Marketing
20 columns
200 rows
8 formats

Customer Profiles

This dataset offers a detailed view of customer profiles, including demographics, contact information, account status, and summarized purchase history, making it ideal for CRM systems and customer analytics. The data supports segmentation, targeted marketing, and lifecycle analysis, enabling businesses to enhance customer engagement and retention strategies.

last updated 29 Oct 2025
by GoMask

The brief that made it

Customer segmentation for targeted marketing campaigns

Sample rows

preview · 8 of 200 rows · all 20 columns

customer_idstring	customer_segmentstring	total_spentfloat	marketing_opt_inboolean	genderstring	first_namestring	last_namestring	date_of_birthdate	emailstring	phone_numberstring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	account_created_datedatetime	account_statusstring	total_purchasesinteger	last_purchase_datedatetime	preferred_contact_methodstring
CUST_US_0012A	loyal	11234.45	true	female	Jessica	Nguyen	1991-07-20	jessica.nguyen.91@usmail.com	+1-212-555-3498	318 E 23rd St	New York	NY	10010	USA	2019-06-05T14:22:10	active	54	2024-04-09T17:44:23	email
USER_GB_0034D	high-value	30450.29	true	male	Oliver	Smith	1987-03-14	oliver.smith87@ukmail.co.uk	+44 20 7946 8122	50 Baker St	London	England	W1U 8EW	UK	2016-11-30T08:17:45	active	92	2024-03-26T19:15:07	phone
USR_FR_0201F	inactive	1278.65	false	female	Chloé	Lefevre	1970-12-05	chloe.lefevre70@frmail.fr	+33 1 45 62 03 19	14 Rue de Lyon	Lyon	Auvergne-Rhône-Alpes	69001	France	2012-03-21T13:54:30	inactive	8	blank	none
CUST_IN_0457M	new	224.9	true	female	Priya	Sharma	1998-01-07	priya.sharma98@indmail.in	+91 22 9876 5432	22 Marine Drive	Mumbai	Maharashtra	400020	India	2023-01-16T09:25:44	active	5	2024-05-03T14:01:16	sms
USER_US_0024C	loyal	7058.2	true	male	Michael	Johnson	1981-05-09	m.johnson81@usmail.com	+1-617-555-2893	77 Beacon St	Boston	MA	02108	USA	2017-08-20T12:40:38	active	41	2024-06-01T16:33:45	phone
CUST_CA_0765B	new	112.5	true	female	Amelia	Chen	1994-09-27	amelia.chen94@canmail.ca	+1-416-555-6043	150 King St W	Toronto	Ontario	M5H 1J9	Canada	2022-10-11T10:12:05	active	3	2024-06-04T11:21:54	email
USR_DE_0249G	loyal	10187.75	true	male	Jonas	Schmidt	1984-02-28	jonas.schmidt84@demail.de	+49 30 12345678	18 Unter den Linden	Berlin	Berlin	10117	Germany	2015-07-15T17:39:22	active	62	2024-05-24T09:17:12	email
USER_JP_0113E	new	442.75	true	male	Satoshi	Tanaka	1996-11-15	satoshi.tanaka96@jpmail.jp	+81 3-1234-5678	1-2-3 Shibuya	Tokyo	Tokyo	150-0002	Japan	2021-04-29T08:34:27	active	7	2024-05-14T20:12:06	sms
CUST_BR_0038C	loyal	8756.99	true	male	Lucas	Oliveira	1989-10-03	lucas.oliveira89@brmail.com	+55 11 91234 5678	Av. Paulista 1009	São Paulo	SP	01310-100	Brazil	2018-02-15T16:18:04	active	33	2024-04-28T16:54:39	email
USER_AU_0092F	inactive	0	false	female	Emily	Turner	1978-06-19	emily.turner78@aumail.au	+61 3 9123 4567	200 Collins St	Melbourne	Victoria	3000	Australia	2010-09-23T13:05:48	closed	0	blank	none
USR_FR_0932K	inactive	210.32	false	male	Jean	Dubois	1965-04-22	jean.dubois65@frmail.fr	+33 4 72 58 99 10	9 Rue Victor Hugo	Lyon	Auvergne-Rhône-Alpes	69002	France	2008-05-18T11:16:02	inactive	4	blank	none
CUST_US_0129T	new	49.99	true	male	Daniel	Rivera	1999-01-31	daniel.rivera99@usmail.com	+1-305-555-7841	800 Brickell Ave	Miami	FL	33131	USA	2024-01-07T10:01:39	active	1	2024-06-02T12:00:36	sms
USR_GB_0922J	new	175	false	non-binary	Morgan	Davies	2000-08-15	morgan.davies00@ukmail.co.uk	+44 161 555 8923	233 Oxford Rd	Manchester	England	M13 9PG	UK	2022-06-23T15:16:09	active	4	2024-05-30T18:07:45	sms
CUST_NG_0554N	loyal	6530	true	female	Amina	Abiola	1986-11-09	amina.abiola86@ngmail.ng	+234 1 555 4789	21 Victoria Island	Lagos	Lagos	101001	Nigeria	2017-03-11T10:49:13	active	29	2024-05-18T13:44:21	email
USER_SE_0429Q	inactive	112	false	male	Erik	Larsson	1975-02-22	erik.larsson75@semail.se	+46 8 123 4567	13 Drottninggatan	Stockholm	Stockholm	11151	Sweden	2009-12-11T16:27:55	inactive	3	blank	none
USR_US_0145S	inactive	0	false	male	David	Lee	1972-05-27	david.lee72@usmail.com	+1-415-555-7849	500 Market St	San Francisco	CA	94105	USA	2011-02-17T15:14:12	closed	0	blank	none
CUST_IE_0267V	new	87.5	true	female	Siobhan	Murphy	1995-05-11	siobhan.murphy95@iemail.ie	+353 1 555 6789	48 O'Connell St	Dublin	Leinster	D01 F5P2	Ireland	2023-05-23T16:59:18	active	3	2024-05-24T19:49:03	sms
USR_US_0238R	loyal	9340.25	true	female	Sophia	Martinez	1992-09-03	sophia.martinez92@usmail.com	+1-323-555-6592	410 Sunset Blvd	Los Angeles	CA	90028	USA	2018-04-12T11:43:37	active	39	2024-05-29T15:43:30	email
CUST_MX_0481B	high-value	17650.8	true	male	Carlos	Garcia	1983-04-08	carlos.garcia83@mxmail.mx	+52 55 1234 5678	Av. Insurgentes 1234	Mexico City	CDMX	03100	Mexico	2016-02-09T14:07:56	active	75	2024-05-15T20:51:22	email
USR_KR_0321E	new	72.1	true	female	Min-ji	Kim	1997-12-28	minji.kim97@krmail.kr	+82 2-555-1234	12 Gangnam-daero	Seoul	Seoul	06130	South Korea	2022-09-02T13:03:32	active	2	2024-06-03T16:06:09	sms

