Finance
11 columns
200 rows
8 formats

Finance Payment Method Preference

This dataset provides detailed insights into customer payment method preferences, usage frequency, and transaction values across various demographic and business segments. It enables financial service providers to analyze trends, optimize product offerings, and tailor marketing strategies based on customer behavior and segment characteristics.

last updated 20 Aug 2025
by GoMask

The brief that made it

Analyzing payment method adoption across customer segments

Sample rows

preview · 8 of 200 rows · all 11 columns

customer_idstring	income_bracketstring	ageinteger	is_primary_methodboolean	regionstring	segmentstring	payment_methodstring	frequencyinteger	average_transaction_valuefloat	last_used_datedate	genderstring
GENZ_1004A	Low	19	true	Singapore, Singapore	Gen Z	Mobile Wallet	41	18.5	2024-06-16	Female
MILL_712B	High	32	true	London, UK	Millennial	Credit Card	55	95.8	2024-06-14	Male
SENIOR_991C	High	74	true	Berlin, Germany	Senior	Bank Transfer	7	1200	2024-06-15	Female
SMB_11XQ	Medium	36	true	Delhi, India	SMB	Credit Card	33	525.5	2024-06-13	Male
ENT_998U	High	61	true	New York, NY, USA	Enterprise	Bank Transfer	120	32000	2024-06-16	Female
GENX_712D	Low	45	true	Toronto, Canada	Gen X	Cash	2	6	2024-06-09	Male
LOWIN_001E	Low	20	true	Rio de Janeiro, Brazil	Low Income	Debit Card	8	9.75	2024-06-11	Non-binary
GENZ_1005F	Medium	18	true	Tokyo, Japan	Gen Z	Cryptocurrency	3	750.8	2024-06-07	Prefer not to say
SMB_11ZR	High	36	false	San Francisco, CA, USA	SMB	Bank Transfer	60	2100	2024-06-13	Female
GENX_712H	Medium	54	false	Reykjavik, Iceland	Gen X	Credit Card	18	320	2024-06-14	Other
SENIOR_991J	Medium	82	false	Sydney, Australia	Senior	Debit Card	0	0	blank	Male
GENZ_1007K	Low	21	false	Toronto, Canada	Gen Z	Debit Card	55	16.2	2024-06-15	Female
MILL_712L	Medium	32	false	London, UK	Millennial	Mobile Wallet	48	60.9	2024-06-16	Male
HIGHINC_991M	High	58	true	Dubai, UAE	High Income	Credit Card	80	520	2024-06-14	Female
GENX_712N	Prefer not to say	50	false	San Jose, CA, USA	Gen X	Cryptocurrency	1	8500	2024-06-12	Prefer not to say
SMB_11PO	Medium	36	false	Delhi, India	SMB	Buy Now Pay Later	24	350	2024-06-13	Male
ENTERP_998P	High	62	false	Dubai, UAE	Enterprise	Buy Now Pay Later	60	24000	2024-06-16	Female
GENZ_1009Q	Low	16	false	Singapore, Singapore	Gen Z	Mobile Wallet	0	0	blank	Non-binary
GENX_712R	Prefer not to say	43	false	Reykjavik, Iceland	Gen X	Other	8	5.5	2024-06-15	Non-binary
SENIOR_991S	Medium	68	false	Helsinki, Finland	Senior	Bank Transfer	2	800	2024-06-12	Female

What the 200 rows show

from the 200-row sample

High (income bracket) stands out: mean age is 69.3, against 33.6 for the rest.

38%is_primary_method = true
36median age
5genders
8payment methods
9segments
8median frequency

Mean age by income_bracket200 rows

age200 rows, in bands of 20

Median 36, from 0 to 120.

region200 rows · top 10 of 30 values

Singapore, Singapore23
Toronto, Canada16
Tokyo, Japan16
London, UK15
Dubai, UAE14
Berlin, Germany11
Delhi, India11
Sydney, Australia11
Reykjavik, Iceland10
Helsinki, Finland10

11 columns by typefrom the column list below

string 6
integer 2
float 1
date 1
boolean 1

Columns

11 columns in four groups

blueprint · 11 columns
column	type	description	example
Text 6 columns
`customer_id`	string	Unique identifier for the customerunique	GENZ_1004A
`segment`	string	Customer segment classification (e.g., age group, income bracket, business type)9 values	Gen Z
`payment_method`	string	Preferred payment method chosen by the customer8 values	Mobile Wallet
`region`	string	Geographical region associated with the customer (e.g., city, state, country)optional	Singapore, Singapore
`gender`	string	Gender of the customerMale · Female · Non-binary · Prefer not to say · Other · optional	Female
`income_bracket`	string	Income bracket of the customerLow · Medium · High · Prefer not to say · optional	Low
Numbers 3 columns
`frequency`	integer	Number of times this payment method was used by the customer in the observed period0 or more	41
`average_transaction_value`	float	Average value of transactions made using this payment method by the customer0 or more · optional	18.5
`age`	integer	Age of the customer0 to 120 · optional	19
Dates and times 1 column
`last_used_date`	date	Date when the payment method was last used by the customeroptional	2024-06-16
True or false 1 column
`is_primary_method`	boolean	Indicates if this is the customer's primary payment method	true

Use it for

A finance dashboard
The is_primary_method rate, age by income_bracket and a breakdown of region. Excel, Power BI or Tableau.
Why do the 53 High rows have a mean age of 69.3?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Customers200
GENZ_1004A19Low
MILL_712B32High
SMB_11ZR36High
A software demo
Believable customers with segment, payment_method and frequency to fill a screen in front of a buyer.

This dataset200 rows11 columns

Yours1,000 rows11 columnsregion: UK only

blueprint · finance-payment-method-preference

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 11 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

Categorize payments by method type
Segment data by customer demographics
Track frequency of use per method
Identify emerging payment technologies

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: finance-payment-method-preference

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

A finance dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Finance

What should your data show?