Can I use these to train a churn prediction model?

Yes. Each has a churn, cancellation or retention column to use as the target. Watch for columns like churn_date that would leak the answer into your features.

Can I get a churn dataset for my own product?

Describe it in GoMask Data Factory, for example "2,000 SaaS accounts where accounts with fewer than 3 active users churn twice as often", and preview 20 rows free.

What formats are available?

Excel, CSV, JSON, Parquet, JSONL, SQL, TSV and XML.

Datasets by use case

Customer Churn Datasets

Churn analysis needs more than a churned flag. These datasets carry the story around it: subscription dates, usage and logins, cancellation reasons, retention offers and lifetime value. Use them to build a churn dashboard, a cohort analysis or a churn prediction model.

Subscriptions and SaaS

SaaS accounts with plan, seats, logins and retention rates; subscription billing with failed payments and cancellation dates; cancellations with the retention offer made and whether it worked; and trials with conversion and 30-day retention.

Technology18 cols · 200 rows

B2B SaaS Usage Retention

This dataset provides comprehensive retention and engagement metrics for B2B SaaS customers, including subscription details, user activity, feature usage, and churn risk scores. It enables customer success teams to monitor long-term engagement, identify at-risk accounts, and optimize strategies for churn prevention and customer health. Ideal for SaaS analytics, customer lifecycle management, and predictive modeling.

customer_name	subscription_status	plan_type
Alpha Technologies Inc.	active	Enterprise
Beta Financial Group	active	Pro
Gamma Retail Solutions	active	Basic

customer_idindustryaccount_manager+12 more

Finance19 cols · 200 rows

Subscription Billing

This dataset provides a comprehensive view of recurring subscription billing records, including detailed information on customer accounts, subscription plans, payment cycles, renewal schedules, payment failures, and retention status. It enables robust analysis of customer lifetime value, churn prediction, and revenue forecasting for subscription-based businesses.

plan_name	payment_status	retention_status
Monthly Basic	success	retained
Quarterly Premium	failed	at-risk
Yearly Enterprise	success	retained

subscription_idcustomer_idcustomer_email+13 more

Marketing19 cols · 200 rows

Consumer Subscription Cancellation Data

This dataset provides detailed records of consumer subscription cancellations, including standardized reason codes, customer demographics, subscription details, and retention offer outcomes. It is ideal for churn analysis, customer segmentation, and evaluating the effectiveness of retention strategies in subscription-based businesses.

retention_offer_type	final_cancellation_status	subscription_type
DISCOUNT	CANCELLED	PREMIUM
DISCOUNT	CANCELLED	BASIC
NONE	CANCELLED	STUDENT

cancellation_idcustomer_idsubscription_id+13 more

Technology14 cols · 200 rows

Subscription Service Trial Conversions

This dataset provides detailed tracking of user trial experiences and conversion outcomes across digital subscription services, including onboarding completion, engagement metrics, retention status, and marketing attribution. It enables granular analysis of conversion rates, the effectiveness of onboarding, and the impact of marketing channels, supporting data-driven improvements to user acquisition and retention strategies.

converted	subscription_plan	trial_type
true	PREMIUM	free
true	ENTERPRISE	free
false	–	discounted

user_idservice_idtrial_start_date+8 more

Insurance, banking and telecom

Policyholders with churn dates and reasons, bank customers with lifetime value and churn flags, and telecom churn with the reason given on the call. Pair churn with lifetime value to see which losses hurt most.

Insurance31 cols · 200 rows

Insurance Policyholder Churn Insights

This dataset provides a comprehensive view of insurance policyholders, their demographic details, policy information, claims history, and churn status for both life and auto insurance products. It is designed to support predictive modeling of customer attrition, enabling insurers to identify at-risk customers and develop targeted retention strategies. The inclusion of satisfaction scores, contact history, and churn reasons makes it ideal for advanced analytics and customer experience optimization.

first_name	last_name	policy_type
Amelia	Richards	auto
Omar	Al-Mansouri	life
Priya	Mehra	auto

policyholder_iddate_of_birthgender+25 more

Insurance24 cols · 200 rows

Insurance Customer Lifetime Value

This dataset provides detailed insurance customer profiles, including policy details, claims history, estimated lifetime value, and churn risk indicators. It enables insurers to identify high-value customers, predict churn, and optimize retention strategies through actionable insights. The comprehensive structure supports advanced analytics and customer segmentation for targeted interventions.

first_name	last_name	policy_type
Sophie	Nguyen	auto
Mohammed	AlFarsi	life
Lucas	Müller	health

customer_iddate_of_birthgender+18 more

Finance16 cols · 200 rows

Financial Customer Lifetime Value

This dataset provides a comprehensive view of customer lifetime value in financial services, combining detailed transaction history, product retention, and churn indicators for each customer-product relationship. It enables granular analysis of customer profitability, retention strategies, and churn prediction, making it ideal for financial institutions seeking to optimize customer engagement and value.

customer_name	product_type	transaction_type
Jessica Wu	savings_account	deposit
Omar Castillo	loan	payment
Priya Menon	credit_card	fee

customer_idcustomer_emailcustomer_phone+10 more

Telecommunications19 cols · 65 rows

Customer Churn Call Reason Analysis

This dataset provides detailed call log records linked to customer churn events, including call metadata, customer demographics, churn reasons, and resolution outcomes. It enables comprehensive analysis of why customers leave, how call center interactions influence churn, and supports the development of targeted retention strategies. The dataset is ideal for churn prediction modeling, root cause analysis, and customer experience optimization.

call_type	churn_flag	resolution_status
inbound	false	resolved
outbound	false	resolved
inbound	true	escalated

customer_idcall_idcall_datetime+13 more

Games and retail

Game sessions with a churn flag and session outcomes, and retail customers marked as lapsed, with purchase frequency and win-back eligibility.

Gaming16 cols · 200 rows

Game Session Telemetry Dataset

This dataset contains detailed logs of simulated online gaming sessions, including player identifiers, session timings, actions, feature usage, purchases, and outcomes. Designed for gaming analytics, it enables churn prediction, player segmentation, and feature adoption analysis, offering valuable insights for game developers and startups.

player_segment	session_outcome	device_type
new	abandoned	mobile
casual	win	mobile
new	abandoned	console

session_idplayer_idgame_id+10 more

Marketing19 cols · 120 rows

Customer Purchase Frequency Patterns

This dataset provides a comprehensive view of customer purchase frequency patterns, including total purchases, recency, spending, and lapsed status. It is designed to support marketing optimization, retention analysis, and win-back campaign targeting by offering actionable insights into customer engagement and churn risk.

first_name	last_name	is_lapsed
Marina	Dupont	false
Jason	Richards	false
Naoko	Murakami	true

customer_idemailsignup_date+13 more

Every dataset in this collection

10 datasets