Online Retail Product Return Analytics

This dataset provides detailed, flat records of e-commerce product returns, including transaction details, customer demographics, product information, and reasons for return. It is designed for supply chain optimization, quality improvement, and customer experience analysis, enabling granular insights into return patterns and drivers across products and customer segments.

  • opened 44 times
  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Supply chain and logistics optimization based on return patterns

Sample rows

preview · 8 of 200 rows · all 22 columns
return_idstringproduct_categorystringcustomer_ageintegeris_first_returnbooleancustomer_countrystringorder_idstringproduct_idstringcustomer_idstringreturn_datedatepurchase_datedatetime_to_return_daysintegerreturn_reasonstringreturn_statusstringrefund_amountfloatproduct_namestringcustomer_emailstringcustomer_genderstringcustomer_citystringcustomer_statestringcustomer_postal_codestringreturn_methodstringproduct_pricefloat
RET100001electronics34trueUSAORD2000011PROD-ELEC0011CUS30001112023-04-192023-03-2921defectivecompleted449.99UltraHD Smart TV 55ineric.smith@usamail.commaleSan FranciscoCA94105mail449.99
RET100002apparel28trueUKORD2000012PROD-APP0021CUS30001222024-01-222024-01-157size issueapproved39.99Womens Summer Dress Lemma.jones@ukmail.co.ukfemaleLondonLondonSW1A1AAin-store39.99
RET100003home53falseGermanyORD2000013PROD-HOME0041CUS30001332022-11-122022-10-2518not as describedcompleted125Modern Floor Lamp Blacklucas.meyer@berlinmail.demaleBerlinBerlin10115mail125
RET100004apparel24trueAustraliaORD2000014PROD-APP0022CUS30001442023-09-142023-09-0311wrong itemapproved25.99Mens Cotton T-Shirt Mliam.turner@ausmail.commaleSydneyNSW2000mail25.99
RET100005electronics38falseChinaORD2000015PROD-ELEC0012CUS30001552023-07-212023-06-0150buyer remorsecompleted299Bluetooth Speaker X2olivia.chen@chinapost.cnfemaleShanghaiShanghai200120pickup299
RET100006apparel19trueUSAORD2000016PROD-APP0023CUS30001662024-02-012024-01-293size issuependingblankKids Hoodie Red XSmia.harris@usamail.comfemaleMiamiFL33101mail19.99
RET100007electronics41falseUSAORD2000017PROD-ELEC0013CUS30001772023-12-152023-12-105defectivecompleted1299.99Gaming Laptop 16GB RAMbenjamin.davis@usamail.commaleChicagoIL60601mail1299.99
RET100008home66falseFranceORD2000018PROD-HOME0042CUS30001882022-09-022022-08-2211late deliveryrejected0Ceramic Vase Blue Largemarie.dubois@frmail.frfemaleParisIle-de-France75001mail54.99

What the 200 rows show

from the 200-row sample

Home (product category) stands out: mean customer_age is 62.2, against 27.9 for the rest.

  • 54%is_first_return = true
  • 33median customer_age
  • 2customer genders
  • 3return methods
  • 4return statuses
  • 7return reasons
Mean customer_age by product_category200 rows
0408031.5electronics68 rows24.1apparel66 rows62.2home66 rows
customer_age200 rows, in bands of 10
04080127147826239311060100customer_age →

Median 33, from 16 to 99.

customer_country200 rows · top 10 of 12 values
  1. UK66
  2. USA46
  3. Germany19
  4. Canada18
  5. Australia10
  6. China10
  7. Ireland10
  8. Spain8
  9. France6
  10. Portugal3
22 columns by typefrom the column list below
  • string 15
  • integer 2
  • float 2
  • date 2
  • boolean 1

Columns

22 columns in four groups
blueprint · 22 columns
columntypedescriptionexample
Text 15 columns
return_idstringUnique identifier for each product return transactionuniqueRET100001
order_idstringUnique identifier for the original purchase order associated with the returnORD2000011
product_idstringUnique identifier for the returned productPROD-ELEC0011
customer_idstringUnique identifier for the customer initiating the returnCUS3000111
return_reasonstringReason for the product return (e.g., defective, not as described, wrong item, buyer remorse)7 valuesdefective
return_statusstringCurrent status of the return process (e.g., pending, approved, rejected, completed)pending · approved · rejected · completedcompleted
product_categorystringCategory of the returned product (e.g., electronics, apparel, home)3 categorieselectronics
product_namestringName or description of the returned productUltraHD Smart TV 55in
customer_emailstringEmail address of the customereric.smith@usamail.com
customer_genderstringGender of the customermale · female · other · prefer not to say · optionalmale
customer_citystringCity of the customer's shipping addressoptionalSan Francisco
customer_statestringState of the customer's shipping addressoptionalCA
customer_postal_codestringPostal code of the customer's shipping addressoptional94105
customer_countrystringCountry of the customer's shipping address12 countries · optionalUSA
return_methodstringMethod used for returning the product (e.g., mail, in-store, pickup)mail · in-store · pickup · other · optionalmail
Numbers 4 columns
time_to_return_daysintegerNumber of days between purchase and return0 or more21
refund_amountfloatAmount refunded to the customer for the returned product0 or more · optional449.99
customer_ageintegerAge of the customer at the time of return0 to 120 · optional34
product_pricefloatOriginal price of the returned product0 or more · optional449.99
Dates and times 2 columns
return_datedateDate when the product return was initiated2023-04-19
purchase_datedateDate when the product was originally purchased2023-03-29
True or false 1 column
is_first_returnbooleanIndicates if this is the customer's first returnoptionaltrue

Use it for

  • is first return54%108 of 200 rowsmean customer age by …31.5electr…24.1apparel62.2home

    An e-commerce dashboard

    The is_first_return rate, customer_age by product_category and a breakdown of customer_country. Excel, Power BI or Tableau.

  • Why do the 66 home rows have a mean customer_age of 62.2?

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

Not quite right?

Make it yours.

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This dataset200 rows22 columns
Yours10,000 rows22 columnscustomer_city: UK only

blueprint · online-retail-product-return-analytics

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 22 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
  • Link returns to product and customer segments
  • Capture reason, return method, and time lag
  • Flag frequent returners
  • Synthetic customer anonymization
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
online-retail-product-return-analytics

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