• Other
  • 16 columns
  • 200 rows
  • 8 formats

Retail Payment Method Adoption

This dataset provides granular transaction-level insights into retail payment method adoption, capturing details such as payment type, provider, transaction value, location, and status. It enables comprehensive analysis of trends in cash, card, mobile, and crypto usage across stores and customer segments, supporting retail analytics, fraud detection, and strategic decision-making.

  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

Analyzing trends in payment method adoption across regions and time

Sample rows

preview · 8 of 200 rows · all 16 columns
transaction_idstringpayment_methodstringtransaction_amountfloatis_onlinebooleanpayment_providerstringtransaction_datetimedatetimestore_idstringstore_namestringstore_citystringstore_statestringstore_countrystringcustomer_idstringcurrencystringis_contactlessbooleantransaction_statusstringproduct_categorystring
TXN000001card146.75falseVisa2023-12-01T14:23:10ZS001Metro MarketNew YorkNYUSACUST000001USDtruecompletedgroceries
TXN000002mobile729.49falseApple Pay2023-12-02T21:17:46ZS002Tech HavenLondonLNDUKCUST000002GBPtruecompletedelectronics
TXN000003card87.35falseMastercard2023-12-03T09:04:35ZS003City GrocerBerlinBEGermanyCUST000003EURfalsecompletedgroceries
TXN000004card212.99falseAmex2023-12-03T20:50:12ZS004Apparel CentralSan FranciscoCAUSACUST000004USDfalsecompletedapparel
TXN000005cash24.99falseblank2023-12-04T07:51:03ZS005Book NookTorontoONCanadablankCADfalsecompletedbooks
TXN000006cash1780falseblank2023-12-05T16:36:19ZS006Quick MartTokyo13JapanblankJPYfalsecompletedconvenience
TXN000007mobile659.99trueGoogle Pay2023-12-06T19:12:55ZS007Digital WorldParisIDFFranceCUST000005EURtruecompletedelectronics
TXN000008card1249.95falseVisa Debit2023-12-07T23:58:14ZS008Urban SportsSydneyNSWAustraliaCUST000006AUDtruecompletedsports

What the 200 rows show

from the 200-row sample

Crypto (payment method) stands out: 17 of its 17 rows have is_online = true, against 38 of 183 for the rest.

  • 28%is_online = true
  • 110.0median transaction_amount
  • 4transaction statuses
  • 11product categories
  • 12currencies
  • 13store countries
Is online rate by payment_methodis_online = true
0%50%100%0%cash0 of 2922%card20 of 9229%mobile18 of 62100%crypto17 of 17
transaction_amount200 rows, in bands of 500
07515014824966201402,5004,500transaction_amount →

Median 110.0, from 0.00 to 4,100.

payment_provider171 rows with a value · 29 left blank
  1. Visa49
  2. Apple Pay22
  3. Mastercard22
  4. Google Pay17
  5. Amex13
  6. Bitcoin12
  7. Samsung Pay11
  8. PayPay7
  9. Line Pay5
  10. Ethereum5
16 columns by typefrom the column list below
  • string 12
  • float 1
  • datetime 1
  • boolean 2

Columns

16 columns in four groups
blueprint · 16 columns
columntypedescriptionexample
Text 12 columns
transaction_idstringA unique identifier for each retail transaction.uniqueTXN000001
store_idstringA unique identifier for the retail store where the transaction took place.S001
store_namestringThe name of the retail store.optionalMetro Market
store_citystringThe city where the store is located.optionalNew York
store_statestringThe state or province where the store is located.optionalNY
store_countrystringThe country where the store is located.optionalUSA
customer_idstringA unique identifier for the customer making the transaction. Can be null for anonymous cash transactions.optionalCUST000001
payment_methodstringThe payment method used for the transaction.cash · card · mobile · cryptocard
payment_providerstringThe payment provider or network used (e.g., Visa, Mastercard, Apple Pay, Bitcoin, etc.).12 providers · optionalVisa
currencystringThe ISO 4217 currency code for the transaction (e.g., USD, EUR, BTC).12 currenciesUSD
transaction_statusstringThe status of the transaction (e.g., completed, failed, refunded, pending).completed · failed · refunded · pendingcompleted
product_categorystringThe primary product category involved in the transaction (e.g., groceries, electronics, apparel).11 categories · optionalgroceries
Numbers 1 column
transaction_amountfloatThe total monetary amount of the transaction in the store's local currency.0 or more146.75
Dates and times 1 column
transaction_datetimedatetimeThe date and time when the transaction occurred.2023-12-01T14:23:10Z
True or false 2 columns
is_contactlessbooleanIndicates whether the payment was made using a contactless method (e.g., tap-to-pay, NFC).optionaltrue
is_onlinebooleanIndicates whether the transaction was made online (true) or in-store (false).optionalfalse

Use it for

  • is online28%55 of 200 rowsmean transaction amou…109.5cash481.9card745.4mobi…0.02cryp…

    A dashboard

    The is_online rate, transaction_amount by payment_method and a breakdown of payment_provider. Excel, Power BI or Tableau.

  • Why do 55 of 200 rows have is_online = 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 transactions with transaction_datetime, store_id and store_name to fill a screen in front of a buyer.

Not quite right?

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This dataset200 rows16 columns
Yours10,000 rows16 columnsstore_city: UK only

blueprint · retail-payment-method-adoption

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 16 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 transaction logs payment type and amount
  • Store, time, and customer segment included
  • Innovative payment methods increase over time
  • Outliers: unusually high-value payments flagged
  • Merchant category and loyalty status added
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
retail-payment-method-adoption

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