Marketing
24 columns
201 rows
8 formats

AI-Based Customer Segmentation Data

This dataset contains synthetic customer profiles with detailed demographic, transactional, and interaction data, enriched by AI-driven segmentation and predictive metrics. It enables robust machine learning for identifying high-value segments, personalizing marketing, and optimizing customer retention strategies in a scalable, analytics-friendly format.

last updated 20 Aug 2025
by GoMask

The brief that made it

AI-driven customer segmentation for targeted marketing campaigns

Sample rows

preview · 8 of 201 rows · all 24 columns

customer_idstring	preferred_channelstring	churn_risk_scorefloat	marketing_opt_inboolean	customer_segmentstring	first_namestring	last_namestring	emailstring	phone_numberstring	date_of_birthdate	genderstring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	account_created_datedate	lifetime_valuefloat	last_interaction_datedatetime	total_interactionsinteger	last_purchase_datedate	total_purchasesinteger	average_purchase_valuefloat	last_interaction_typestring
jY5n8KsW_	email	0.17	true	Digital Native	Lucas	Kumar	lucas.kumar.uk2024@outlook.com	+44 20 7946 5412	1997-01-14	male	79 Queen's Gate	London	England	SW7 5JP	UK	2021-08-11	3200.5	2024-06-08T18:23:09	51	2024-06-05	24	135.75	purchase
hK8w2zLn9	phone	0.92	false	Senior Saver	Fatima	Santos	fatima.santos.br@outlook.com	+55 11 91234-5678	1962-09-22	female	482 Rua Augusta	São Paulo	SP	01304-001	Brazil	2017-03-15	230	2023-11-20T09:45:15	3	2023-11-16	2	115	inquiry
QwE9rTy_7	none	0.97	false	Student	Sofia	Ivanova	sofia.ivanova.ru@protonmail.com	+7 495 123 4567	2001-05-02	female	128 Ulitsa Tverskaya	Moscow	Moscow	125009	Russia	2023-02-17	0	2024-01-15T15:08:55	1	blank	0	0	support
B8zL5vCa_	chat	0.38	true	Urban Nomad	Wei	Zhang	wei.zhang.cn@163.com	+86 21 5555 2222	1989-07-30	male	22 Nanjing Rd East	Shanghai	Shanghai	200002	China	2022-05-10	2100.25	2024-06-09T10:40:21	34	2024-06-01	19	110.55	purchase
Xk_7rFpQ5	email	0.07	true	VIP	Marie	Dubois	marie.dubois.fr@orange.fr	+33 1 42 86 12 90	1980-04-18	female	16 Rue de Rivoli	Paris	Île-de-France	75004	France	2019-10-08	16500	2024-06-07T16:55:04	88	2024-06-04	103	320.15	purchase
tXrP8fZ_2	phone	0.12	true	Frequent Flyer	Omar	Bakri	omar.bakri.uae@outlook.com	+971 4 333 2121	1974-12-12	male	102 Sheikh Zayed Rd	Dubai	Dubai	00000	UAE	2018-09-27	12100.75	2024-06-01T22:04:09	57	2024-06-01	59	205.79	purchase
dV3j9QkG_	none	0.95	false	Legacy	Isabel	Costa	isabel.costa.pt@protonmail.com	+351 21 324 1234	1958-05-22	female	17 Avenida Liberdade	Lisbon	Lisboa	1250-096	Portugal	2010-04-14	90	2022-09-20T08:33:12	0	2021-12-11	0	0	feedback
rN6wYzQ_0	social_media	0.29	true	Online Explorer	Julia	Nguyen	julia.nguyen.vn@gmail.com	+84 28 3930 1234	1993-03-21	female	23 Nguyen Hue Blvd	Ho Chi Minh	Ho Chi Minh	700000	Vietnam	2023-08-05	1250.4	2024-06-10T11:47:12	37	2024-06-08	13	96.2	purchase
