Credit Card Spend Pattern Clusters

This dataset contains anonymized credit card transaction records, enriched with behavioral cluster assignments and key transaction attributes such as merchant category, transaction type, and customer demographics. Designed for segmentation and marketing analytics, it enables organizations to identify spending patterns, target customer segments, and optimize marketing strategies.

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

Customer segmentation and behavioral analysis for targeted marketing

Sample rows

preview · 8 of 200 rows · all 15 columns
transaction_idstringcard_typestringamountfloatis_onlinebooleanmerchant_categorystringcustomer_idstringtransaction_datetimedatetimemerchant_namestringtransaction_typestringcurrencystringcountrystringcluster_idintegercluster_labelstringcustomer_age_groupstringcustomer_genderstring
TXN0001Visa87.54falseGroceryCUST10012024-04-21T14:22:18ZFresh FoodspurchaseUSDUS1Grocery Shopper26-35Female
TXN0002MasterCard23.99falseDiningCUST10022024-05-02T19:44:03ZUrban EatspurchaseCADCA2Casual Diner26-35Male
TXN0003Discover0.01trueFeeCUST10032024-05-09T08:15:59ZblankfeeEURNL11Credit Seeker51+Unknown
TXN0004Amex1500000falseReal EstateCUST10042024-04-30T16:06:32ZPrime RealtypurchaseUSDUS7Ultra High Net Worth51+Male
TXN0005Visa59.9trueDigital GoodsCUST10052024-05-11T21:18:44ZAppPlaypurchaseGBPGB5Digital Enthusiast18-25Other
TXN0006MasterCard0trueDiningCUST10062024-04-22T12:33:17ZblankrefundCADCA2Casual Diner26-35Female
TXN0007Visa32.8falseGroceryCUST10072024-04-25T10:46:00ZMarket LanepurchaseUSDUS1Grocery Shopper26-35Male
TXN0008Visa75trueElectronicsCUST10082024-05-04T15:27:11ZTech HavenpurchaseUSDUS3Tech Savvy18-25Female

What the 200 rows show

from the 200-row sample

Amex (card type) stands out: mean amount is 717,574, against 691.5 for the rest.

  • 38%is_online = true
  • 75.0median amount
  • 4transaction types
  • 4customer age groups
  • 4customer genders
  • 8currencies
Mean amount by card_type200 rows
0400k800k338.2Visa77 rows282.8Master…50 rows717.6kAmex25 rows6.0Discov…24 rows3,362Other24 rows
amount200 rows, in bands of 250k
09519019000030210401.3M2.5Mamount →

Median 75.0, from 0.0 to 2,500,000.

merchant_category200 rows · top 10 of 13 values
  1. Grocery33
  2. Fee30
  3. Dining29
  4. Electronics19
  5. Digital Goods16
  6. Travel16
  7. Entertainment14
  8. Luxury10
  9. Health8
  10. Automobile7
15 columns by typefrom the column list below
  • string 11
  • integer 1
  • float 1
  • datetime 1
  • boolean 1

Columns

15 columns in four groups
blueprint · 15 columns
columntypedescriptionexample
Text 11 columns
transaction_idstringUnique anonymized identifier for each credit card transactionuniqueTXN0001
customer_idstringAnonymized unique identifier for the credit card holderCUST1001
merchant_categorystringCategory of the merchant where the transaction took place (e.g., Grocery, Dining, Travel)Grocery
merchant_namestringAnonymized or generic name of the merchantoptionalFresh Foods
transaction_typestringType of transaction (e.g., purchase, refund, cash_advance)purchase · refund · cash_advance · payment · feepurchase
currencystringISO 4217 currency code for the transaction (e.g., USD, EUR)8 currenciesUSD
countrystringCountry where the transaction was made9 countriesUS
cluster_labelstringDescriptive label for the behavioral cluster (e.g., Frequent Traveler, Grocery Shopper)11 labels · optionalGrocery Shopper
card_typestringType of credit card used (e.g., Visa, MasterCard, Amex, Discover)Visa · MasterCard · Amex · Discover · Other · optionalVisa
customer_age_groupstringAnonymized age group of the customer (e.g., 18-25, 26-35, 36-50, 51+)18-25 · 26-35 · 36-50 · 51+ · optional26-35
customer_genderstringAnonymized gender of the customer (e.g., Male, Female, Other, Unknown)Male · Female · Other · Unknown · optionalFemale
Numbers 2 columns
amountfloatTransaction amount in the local currency0 or more87.54
cluster_idintegerIdentifier for the behavioral cluster assigned to this transaction or customer1 or more1
Dates and times 1 column
transaction_datetimedatetimeTimestamp when the transaction occurred2024-04-21T14:22:18Z
True or false 1 column
is_onlinebooleanIndicates if the transaction was made online (true) or in-store (false)false

Use it for

  • is online38%76 of 200 rowsmean amount by card t…338.2Visa282.8Mast…717.6kAmex6.0Disc…

    A finance dashboard

    The is_online rate, amount by card_type and a breakdown of merchant_category. Excel, Power BI or Tableau.

  • Why do the 25 Amex rows have a mean amount of 717,574?

    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 customer_id, transaction_datetime and amount to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 15 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows15 columns
Yours1,000 rows15 columnscountry: UK only

blueprint · credit-card-spend-pattern-clusters

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 15 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: amount, merchant, category
  • Users grouped by spend cluster (labels)
  • High-value and outlier clusters present
  • Time-of-day and channel (online/offline) encoded
  • Promotional impact observed per cluster
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
credit-card-spend-pattern-clusters

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