Digital Wallet Adoption Dataset

This dataset provides granular action logs and rich demographic profiles for users interacting with digital wallets, enabling in-depth analysis of adoption drivers and barriers. It supports behavioral segmentation, trend identification, and targeted strategy development for financial technology products.

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

Analyzing factors influencing digital wallet adoption and usage patterns

Sample rows

preview · 8 of 200 rows · all 23 columns
user_idstringemployment_statusstringageintegerhas_bank_accountbooleantransaction_currencystringaction_idstringaction_typestringaction_timestampdatetimedevice_typestringos_typestringlocation_citystringlocation_statestringlocation_countrystringtransaction_amountfloattransaction_statusstringgenderstringeducation_levelstringincome_bracketstringis_first_time_userbooleanbarrier_reasonstringdriver_reasonstringregistration_datedatelast_login_datedate
nyc_user_01employed29trueblankACTN00000101registration2024-05-01T09:18:23ZmobileiOSNew YorkNYUSAblankblankfemalemasterhightrueblankconvenience2024-05-012024-05-01
lnd-user-001employed32trueGBPACTN00000102payment2024-05-03T16:14:56ZmobileAndroidLondonENGUK54.25successmalebachelormediumfalseblankrewards2023-12-152024-05-03
delhi_002student22falseblankACTN00000103add_card2024-05-05T10:23:05ZmobileAndroidDelhiDLIndiablankblankmalehigh_schoollowtrueblankblank2024-05-042024-05-05
parisUser12employed52trueEURACTN00000104withdrawal2024-05-10T19:32:17ZdesktopWindowsParisIDFFrance1100successfemaledoctoratehighfalseblankmerchant_acceptance2021-11-142024-05-10
user_syd_03unemployed48falseblankACTN00000105failed_attempt2024-05-13T08:44:52ZtabletOtherSydneyNSWAustraliablankblankfemalesecondarymediumfalseusability_issuesblank2022-08-242024-05-13
user_mum_04student19falseblankACTN00000106registration2024-05-15T15:05:14ZmobileAndroidMumbaiMHIndiablankblankmalehigh_schoollowtrueblankpeer_influence2024-05-152024-05-15
berlinUser_5employed41trueblankACTN00000107login2024-05-18T07:13:31ZdesktopWindowsBerlinBEGermanyblankblankfemalebachelormediumfalseblankblank2020-03-272024-05-18
chi-user-06employed27trueUSDACTN00000108payment2024-05-20T12:28:06ZmobileiOSChicagoILUSA250.75successfemalebachelormediumfalseblankconvenience2022-05-202024-05-20

What the 200 rows show

from the 200-row sample

Retired (employment status) stands out: mean age is 59.3, against 30.8 for the rest.

  • 70%has_bank_account = true
  • 32median age
  • 2transaction statuses
  • 3device types
  • 3genders
  • 4income brackets
Mean age by employment_status200 rows
0408034.8employed119 rows33.3unemploy…3 rows20.1student46 rows59.3retired32 rows
age200 rows, in bands of 10
035701868502822131105080age →

Median 32, from 15 to 72.

transaction_currency65 rows with a value · 135 left blank
  1. EUR14
  2. USD11
  3. GBP4
  4. CAD4
  5. JPY3
  6. AED3
  7. MXN3
  8. SGD2
  9. INR2
  10. AUD2
23 columns by typefrom the column list below
  • string 16
  • integer 1
  • float 1
  • date 2
  • datetime 1
  • boolean 2

Columns

23 columns in four groups
blueprint · 23 columns
columntypedescriptionexample
Text 16 columns
user_idstringUnique identifier for each user in the digital wallet systemuniquenyc_user_01
action_idstringUnique identifier for each user action log entryuniqueACTN00000101
action_typestringType of action performed by the user (e.g., registration, add_card, payment, withdrawal, failed_attempt)9 valuesregistration
device_typestringType of device used for the action (e.g., mobile, desktop, tablet)mobile · desktop · tablet · other · optionalmobile
os_typestringOperating system of the device used (e.g., Android, iOS, Windows, MacOS)6 values · optionaliOS
location_citystringCity where the user action was performedoptionalNew York
location_statestringState or region where the user action was performedoptionalNY
location_countrystringCountry where the user action was performedoptionalUSA
transaction_currencystringCurrency code for the transaction (e.g., USD, EUR, INR)optionalGBP
transaction_statusstringStatus of the transaction (e.g., success, failed, pending)success · failed · pending · cancelled · optionalsuccess
genderstringGender of the usermale · female · other · prefer_not_to_say · optionalfemale
education_levelstringHighest education level attained by the user8 values · optionalmaster
income_bracketstringIncome bracket of the user (e.g., low, medium, high)low · medium · high · prefer_not_to_say · optionalhigh
employment_statusstringEmployment status of the useremployed · unemployed · student · retired · other · optionalemployed
barrier_reasonstringReason for adoption barrier if the user failed to complete a key action (e.g., security_concerns, lack_of_trust, usability_issues, no_internet_access)6 values · optionalusability_issues
driver_reasonstringMain driver for adoption if the user completed a key action (e.g., convenience, rewards, peer_influence, merchant_acceptance)6 values · optionalconvenience
Numbers 2 columns
transaction_amountfloatAmount involved in the transaction, if applicable0 or more · optional54.25
ageintegerAge of the user13 to 120 · optional29
Dates and times 3 columns
action_timestampdatetimeDate and time when the user action occurred2024-05-01T09:18:23Z
registration_datedateDate when the user registered for the digital walletoptional2024-05-01
last_login_datedateDate of the user's last loginoptional2024-05-01
True or false 2 columns
has_bank_accountbooleanIndicates if the user has a bank accountoptionaltrue
is_first_time_userbooleanIndicates if the user is using the digital wallet for the first timeoptionaltrue

Use it for

  • has bank accou…70%140 of 200 rowsmean age by employmen…34.8empl…33.3unem…20.1stud…59.3reti…

    A finance dashboard

    The has_bank_account rate, age by employment_status and a breakdown of transaction_currency. Excel, Power BI or Tableau.

  • Why do the 32 retired rows have a mean age of 59.3?

    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 users with action_id, action_type and action_timestamp to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 23 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows23 columns
Yours1,000 rows23 columnslocation_city: UK only

blueprint · digital-wallet-adoption-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 23 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
  • User onboarding status tracked
  • Transaction channels differentiated
  • Adoption trigger events logged
  • Demographics mapped for all users
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
digital-wallet-adoption-dataset

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