pW9mK_6Q2	chat	0.23	true	Digital Native	Priya	Singh	priya.singh.in@rediffmail.com	+91 22 2875 1114	1999-12-09	female	50 Linking Road	Mumbai	Maharashtra	400050	India	2021-04-27	3100	2024-06-06T19:11:11	47	2024-06-05	22	140.91	purchase
Zw7nV_3ha	email	0.42	true	Regular	James	Miller	james.miller.us2024@gmail.com	+1 206-555-0191	1987-06-15	male	1533 Pine St	Seattle	WA	98101	USA	2022-03-13	1100.8	2024-06-07T14:28:32	18	2024-06-07	9	122.31	purchase
Uk_9mX7wQ	none	0.95	false	Young Starter	Alina	Petrova	alina.petrova.ru2024@yandex.ru	+7 812 234 5678	1998-09-30	female	44 Nevsky Prospect	St. Petersburg	St. Petersburg	191025	Russia	2022-12-01	0	2024-03-11T10:22:00	0	blank	0	0	support
Vt7nKq_9L	sms	0.68	false	Deal Hunter	Anna	Schmidt	anna.schmidt.de@web.de	+49 30 1234 5678	1986-04-16	female	33 Kurfürstendamm	Berlin	Berlin	10719	Germany	2020-11-18	875.45	2024-06-04T17:54:37	19	2024-06-01	6	145.91	purchase
Qx_9lP7tW	email	0.11	true	Business	David	Smith	david.smith.ca2024@gmail.com	+1 416-555-0178	1984-02-23	male	501 Yonge St	Toronto	ON	M4Y 1X9	Canada	2016-07-09	17800	2024-06-09T10:05:40	71	2024-06-08	79	225.4	purchase
Tg_8oQkW5	phone	0.21	true	Frequent Flyer	Mohammed	Ali	mohammed.ali.sa2024@gmail.com	+966 12 123 4567	1977-11-30	male	28 King Abdulaziz Rd	Jeddah	Makkah	23326	Saudi Arabia	2018-06-22	10500	2024-06-08T18:21:33	49	2024-06-08	52	201.15	purchase
Lk_4jR7fZ	none	0.99	false	Retired	Elena	Garcia	elena.garcia.es2024@outlook.com	+34 91 345 6789	1969-12-13	female	55 Gran Via	Madrid	Madrid	28013	Spain	2007-08-31	65.75	2022-01-15T08:02:20	0	2020-12-01	0	0	inquiry
Pq_8xA1sK	email	0.09	true	VIP	Tom	Nelson	tom.nelson.au@gmail.com	+61 2 9123 4567	1979-08-21	male	84 Pitt St	Sydney	NSW	2000	Australia	2014-02-14	14500	2024-06-02T09:40:33	63	2024-05-29	89	162.31	purchase
Xw_6ZkQ1t	social_media	0.31	true	Digital Native	Ayumi	Tanaka	ayumi.tanaka.jp@gmail.com	+81 3 1234 5678	1995-03-05	female	4-7-1 Ginza	Tokyo	Tokyo	104-0061	Japan	2023-03-21	2850.6	2024-06-10T17:49:10	53	2024-06-09	27	105.57	purchase
Sg_9nP7kH	email	0.47	false	Regular	Sara	Johnson	sara.johnson.us@gmail.com	+1 312-555-0170	1989-07-28	female	222 S State St	Chicago	IL	60604	USA	2022-11-03	980.3	2024-06-05T14:17:22	14	2024-06-04	7	140.04	purchase
Wl_3fK8vN	email	0.14	true	Loyalist	Emmanuel	Okoye	emmanuel.okoye.ng2024@gmail.com	+234 1 555 1234	1972-05-03	male	15 Adeola Odeku St	Lagos	Lagos	101241	Nigeria	2015-07-16	8700	2024-06-08T13:51:10	48	2024-06-07	53	165.09	purchase
Km_4Pj8vW	none	0.99	false	Legacy	Paul	Baker	paul.baker.us@gmail.com	+1 602-555-0145	1961-02-11	male	8825 N 7th Ave	Phoenix	AZ	85021	USA	2002-09-07	45	2021-08-25T12:15:32	0	2019-06-01	0	0	feedback

What the 201 rows show

from the 201-row sample

None (preferred channel) stands out: mean churn_risk_score is 0.98, against 0.39 for the rest.

