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.

  • opened 9 times
  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Customer segmentation for retention campaigns

Sample rows

preview · 8 of 200 rows · all 24 columns
customer_idstringpolicy_typestringchurn_risk_scorefloatis_churnedbooleanaddress_countrystringfirst_namestringlast_namestringdate_of_birthdategenderstringemailstringphone_numberstringaddress_streetstringaddress_citystringaddress_statestringaddress_postal_codestringpolicy_idstringpolicy_start_datedatepolicy_end_datedateannual_premiumfloattotal_claimsintegertotal_claim_amountfloatcustomer_lifetime_valuefloatlast_interaction_datedatepreferred_contact_methodstring
CUST_0001auto0.15falseSingaporeSophieNguyen1989-07-11femalesophie.nguyen89@gmail.com+65 9825 102321 Orchard Road Apt 7BSingaporeCentral238841POL_AU00112022-06-152024-06-141350.5500105002024-03-10email
CUST_0002life0.07falseUAEMohammedAlFarsi1960-03-02malemohammed.alfarsi60@outlook.com+971 50 234 765445 Sheikh Zayed RdDubaiDubai00000POL_LI21002017-09-232027-09-228500.75225000920002024-04-19phone
CUST_0003health0.22falseGermanyLucasMüller1975-11-30malelucas.mueller75@web.de+49 172 554832982 WilhelmstrasseBerlinBerlin10117POL_HE32092021-01-012026-12-314200.25312000420002024-05-05postal_mail
CUST_0004travel0.88trueUSAEmilyJohnson1995-02-17femaleemily.johnson95@yahoo.com+1 917-555-9087310 West 76th StNew YorkNY10024POL_TR40222023-08-012024-07-31150.99003102024-07-31sms
CUST_0005travel0.93trueIndiaAnanyaPatel2006-12-09femaleananya.patel06@gmail.comblank12 MG RoadMumbaiMaharashtra400001POL_TR41212024-05-202024-06-1999.500120blanknone
CUST_0006auto0.32falseSpainMateoGarcía1991-04-05malemateo.garcia91@gmail.com+34 600 839 41256 Calle MayorMadridMadrid28013POL_AU00982023-03-122025-03-111100.75190075002024-05-10email
CUST_0007health0.16falseUKAvaWilliams1970-09-25femaleava.williams70@outlook.com+44 7900 123456100 PiccadillyLondonLondonW1J 7NHPOL_HE32102022-06-012026-05-316200.15218000490002024-05-12postal_mail
CUST_0008home0.21falseSouth KoreaJinLee1984-01-08malejin.lee84@naver.com+82 10-2345-678972 Gangnam-daeroSeoulSeoul06030POL_HO20032020-11-152025-11-143400.7500202002024-05-09email

What the 200 rows show

from the 200-row sample

Travel (policy type) stands out: 31 of its 31 rows have is_churned = true, against 4 of 169 for the rest.

  • 18%is_churned = true
  • 0.21median churn_risk_score
  • 4genders
  • 5preferred contact methods
  • 1,850median annual_premium
  • 1median total_claims
Is churned rate by policy_typeis_churned = true
0%50%100%6%auto4 of 620%home0 of 420%life0 of 340%health0 of 31100%travel31 of 31
churn_risk_score200 rows, in bands of 0.1
030603850552020001025is_churned from 0.800.50.81churn_risk_score →

Every row with churn_risk_score of 0.8 or more has is_churned = true. None below it.

address_country200 rows · top 10 of 40 values
  1. USA22
  2. Spain15
  3. UK14
  4. India13
  5. France13
  6. Russia12
  7. Egypt11
  8. Germany10
  9. Brazil8
  10. Singapore7
24 columns by typefrom the column list below
  • string 14
  • integer 1
  • float 4
  • date 4
  • boolean 1

Columns

24 columns in four groups
blueprint · 24 columns
columntypedescriptionexample
Text 14 columns
customer_idstringUnique identifier for each insurance customeruniqueCUST_0001
first_namestringCustomer's first nameSophie
last_namestringCustomer's last nameNguyen
genderstringCustomer's gendermale · female · other · prefer_not_to_say · optionalfemale
emailstringCustomer's email addressuniquelucas.mueller75@web.de
phone_numberstringCustomer's phone numberoptional+65 9825 1023
address_streetstringCustomer's street addressoptional21 Orchard Road Apt 7B
address_citystringCustomer's cityoptionalSingapore
address_statestringCustomer's state or provinceoptionalCentral
address_postal_codestringCustomer's postal or ZIP codeoptional238841
address_countrystringCustomer's countryoptionalSingapore
policy_idstringUnique identifier for the customer's active insurance policyPOL_AU0011
policy_typestringType of insurance policy held by the customer6 valuesauto
preferred_contact_methodstringCustomer's preferred method of contactemail · phone · sms · postal_mail · none · optionalemail
Numbers 5 columns
annual_premiumfloatAnnual premium amount paid by the customer for the policy0 or more1350.55
total_claimsintegerTotal number of claims made by the customer to date0 or more · optional0
total_claim_amountfloatTotal monetary value of claims made by the customer0 or more · optional0
customer_lifetime_valuefloatEstimated lifetime value of the customer to the insurer0 or more10500
churn_risk_scorefloatPredicted risk score for customer churn (0-1, where 1 is highest risk)0 to 10.15
Dates and times 4 columns
date_of_birthdateCustomer's date of birth1989-07-11
policy_start_datedateDate when the customer's current policy started2022-06-15
policy_end_datedateDate when the customer's current policy is set to expireoptional2024-06-14
last_interaction_datedateDate of the customer's last interaction with the insureroptional2024-03-10
True or false 1 column
is_churnedbooleanIndicator if the customer has churned (true) or is active (false)false

Use it for

  • is churned18%35 of 200 rowsmean churn risk score…0.31auto0.22home0.05life0.14heal…

    An insurance dashboard

    The is_churned rate, churn_risk_score by policy_type and a breakdown of address_country. Excel, Power BI or Tableau.

  • Why do 35 of 200 rows have is_churned = true?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable customers with first_name, last_name and date_of_birth to fill a screen in front of a buyer.

Not quite right?

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This dataset200 rows24 columns
Yours10,000 rows24 columnsaddress_street: UK only

blueprint · insurance-customer-lifetime-value

Behind this dataset

Same schema. As many rows as you need.

These 200 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
  • Customer ID and policy details anonymized
  • Claim frequency and premium logged
  • Lifetime value calculated by business logic
  • Churn indicator flagged
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
insurance-customer-lifetime-value

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