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.

  • opened 10 times
  • last updated 21 Jul 2025
  • by GoMask
The brief that made it

Predicting policyholder churn and identifying at-risk customers

Sample rows

preview · 8 of 200 rows · all 31 columns
policyholder_idstringpolicy_statusstringsatisfaction_scorefloatchurnedbooleanaddress_statestringfirst_namestringlast_namestringdate_of_birthdategenderstringemailstringphone_numberstringaddress_streetstringaddress_citystringaddress_postal_codestringaddress_countrystringpolicy_idstringpolicyholder_id_refstringpolicy_typestringpolicy_start_datedatepolicy_end_datedatepremium_amountfloatpayment_frequencystringtotal_claims_countintegertotal_claims_amountfloatlast_claim_datedatechurn_datedatechurn_reasonstringcustomer_tenure_monthsintegerlast_contact_datedatecontact_channelstringnumber_of_policiesinteger
PH0001active8.7falseNYAmeliaRichards1987-08-21femaleamelia.richards@gmail.com+1-212-555-0134120 East 54th StNew York10022USAPL0001PH0001auto2019-06-10blank84.6monthly00blankblankblank592023-11-05online-portal1
PH0002active9.2falseblankOmarAl-Mansouri1965-03-14maleo.mansouri@emiratesmail.ae+971-50-123-994256 Sheikh Zayed RdDubai00000UAEPL0002PH0002life2007-12-01blank395.3annual1250002020-04-15blankblank1972024-03-19phone1
PH0003active7.9falseMHPriyaMehra1992-11-04femalepriya.mehra@outlook.comblank45 Dadar WestMumbai400014IndiaPL0003PH0003auto2022-01-12blank51.75monthly00blankblankblank292024-02-15email2
PH0004active9.8falseILJamesHenderson1975-07-29malejames.henderson@usa.net+1-312-555-081289 Wacker DrChicago60601USAPL0004PH0004life2010-04-10blank175semi-annual00blankblankblank1702024-02-28mail1
PH0005expired8.9trueblankElenaMorozova1958-02-17femalee.morozova@mail.rublankProspekt Mira 12Moscow129090RussiaPL0005PH0005life2000-09-012020-09-01980.25annual21100002018-11-232020-09-01Policy matured2402020-09-02mail1
PH0006active8.2falseblankLiamO'Connor2001-12-19maleliam.oconnor@dublinmail.ie+353-1-234-567823 St Stephen's GreenDublinD02 XY76IrelandPL0006PH0006auto2023-02-01blank42.99monthly17802023-11-18blankblank162024-03-01email1
PH0007active9.3falseblankChloeDubois1998-05-12femalechloe.dubois@orange.fr+33-1-234-567814 Rue Victor HugoParis75008FrancePL0007PH0007auto2020-10-15blank77.5quarterly00blankblankblank432024-03-05online-portal1
PH0008active8.5falseblankMarcusNguyen1983-03-02malemarcus.nguyen@gmail.com+84-28-123-4567456 Nguyen Hue BlvdHo Chi Minh City700000VietnamPL0008PH0008auto2021-05-15blank60.45monthly00blankblankblank362024-01-21phone2

What the 200 rows show

from the 200-row sample

Cancelled (policy status) stands out: mean satisfaction_score is 2.7, against 8.1 for the rest.

  • 23%churned = true
  • 8.1median satisfaction_score
  • 2policy types
  • 4genders
  • 4payment frequencies
  • 6contact channels
Mean satisfaction_score by policy_status200 rows
05108.3active155 rows2.7cancelled16 rows3.4lapsed10 rows8.6expired19 rows
satisfaction_score200 rows, in bands of 1
0408014776125575420510satisfaction_score →

Median 8.1, from 0.0 to 10.0.

address_state77 rows with a value · 123 left blank
  1. NY9
  2. CA9
  3. MH7
  4. IL6
  5. SP4
  6. MA4
  7. RM4
  8. ON3
  9. CDMX3
  10. KA3
31 columns by typefrom the column list below
  • string 18
  • integer 3
  • float 3
  • date 6
  • boolean 1

Columns

31 columns in four groups
blueprint · 31 columns
columntypedescriptionexample
Text 18 columns
policyholder_idstringUnique identifier for each policyholderuniquePH0001
first_namestringPolicyholder's first nameAmelia
last_namestringPolicyholder's last nameRichards
genderstringPolicyholder's gendermale · female · other · prefer_not_to_say · optionalfemale
emailstringPolicyholder's email addressuniquepriya.mehra@outlook.com
phone_numberstringPolicyholder's primary phone numberoptional+1-212-555-0134
address_streetstringPolicyholder's street addressoptional120 East 54th St
address_citystringPolicyholder's cityoptionalNew York
address_statestringPolicyholder's state or provinceoptionalNY
address_postal_codestringPolicyholder's postal or ZIP codeoptional10022
address_countrystringPolicyholder's countryoptionalUSA
policy_idstringUnique identifier for the insurance policyuniquePL0001
policyholder_id_refstringReference to the policyholder_id for this policyPH0001
policy_typestringType of insurance policy (e.g., life, auto)life · autoauto
policy_statusstringCurrent status of the policyactive · cancelled · lapsed · expiredactive
payment_frequencystringFrequency of premium paymentsmonthly · quarterly · semi-annual · annualmonthly
churn_reasonstringReason for churn, if knownoptionalPolicy matured
contact_channelstringPreferred or last used contact channel6 values · optionalonline-portal
Numbers 6 columns
premium_amountfloatPolicy premium amount per payment period0 or more84.6
total_claims_countintegerTotal number of claims filed under this policy0 or more · optional0
total_claims_amountfloatTotal amount claimed under this policy0 or more · optional0
customer_tenure_monthsintegerNumber of months the policyholder has been a customer0 or more · optional59
satisfaction_scorefloatCustomer satisfaction score (e.g., from surveys) on a scale of 0-100 to 10 · optional8.7
number_of_policiesintegerTotal number of active policies held by the policyholder1 or more · optional1
Dates and times 6 columns
date_of_birthdatePolicyholder's date of birth1987-08-21
policy_start_datedateDate when the policy became active2019-06-10
policy_end_datedateDate when the policy ended or is scheduled to endoptional2020-09-01
last_claim_datedateDate of the most recent claim filed under this policyoptional2020-04-15
churn_datedateDate when the policyholder churned, if applicableoptional2020-09-01
last_contact_datedateDate of last contact with the policyholderoptional2023-11-05
True or false 1 column
churnedbooleanIndicates whether the policyholder has churned (true) or is retained (false)false

Use it for

  • churned23%45 of 200 rowsmean satisfaction sco…8.3acti…2.7canc…3.4laps…8.6expi…

    An insurance dashboard

    The churned rate, satisfaction_score by policy_status and a breakdown of address_state. Excel, Power BI or Tableau.

  • Why do the 16 cancelled rows have a mean satisfaction_score of 2.7?

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

Not quite right?

Make it yours.

Same 31 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows31 columns
Yours10,000 rows31 columnsaddress_street: UK only

blueprint · insurance-policyholder-churn-insights

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 31 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
  • One record per policyholder yearly.
  • Churn and retention flags provided.
  • Policy type, premium, and claims count listed.
  • Demographics and tenure included.
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
insurance-policyholder-churn-insights

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