Fintech App User Engagement

This dataset provides detailed, user-level engagement and transaction metrics for a fintech app, including feature usage, transaction volumes, device types, and premium status. It enables startups to analyze user behavior, optimize product features, and track engagement trends for targeted improvements and growth strategies.

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

Analyzing user engagement and retention patterns

Sample rows

preview · 8 of 200 rows · all 13 columns
user_idstringdevice_typestringtransaction_volumefloatis_premium_userbooleanfeature_namestringsignup_datedatelast_active_datetimedatetimecountrystringfeature_engagement_countintegerfeature_engagement_last_datetimedatetimetransaction_countintegertransaction_last_datetimedatetimeapp_versionstring
user_abcD1234Android14290.67trueTransfer2023-11-212024-06-04T17:32:15US5402024-06-03T16:44:553202024-06-03T17:01:16v2.7.1
XyZ_5678klmniOS0falseBudgeting2024-01-032024-06-04T10:25:54GB1202024-06-03T20:19:370blankv2.6.8
user-9876qwErWeb2580.2falseAnalytics2024-03-152024-06-04T19:12:03FR652024-06-04T09:10:30482024-06-04T09:10:29v2.7.4
aBcD_20240527Other55190.15trueTransfer2024-05-272024-06-04T14:17:41IN10722024-06-02T17:28:009892024-06-02T17:27:59v2.7.0
user_2024UK78xiOS0falseBudgeting2024-04-012024-06-04T15:32:18GB02024-05-30T17:22:410blankv2.6.9
FinU_20240604AAndroid1120.52falseTransfer2024-02-122024-06-04T11:50:40DE882024-06-02T13:11:55132024-06-02T13:11:54v2.7.2
user_4xRtYz2023Web0falseAnalytics2023-12-132024-06-04T09:16:23US52024-06-03T21:09:450blankv2.7.5
user_Xyz_2024DEAndroid11940.88trueBudgeting2024-02-102024-06-04T18:10:12DE4102024-06-01T18:22:112312024-06-01T18:22:10v2.7.1

What the 200 rows show

from the 200-row sample
  • 28%is_premium_user = true
  • 1,955median transaction_volume
  • 43countries
  • 58median feature_engagement_count
  • 11median transaction_count
device_type200 rows by value
0408051iOS26%71Android36%52Web26%26Other13%
transaction_volume200 rows, in bands of 100k
01002001940110001120500k1Mtransaction_volume →

Median 1,955, from 0.0 to 1,000,000.

feature_name200 rows · 5 values
  1. Budgeting63
  2. Transfer60
  3. Analytics58
  4. Cards11
  5. Investments8
13 columns by typefrom the column list below
  • string 5
  • integer 2
  • float 1
  • date 1
  • datetime 3
  • boolean 1

Columns

13 columns in four groups
blueprint · 13 columns
columntypedescriptionexample
Text 5 columns
user_idstringUnique identifier for each app useruniqueuser_abcD1234
device_typestringType of device used by the user (e.g., iOS, Android, Web)iOS · Android · Web · OtherAndroid
countrystringCountry where the user is locatedUS
feature_namestringName of the app feature engaged with (e.g., Transfer, Budgeting, Analytics)5 namesTransfer
app_versionstringVersion of the app used during the last sessionoptionalv2.7.1
Numbers 3 columns
feature_engagement_countintegerNumber of times the user engaged with the feature0 or more540
transaction_countintegerTotal number of financial transactions made by the user0 or more320
transaction_volumefloatTotal monetary value of transactions made by the user (in app's base currency)0 or more14290.67
Dates and times 4 columns
signup_datedateDate when the user registered on the app2023-11-21
last_active_datetimedatetimeTimestamp of the user's most recent activity in the app2024-06-04T17:32:15
feature_engagement_last_datetimedatetimeTimestamp of the last engagement with the feature2024-06-03T16:44:55
transaction_last_datetimedatetimeTimestamp of the user's most recent transactionoptional2024-06-03T17:01:16
True or false 1 column
is_premium_userbooleanIndicates whether the user has a premium accounttrue

Use it for

  • is premium user28%56 of 200 rowsmean transaction volu…2.2kiOS42.9kAndr…30.4kWeb42.1kOther

    A finance dashboard

    The is_premium_user rate, transaction_volume by device_type and a breakdown of feature_name. Excel, Power BI or Tableau.

  • Why do 56 of 200 rows have is_premium_user = true?

    A class exercise

    Hand out the rows and one question. Everyone works from the same 200 rows.

  • A software demo

    Believable users with signup_date, last_active_datetime and device_type to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 13 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 rows13 columns
Yours1,000 rows13 columnscountry: UK only

blueprint · fintech-app-user-engagement

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 13 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 ID and session times anonymized
  • Feature interaction recorded
  • Transaction types and volumes tracked
  • Churn and retention labels available
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
fintech-app-user-engagement

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