User Engagement Frequency Dataset
This dataset provides detailed user-level engagement metrics, including session frequency, feature adoption, and activity recency, enabling identification of power users and analysis of retention patterns. With cohort, platform, and geographic segmentation, it is ideal for optimizing user engagement strategies, tracking feature adoption, and supporting data-driven product decisions.
Sample rows
preview · 8 of 100 rows · all 11 columns| user_idstring | retention_cohortstring | feature_adoption_countinteger | power_user_flagboolean | countrystring | signup_datedate | last_active_datedate | total_sessionsinteger | average_session_duration_minutesfloat | days_active_past_30integer | platformstring |
|---|---|---|---|---|---|---|---|---|---|---|
| USR10001 | 2024-Q1 | 7 | true | United States | 2024-02-13 | 2024-06-06 | 112 | 23.6 | 28 | web |
| USR10002 | 2024-06 | 1 | false | Canada | 2024-06-01 | 2024-06-05 | 7 | 8.1 | 4 | ios |
| USR10003 | 2024-Q1 | 8 | true | Germany | 2024-03-20 | 2024-06-06 | 96 | 31.4 | 27 | android |
| USR10004 | 2024-05 | 2 | false | United Kingdom | 2024-05-14 | 2024-06-04 | 14 | 10 | 9 | web |
| USR10005 | 2024-Q1 | 10 | true | Australia | 2024-01-27 | 2024-06-06 | 148 | 37.2 | 30 | android |
| USR10006 | 2024-06 | 1 | false | France | 2024-06-03 | 2024-06-06 | 4 | 5.5 | 3 | ios |
| USR10007 | 2024-Q2 | 3 | false | India | 2024-04-19 | 2024-06-04 | 29 | 14.9 | 15 | web |
| USR10008 | 2024-05 | 2 | false | United States | 2024-05-07 | 2024-06-01 | 19 | 9.8 | 10 | ios |
| USR10009 | 2024-Q1 | 9 | true | Brazil | 2024-02-28 | 2024-06-05 | 123 | 27 | 29 | android |
| USR10010 | 2024-06 | 2 | false | Japan | 2024-05-29 | 2024-06-03 | 6 | 6.4 | 3 | other |
| USR10011 | 2024-Q1 | 7 | true | United States | 2024-03-10 | 2024-06-06 | 102 | 22.7 | 24 | web |
| USR10012 | 2024-05 | 1 | false | Spain | 2024-05-15 | 2024-05-30 | 10 | 12.3 | 7 | android |
| USR10013 | 2024-06 | 0 | false | Italy | 2024-06-02 | 2024-06-04 | 5 | 4.2 | 2 | web |
| USR10014 | 2024-Q2 | 6 | true | Mexico | 2024-03-22 | 2024-06-05 | 86 | 18 | 22 | ios |
| USR10015 | 2024-Q2 | 3 | false | United Arab Emirates | 2024-04-05 | 2024-06-03 | 45 | 15.6 | 17 | android |
| USR10016 | 2024-05 | 1 | false | France | 2024-05-20 | 2024-05-25 | 6 | 6.9 | 3 | web |
| USR10017 | 2024-Q1 | 10 | true | United States | 2024-01-14 | 2024-06-06 | 157 | 34.3 | 29 | android |
| USR10018 | 2024-06 | 0 | false | Germany | 2024-06-01 | 2024-06-01 | 1 | 2.7 | 1 | other |
| USR10019 | 2024-Q1 | 5 | false | India | 2024-02-24 | 2024-05-30 | 68 | 18.4 | 15 | ios |
| USR10020 | 2024-Q2 | 7 | true | Brazil | 2024-04-08 | 2024-06-06 | 57 | 12.8 | 23 | web |
What the 100 rows show
from the 100-row sample2023-Q4 (retention cohort) stands out: mean feature_
- 36%power_
user_ flag = true - 4median feature_
adoption_ count - 4platforms
- 41.5median total_
sessions - 15.5median average_
session_ duration_ minutes - 13.5median days_
active_ past_ 30
Median 4, from 0 to 15.
- string 4
- integer 3
- float 1
- date 2
- boolean 1
Columns
11 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 4 columns | |||
user_id | string | Unique identifier for each user in the system.unique | USR10001 |
retention_cohort | string | Cohort label indicating the user's signup period (e.g., '2024-Q1', '2024-06').6 cohorts · optional | 2024-Q1 |
country | string | Country associated with the user's primary location.optional | United States |
platform | string | Platform used by the user (e.g., 'web', 'ios', 'android').web · ios · android · other · optional | web |
| Numbers 4 columns | |||
total_sessions | integer | Total number of distinct sessions or logins by the user.0 or more | 112 |
average_session_duration_minutes | float | Average duration of a user session in minutes.0 or more · optional | 23.6 |
days_active_past_30 | integer | Number of days the user was active in the past 30 days.0 to 30 | 28 |
feature_adoption_count | integer | Number of unique features the user has adopted or used.0 or more · optional | 7 |
| Dates and times 2 columns | |||
signup_date | date | The date the user registered or created their account. | 2024-02-13 |
last_active_date | date | The most recent date the user was active in the system. | 2024-06-06 |
| True or false 1 column | |||
power_user_flag | boolean | Indicates whether the user qualifies as a power user based on engagement criteria. | true |
Use it for
A technology dashboard
The power_
user_ flag rate, feature_ adoption_ count by retention_ cohort and a breakdown of country. Excel, Power BI or Tableau. Why do the 10 2023-Q4 rows have a mean feature_
adoption_ count of 11.7? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Users100USR1000172024-Q1USR1000212024-06USR1000382024-Q1
A software demo
Believable users with signup_
date, last_ active_ date and total_ sessions to fill a screen in front of a buyer.
blueprint · user-engagement-frequency-dataset
Behind this dataset
Same schema. As many rows as you need.
These 100 rows came out of a blueprint — 11 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.
- Each user must have at least three activities
- Flag users inactive >14 days
- Aggregate by daily, weekly, monthly frequency
- Highlight top 5% most active users
- Capture device type for each session
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
- user-engagement-frequency-dataset