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.

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

Predicting and optimizing customer lifetime value for financial products

Sample rows

preview · 8 of 200 rows · all 16 columns
customer_idstringtransaction_typestringlifetime_valuefloatis_churnedbooleancurrencystringcustomer_namestringcustomer_emailstringcustomer_phonestringcustomer_join_datedateproduct_idstringproduct_typestringtransaction_idstringtransaction_datedatetimetransaction_amountfloatretention_period_monthsintegerchurn_datedate
CUST00001deposit6800falseUSDJessica Wujessica.wu.001@example.com+1-202-555-01812019-03-15PROD-A-001savings_accountTXN-1000012023-04-11T09:39:002200.3551blank
CUST00002payment18000trueUSDOmar Castilloomar.castillo.002@example.com+44 7911 1234562021-07-03PROD-L-002loanTXN-1000022023-06-12T15:40:2415840342023-11-20
CUST00003fee4200.5falseINRPriya Menonpriya.menon.003@example.com+91 98123456782020-02-17PROD-CC-003credit_cardTXN-1000032022-12-09T11:22:54346.7540blank
CUST00004deposit35900falseUSDDavid Leedavid.lee.004@example.comblank2017-08-22PROD-I-004investmentTXN-1000042023-03-15T13:15:00945075blank
CUST00005payment1230falseEURSofia Rossisofia.rossi.005@example.com+39 347 12345672022-05-30PROD-IN-005insuranceTXN-1000052023-07-21T10:36:0021013blank
CUST00006fee540falseUSDMichael O'Brienmichael.obrien.006@example.com+1-303-555-01922020-10-07PROD-O-006otherTXN-1000062023-01-19T16:29:0073.9932blank
CUST00007withdrawal3580trueAEDFatima El-Sayedfatima.elsayed.007@example.com+971 50 123 45672021-03-11PROD-CC-007credit_cardTXN-1000072023-09-26T18:42:001276.62302023-10-02
CUST00008deposit1450falseEURLucas Schmidtlucas.schmidt.008@example.com+49 151 123456782022-12-01PROD-SA-008savings_accountTXN-1000082023-12-21T12:03:0030012blank

What the 200 rows show

from the 200-row sample

Withdrawal (transaction type) stands out: mean lifetime_value is 19,291, against 5,753 for the rest.

  • 15%is_churned = true
  • 3,050median lifetime_value
  • 6product types
  • 940.3median transaction_amount
  • 27median retention_period_months
Mean lifetime_value by transaction_type200 rows
010k20k8,521depos…90 rows19,291withd…16 rows3,974payme…64 rows1,096fee25 rows2,218inter…4 rows1,030refund1 rows
lifetime_value200 rows, in bands of 5,000
06012011827221972311025k45klifetime_value →

Median 3,050, from 90.0 to 41,201.

currency200 rows · top 10 of 31 values
  1. EUR57
  2. USD37
  3. SEK9
  4. RUB9
  5. INR8
  6. BRL8
  7. GBP8
  8. CNY7
  9. EGP6
  10. AED5
16 columns by typefrom the column list below
  • string 9
  • integer 1
  • float 2
  • date 2
  • datetime 1
  • boolean 1

Columns

16 columns in four groups
blueprint · 16 columns
columntypedescriptionexample
Text 9 columns
customer_idstringUnique identifier for each customeruniqueCUST00001
customer_namestringFull name of the customerJessica Wu
customer_emailstringEmail address of the customeruniqueanna.kim.015@example.com
customer_phonestringContact phone number of the customeroptional+1-202-555-0181
product_idstringIdentifier for the financial product associated with the transactionPROD-A-001
product_typestringType of financial product (e.g., savings account, loan, credit card)6 valuessavings_account
transaction_idstringUnique identifier for each financial transactionuniqueTXN-100001
transaction_typestringType of transaction (e.g., deposit, withdrawal, payment, fee)7 valuesdeposit
currencystringCurrency code for transaction and lifetime value amounts (e.g., USD, EUR)USD
Numbers 3 columns
transaction_amountfloatMonetary value of the transaction0 or more2200.35
retention_period_monthsintegerNumber of months the customer has retained the financial product0 or more · optional51
lifetime_valuefloatCalculated lifetime value of the customer for the financial product0 or more6800
Dates and times 3 columns
customer_join_datedateDate when the customer first joined or registered2019-03-15
transaction_datedatetimeDate and time when the transaction occurred2023-04-11T09:39:00
churn_datedateDate when the customer churned, if applicableoptional2023-11-20
True or false 1 column
is_churnedbooleanIndicates whether the customer has churned (true) or is still active (false)false

Use it for

  • is churned15%30 of 200 rowsmean lifetime value b…8.5kdepo…19.3kwith…4kpaym…1.1kfee

    A finance dashboard

    The is_churned rate, lifetime_value by transaction_type and a breakdown of currency. Excel, Power BI or Tableau.

  • Why do the 16 withdrawal rows have a mean lifetime_value of 19,291?

    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 customer_name, customer_email and customer_phone to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 16 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows16 columns
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blueprint · financial-customer-lifetime-value

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 16 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
  • Log customer ID and product type
  • Record account open/close dates
  • Track yearly transaction sums
  • Flag inactive customers
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
financial-customer-lifetime-value

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