67%marketing_opt_in = true
0.31median churn_risk_score
3genders
5last interaction types
46address countries
2,950median lifetime_value

Mean churn_risk_score by preferred_channel201 rows

churn_risk_score201 rows, in bands of 0.1

Median 0.31, from 0.04 to 1.0.

customer_segment201 rows · top 10 of 16 values

Digital Native27
Online Explorer24
Regular23
Frequent Flyer18
Deal Hunter18
Loyalist16
Senior Saver13
Legacy12
Retired10
Student9

24 columns by typefrom the column list below

string 14
integer 2
float 3
date 3
datetime 1
boolean 1

Columns

24 columns in four groups

blueprint · 24 columns
column	type	description	example
Text 14 columns
`customer_id`	string	Unique identifier for each customer profileunique	jY5n8KsW_
`first_name`	string	Customer's first name	Lucas
`last_name`	string	Customer's last name	Kumar
`email`	string	Customer's email addressunique	wei.zhang.cn@163.com
`phone_number`	string	Customer's phone numberoptional	+44 20 7946 5412
`gender`	string	Customer's gendermale · female · other · prefer_not_to_say · optional	male
`address_street`	string	Customer's street addressoptional	79 Queen's Gate
`address_city`	string	Customer's cityoptional	London
`address_state`	string	Customer's state or provinceoptional	England
`address_postal_code`	string	Customer's postal or ZIP codeoptional	SW7 5JP
`address_country`	string	Customer's countryoptional	UK
`customer_segment`	string	AI-assigned customer segment labeloptional	Digital Native
`preferred_channel`	string	Customer's preferred communication channel6 values · optional	email
`last_interaction_type`	string	Type of the most recent interaction (e.g., purchase, support, inquiry)purchase · support · inquiry · feedback · other · optional	purchase
Numbers 5 columns
`lifetime_value`	float	Estimated lifetime value of the customer0 or more · optional	3200.5
`churn_risk_score`	float	AI-predicted risk score for customer churn (0-1)0 to 1 · optional	0.17
`total_interactions`	integer	Total number of interactions logged for the customer0 or more · optional	51
`total_purchases`	integer	Total number of purchases made by the customer0 or more · optional	24
`average_purchase_value`	float	Average value of purchases made by the customer0 or more · optional	135.75
Dates and times 4 columns
`date_of_birth`	date	Customer's date of birthoptional	1997-01-14
`account_created_date`	date	Date the customer account was created	2021-08-11
`last_interaction_date`	datetime	Timestamp of the most recent customer interactionoptional	2024-06-08T18:23:09
`last_purchase_date`	date	Date of the customer's most recent purchaseoptional	2024-06-05
True or false 1 column
`marketing_opt_in`	boolean	Whether the customer has opted in to marketing communicationsoptional	true

Use it for

A marketing dashboard
The marketing_opt_in rate, churn_risk_score by preferred_channel and a breakdown of customer_segment. Excel, Power BI or Tableau.
Why do the 23 none rows have a mean churn_risk_score of 0.98?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Customers201
jY5n8KsW_0.17email
hK8w2zLn90.92phone
B8zL5vCa_0.38chat
A software demo
Believable customers with first_name, last_name and email to fill a screen in front of a buyer.

This dataset201 rows24 columns

Yours1,000 rows24 columnsaddress_street: UK only

blueprint · ai-based-customer-segmentation-data

Behind this dataset

Same schema. As many rows as you need.

These 201 rows came out of a blueprint — 24 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

Profiles include multi-channel interaction logs
Label segment membership per profile
Include demographic and behavioral features
Data covers simulated campaign responses
Anonymize all personal identifiers

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: ai-based-customer-segmentation-data

What should your data show?

No signup. No card.

Sample rows

What the 201 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?