What the 200 rows show

from the 200-row sample

High-value (customer segment) stands out: mean total_spent is 15,627, against 2,707 for the rest.

63%marketing_opt_in = true
307.5median total_spent
4account statuses
4preferred contact methods
41address countries
7median total_purchases

Mean total_spent by customer_segment192 rows

total_spent200 rows, in bands of 5,000

Median 307.5, from 0.0 to 32,891.

gender200 rows · 4 values

male98
female93
prefer-not-to-say6
non-binary3

20 columns by typefrom the column list below

string 14
integer 1
float 1
date 1
datetime 2
boolean 1

Columns

20 columns in four groups

blueprint · 20 columns
column	type	description	example
Text 14 columns
`customer_id`	string	Unique identifier for each customerunique	CUST_US_0012A
`first_name`	string	Customer's first name1 to 50	Jessica
`last_name`	string	Customer's last name1 to 50	Nguyen
`gender`	string	Customer's gendermale · female · non-binary · prefer-not-to-say · optional	female
`email`	string	Customer's email addressunique	m.johnson81@usmail.com
`phone_number`	string	Customer's primary phone numberoptional	+1-212-555-3498
`address_street`	string	Customer's street addressup to 100 · optional	318 E 23rd St
`address_city`	string	Customer's cityup to 50 · optional	New York
`address_state`	string	Customer's state or provinceup to 50 · optional	NY
`address_postal_code`	string	Customer's postal or ZIP codeup to 20 · optional	10010
`address_country`	string	Customer's countryup to 50 · optional	USA
`account_status`	string	Current status of the customer accountactive · inactive · suspended · closed	active
`preferred_contact_method`	string	Customer's preferred method of contactemail · phone · sms · none · optional	email
`customer_segment`	string	Segment or category assigned to the customer for analyticsnew · loyal · high-value · at-risk · inactive · optional	loyal
Numbers 2 columns
`total_purchases`	integer	Total number of purchases made by the customer0 or more · optional	54
`total_spent`	float	Total monetary amount spent by the customer0 or more · optional	11234.45
Dates and times 3 columns
`date_of_birth`	date	Customer's date of birthoptional	1991-07-20
`account_created_date`	datetime	Date and time when the customer account was created	2019-06-05T14:22:10
`last_purchase_date`	datetime	Date and time of the customer's most recent purchaseoptional	2024-04-09T17:44:23
True or false 1 column
`marketing_opt_in`	boolean	Indicates if the customer has opted in to receive marketing communicationsoptional	true

Use it for

A marketing dashboard
The marketing_opt_in rate, total_spent by customer_segment and a breakdown of gender. Excel, Power BI or Tableau.
Why do the 29 high-value rows have a mean total_spent of 15,627?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Customers200
CUST_US_0012A11234.45loyal
USER_GB_0034D30450.29high-val…
USR_FR_0201F1278.65inactive
A software demo
Believable customers with first_name, last_name and date_of_birth to fill a screen in front of a buyer.

This dataset200 rows20 columns

Yours1,000 rows20 columnsaddress_street: UK only

blueprint · customer-profiles

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 20 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

Email addresses must be valid format
Age ranges from 18-85
Phone numbers follow international format
Active/inactive status based on last purchase date
Customer since date is before current date

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: customer-profiles

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

A marketing dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Marketing

What should your data show?