for this return0 or more · optional49.99
Dates and times 3 columns
return_initiated_datedatetimeTimestamp when the return was initiated by the customer2024-03-20T12:41:00Z
return_received_datedatetimeTimestamp when the returned item was received at the processing centeroptional2024-03-22T09:23:00Z
refund_issued_datedatetimeTimestamp when the refund was issuedoptional2024-03-23T15:18:00Z
True or false 2 columns
is_automatedbooleanIndicates whether the current workflow step was automatedtrue
bottleneck_flagbooleanIndicates if a bottleneck was detected at any workflow stepfalse

Use it for

  • is automated78%66 of 85 rowsmean carbon impact kg…1.6rest…2.0recy…1.3dona…3.2land…

    An e-commerce dashboard

    The is_automated rate, carbon_impact_kgco2e by route_type and a breakdown of processing_center_state. Excel, Power BI or Tableau.

  • Why do 66 of 85 rows have is_automated = true?

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

Not quite right?

Make it yours.

Same 23 columns, your size and your rules. See 20 rows before you pay.

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This dataset85 rows23 columns
Yours10,000 rows23 columnsprocessing_center_city: UK only

blueprint · e-commerce-returns-automation-tracker

Behind this dataset

Same schema. As many rows as you need.

These 85 rows came out of a blueprint — 23 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 return event must include item category, return reason, and automation step status.
  • Carbon impact is calculated based on transport distance and item type.
  • Returns can be restocked, recycled, or disposed; each outcome must be tracked.
  • Workflow automation outcome must be labeled as 'manual', 'semi-automated', or 'fully automated'.
  • Dates for return initiation, processing, and closure must be recorded.
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-tracker

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