Finance
28 columns
200 rows
8 formats

Bank Customer Segmentation Dataset

This dataset contains rich, structured information about bank customers, including demographics, account details, product holdings, financial metrics, and segmentation labels. It is ideal for financial institutions seeking to personalize marketing, manage risk, and identify cross-selling opportunities through data-driven customer segmentation and profiling.

opened 19 times
last updated 20 Aug 2025
by GoMask

The brief that made it

Customer segmentation for targeted marketing campaigns

Sample rows

preview · 8 of 200 rows · all 28 columns

customer_idstring	employment_statusstring	num_accountsinteger	marketing_opt_inboolean	products_heldstring	first_namestring	last_namestring	genderstring	date_of_birthdate	emailstring	phone_numberstring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	account_typestring	account_open_datedate	account_balancefloat	annual_incomefloat	marital_statusstring	credit_scoreinteger	risk_categorystring	segmentstring	last_transaction_datedate	avg_monthly_transactionsfloat	avg_monthly_spendfloat	cross_sell_scorefloat
a_jones001	employed	2	true	credit card, savings account	Alex	Jones	male	1990-05-28	alex.jones001@urbanbank.com	+1-312-555-0151	1132 W Madison St	Chicago	IL	60607	USA	checking	2018-11-20	5670.43	68500	single	705	low	young professional	2024-04-18	32.4	1950.25	0.61
m_singh002	student	1	false	student loan, student savings	Meera	Singh	female	1997-09-14	meera.singh002@indiabank.in	+91-98213-67912	Flat 804, Shree Residency	Mumbai	MH	400067	India	student	2022-03-11	98.2	0	single	510	medium	student	2024-05-01	7	120	0.18
r_brown003	employed	3	false	credit card, savings account	Riley	Brown	other	1982-01-05	riley.brown003@citybank.com	+44 20 7946 0152	22 Fleet Street	London	ENG	EC4Y1AA	UK	savings	2011-08-24	14520.19	48000	married	680	medium	mass market	2024-05-02	23.2	1670.46	0.49
l_williams004	retired	4	false	retirement, mortgage, savings, credit card	Laura	Williams	female	1958-06-17	laura.williams004@suburbbank.com	+1-215-555-0178	728 Elmwood Ave	Philadelphia	PA	19104	USA	joint	1982-04-12	174500.21	74000	widowed	820	low	wealth	2024-04-08	8.2	820	0.93
j_martin005	self-employed	6	true	business loan, mortgage, credit card, savings	James	Martin	male	1978-02-19	james.martin005@enterprisefinance.com	+61 2 5550 1234	8 Pitt St	Sydney	NSW	2000	Australia	business	2004-09-15	412000	215000	married	810	low	enterprise	2024-05-02	124.5	83000.1	0.88
s_kumar006	student	1	false	student loan	Sahil	Kumar	male	2004-03-11	sahil.kumar006@youthbank.in	+91-98182-37281	B-17, Sector 44	Gurgaon	HR	122002	India	student	2023-01-19	12.01	0	single	330	high	Gen Z	2024-04-30	2	22	0.09
c_lee007	employed	2	true	credit card, loan	Carmen	Lee	female	1987-11-23	carmen.lee007@pacificbank.com	+1-206-555-0132	3200 Rainier Ave S	Seattle	WA	98144	USA	checking	2015-06-01	4320.56	67000	married	735	low	urban	2024-05-01	38.7	2200	0.72
d_nguyen008	employed	2	false	credit card, savings account	Duy	Nguyen	male	1994-07-04	duy.nguyen008@asiabank.vn	+84 904 123 456	23 Le Duan	Ho Chi Minh	SG	700000	Vietnam	checking	2021-02-10	145.2	32000	single	670	medium	mass market	2024-05-01	22.5	950	0.37
v_morris009	retired	2	false	retirement, savings	Victoria	Morris	female	1956-02-28	victoria.morris009@retiredbank.com	+1-718-555-0107	88 Parkside Ave	Brooklyn	NY	11226	USA	savings	1979-09-17	92000.01	35000	widowed	470	high	senior	2024-03-22	4	350	0.16
t_clark010	student	1	false	blank	Tyler	Clark	male	2002-11-09	tyler.clark010@campusbank.com	+1-617-555-0194	41 Harvard Yard	Cambridge	MA	02138	USA	student	2022-07-11	35.67	0	single	375	high	student	2024-05-01	5	45	0.11
h_davies011	retired	3	true	savings, mortgage	Holly	Davies	female	1966-03-21	holly.davies011@midlandsbank.co.uk	+44 161 555 0156	15 King St	Manchester	ENG	M2 6DW	UK	joint	1993-01-15	15400.88	48000	married	810	low	retired	2024-04-20	11.2	640	0.72
e_hernandez012	employed	5	true	business loan, mortgage, credit card, savings	Eduardo	Hernandez	male	1972-10-31	eduardo.hernandez012@latamcorp.com	+52 55 1234 5678	Avenida Reforma 202	Mexico City	CDMX	06600	Mexico	business	2006-04-04	215780.92	4500000	married	765	low	VIP	2024-05-02	224	181000	0.99
g_watson013	employed	2	true	credit card, savings account	Grace	Watson	female	1985-09-17	grace.watson013@urbanbank.com	+1-415-555-0129	918 Market St	San Francisco	CA	94103	USA	checking	2013-07-18	2300.45	38600	single	690	medium	urban	2024-05-01	26.6	1210	0.55
s_chen014	student	1	false	student savings	Shan	Chen	female	2001-01-18	shan.chen014@campusbank.cn	+86 21 5555 7777	32 Fuxing Rd	Shanghai	SH	200031	China	student	2020-09-01	39.99	8500	single	445	high	student	2024-04-28	6	60	0.13
b_johnson015	employed	1	false	credit card	Brian	Johnson	male	1988-04-06	brian.johnson015@midwestbank.com	+1-773-555-0147	5715 N Clark St	Chicago	IL	60660	USA	checking	2016-11-13	380.19	38600	single	610	medium	mass market	2024-04-29	18.2	790	0.38
n_evans016	employed	2	true	credit card, loan	Nia	Evans	female	1995-06-13	nia.evans016@citybank.com	+44 161 555 0197	41 Oxford Rd	Manchester	ENG	M1 6EU	UK	checking	2019-05-22	630.22	52500	single	680	medium	young professional	2024-04-27	24.8	1050	0.51
k_jackson017	retired	2	true	retirement, mortgage, savings	Kylie	Jackson	female	1962-12-24	kylie.jackson017@seniorbank.com	+1-212-555-0166	2331 Broadway	New York	NY	10024	USA	joint	2000-03-07	21590.4	96000	married	795	low	senior	2024-04-21	15.5	1200	0.81
a_smith018	employed	2	false	credit card, savings account	Ava	Smith	female	1992-03-05	ava.smith018@urbanbank.com	+1-213-555-0182	654 S Grand Ave	Los Angeles	CA	90017	USA	checking	2017-08-11	199.98	38600	single	675	medium	mass market	2024-05-01	28.5	1100	0.47
t_wilson019	student	1	false	student savings	Tara	Wilson	female	2000-10-31	tara.wilson019@campusbank.com	+1-312-555-0199	19 E 8th St	Chicago	IL	60605	USA	student	2021-08-09	9.99	8800	single	420	high	student	2024-04-30	4.2	29	0.08
m_roberts020	employed	1	false	savings account	Miles	Roberts	male	1980-05-11	miles.roberts020@citybank.com	+44 161 555 0124	70 Deansgate	Manchester	ENG	M3 2RJ	UK	savings	2010-03-12	350.01	32000	married	655	medium	mass market	2024-04-29	13.3	770	0.34

What the 200 rows show

from the 200-row sample

Self-employed (employment status) stands out: mean num_accounts is 6.6, against 2.1 for the rest.

51%marketing_opt_in = true
2median num_accounts
4genders
4marital statuses
4risk categories
6account types

Mean num_accounts by employment_status200 rows

num_accounts200 rows, in bands of 1

Median 2, from 1 to 10.

products_held192 rows with a value · 8 left blank

credit card,savings account25
credit card,loan,savings account21
business loan,mortgage,credit card,savings17
savings account,credit card16
student savings13
savings account12
credit card, savings account11
business loan, mortgage, credit card, savings8
retirement,mortgage,savings,credit card8
retirement,savings,credit card7

28 columns by typefrom the column list below

string 17
integer 2
float 5
date 3
boolean 1

Columns

28 columns in four groups

blueprint · 28 columns
column	type	description	example
Text 17 columns
`customer_id`	string	Unique identifier for each bank customerunique	a_jones001
`first_name`	string	Customer's first name	Alex
`last_name`	string	Customer's last name	Jones
`gender`	string	Customer's gendermale · female · other · prefer_not_to_say · optional	male
`email`	string	Customer's email addressunique	neha.patel27@gmail.com
`phone_number`	string	Customer's phone numberoptional	+1-312-555-0151
`address_street`	string	Street address of the customeroptional	1132 W Madison St
`address_city`	string	City of the customer's addressoptional	Chicago
`address_state`	string	State or province of the customer's addressoptional	IL
`address_postal_code`	string	Postal code of the customer's addressoptional	60607
`address_country`	string	Country of the customer's addressoptional	USA
`account_type`	string	Type of primary bank account held by the customer6 values	checking
`products_held`	string	Comma-separated list of banking products held (e.g., credit card, mortgage, loan)optional	student loan
`employment_status`	string	Customer's employment status6 values · optional	employed
`marital_status`	string	Customer's marital statussingle · married · divorced · widowed · other · optional	single
`risk_category`	string	Bank-assigned risk category for the customerlow · medium · high · unknown · optional	low
`segment`	string	Customer segment assigned for marketing or analysisoptional	young professional
Numbers 7 columns
`account_balance`	float	Current balance of the customer's primary account0 or more	5670.43
`num_accounts`	integer	Total number of accounts held by the customer1 or more	2
`annual_income`	float	Customer's self-reported or estimated annual income0 or more · optional	68500
`credit_score`	integer	Customer's latest credit score300 to 850 · optional	705
`avg_monthly_transactions`	float	Average number of transactions per month0 or more · optional	32.4
`avg_monthly_spend`	float	Average monthly spend by the customer0 or more · optional	1950.25
`cross_sell_score`	float	Score indicating likelihood of cross-selling success0 to 1 · optional	0.61
Dates and times 3 columns
`date_of_birth`	date	Customer's date of birthoptional	1990-05-28
`account_open_date`	date	Date when the customer's primary account was opened	2018-11-20
`last_transaction_date`	date	Date of the customer's most recent transactionoptional	2024-04-18
True or false 1 column
`marketing_opt_in`	boolean	Whether the customer has opted in to receive marketing communicationsoptional	true

Use it for

A finance dashboard
The marketing_opt_in rate, num_accounts by employment_status and a breakdown of products_held. Excel, Power BI or Tableau.
Why do the 24 self-employed rows have a mean num_accounts of 6.6?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Customers200
a_jones0012employed
m_singh0021student
j_martin0056self-emp…
A software demo
Believable customers with first_name, last_name and gender to fill a screen in front of a buyer.

This dataset200 rows28 columns

Yours1,000 rows28 columnsaddress_street: UK only

blueprint · bank-customer-segmentation-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 28 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

Customer demographics and behavior included
Segmentation labels based on transaction patterns
Product holdings annotated
Supports clustering and supervised models

Rows1,000 rows10,000 rows100,000 rows1,000,000 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: bank-customer-segmentation-dataset

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?