Software Feature Usage Frequency
This dataset provides detailed, event-level tracking of software feature usage, including user, feature, session, platform, and application version context. It enables product teams to analyze feature adoption, identify usage trends, and prioritize development or training efforts based on real user behavior.
Sample rows
preview · 8 of 100 rows · all 10 columns| usage_idstring | user_rolestring | usage_countinteger | app_versionstring | user_idstring | feature_idstring | feature_namestring | usage_timestampdatetime | session_idstring | platformstring |
|---|---|---|---|---|---|---|---|---|---|
| c1a2b3d4-1e2f-4a5b-9c1d-2e3f4a5b6c7d | viewer | 1 | v2.3.1 | user_1028 | FTR-001 | Dashboard View | 2024-05-17T09:21:33 | sess-9af2e4e1 | web |
| d3e2f1a4-973b-4c1a-8f2d-7c6b5a4e3d2c | editor | 2 | v2.2.9 | user_1009 | FTR-003 | Export Report | 2024-05-16T14:44:57 | sess-7b8c9d0e | desktop |
| a5b6c7d8-2e1f-4c3b-9a0b-1c2d3e4f5a6b | admin | 1 | v2.3.0 | user_1043 | FTR-002 | User Management | 2024-05-11T16:15:23 | sess-261d7e16 | web |
| b7c8d9e0-6a7b-4e1d-9c2f-3a4b5e6d7c8f | viewer | 1 | v2.2.8 | user_1001 | FTR-005 | Notifications | 2024-05-18T10:02:14 | sess-0f1e2d3c | android |
| e1f2a3b4-5c6d-7e8f-9a0b-1c2d3e4f5a6b | editor | 3 | v2.3.1 | user_1040 | FTR-004 | Settings | 2024-05-13T19:35:08 | sess-4d5e6f7a | ios |
| f9e8d7c6-5b4a-3f2e-1d0c-9b8a7e6d5c4b | viewer | 1 | v2.3.0 | user_1015 | FTR-003 | Export Report | 2024-05-17T11:55:47 | sess-3c4b5a6d | web |
| c2d3e4f5-6a7b-8c9d-0e1f-2a3b4c5d6e7f | admin | 1 | v2.3.1 | user_1012 | FTR-002 | User Management | 2024-05-15T13:32:21 | sess-5e6d7c8b | desktop |
| g7h8i9j0-1a2b-3c4d-5e6f-7g8h9i0j1k2l | editor | 1 | v2.2.8 | user_1004 | FTR-001 | Dashboard View | 2024-05-10T08:43:12 | sess-6f7g8h9i | web |
| h1i2j3k4-5l6m-7n8o-9p0q-1r2s3t4u5v6w | viewer | 2 | v2.3.1 | user_1023 | FTR-005 | Notifications | 2024-05-16T18:11:59 | sess-2b3c4d5e | android |
| i2j3k4l5-6m7n-8o9p-0q1r-2s3t4u5v6w7x | admin | 1 | v2.2.9 | user_1031 | FTR-002 | User Management | 2024-05-12T13:09:48 | sess-1a2b3c4d | desktop |
| j3k4l5m6-7n8o-9p0q-1r2s-3t4u5v6w7x8y | editor | 2 | v2.3.1 | user_1055 | FTR-004 | Settings | 2024-05-19T09:51:38 | sess-3e4f5a6b | ios |
| k4l5m6n7-8o9p-0q1r-2s3t-4u5v6w7x8y9z | editor | 1 | v2.3.0 | user_1020 | FTR-003 | Export Report | 2024-05-15T17:38:26 | sess-7g8h9i0j | web |
| l5m6n7o8-9p0q-1r2s-3t4u-5v6w7x8y9z0a | viewer | 1 | v2.3.1 | user_1017 | FTR-001 | Dashboard View | 2024-05-13T20:44:37 | sess-6h7i8j9k | web |
| m6n7o8p9-0q1r-2s3t-4u5v-6w7x8y9z0a1b | viewer | 1 | v2.2.8 | user_1045 | FTR-005 | Notifications | 2024-05-11T15:25:08 | sess-9g8h7f6e | android |
| n7o8p9q0-1r2s-3t4u-5v6w-7x8y9z0a1b2c | editor | 2 | v2.3.0 | user_1010 | FTR-004 | Settings | 2024-05-18T08:39:21 | sess-1e2d3c4b | ios |
| o8p9q0r1-2s3t-4u5v-6w7x-8y9z0a1b2c3d | admin | 1 | v2.3.0 | user_1050 | FTR-002 | User Management | 2024-05-16T10:28:52 | sess-4c3b2a1d | desktop |
| p9q0r1s2-3t4u-5v6w-7x8y-9z0a1b2c3d4e | editor | 1 | v2.2.9 | user_1007 | FTR-003 | Export Report | 2024-05-15T09:12:39 | sess-5b6a7c8d | web |
| q0r1s2t3-4u5v-6w7x-8y9z-0a1b2c3d4e5f | viewer | 2 | v2.3.1 | user_1038 | FTR-001 | Dashboard View | 2024-05-17T13:48:03 | sess-8g9h0i1j | web |
| r1s2t3u4-5v6w-7x8y-9z0a-1b2c3d4e5f6g | viewer | 1 | v2.3.0 | user_1019 | FTR-005 | Notifications | 2024-05-14T21:11:55 | sess-3b2a1c0d | android |
| s2t3u4v5-6w7x-8y9z-0a1b-2c3d4e5f6g7h | admin | 3 | v2.2.8 | user_1042 | FTR-004 | Settings | 2024-05-12T16:37:59 | sess-5e4f3d2c | ios |
What the 100 rows show
from the 100-row sample- 1median usage_
count - 4platforms
- 12feature names
- 14features
Median 1, from 1 to 4.
- string 8
- integer 1
- datetime 1
Columns
10 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
usage_id | string | Unique identifier for each feature usage event recordunique | B4ER-8c9w9A |
user_id | string | Unique identifier for the user who used the feature | user_1028 |
feature_id | string | Unique identifier for the software feature | FTR-001 |
feature_name | string | Human-readable name of the software feature12 names | Dashboard View |
session_id | string | Identifier for the user session during which the feature was usedoptional | sess-9af2e4e1 |
platform | string | Platform or device type from which the feature was accessed (e.g., web, iOS, Android, desktop)web · ios · android · desktop · other · optional | web |
app_version | string | Application version at the time of feature usageoptional | v2.3.1 |
user_role | string | Role or permission level of the user (e.g., admin, editor, viewer)3 roles · optional | viewer |
| Numbers 1 column | |||
usage_count | integer | Number of times the feature was used in this event (typically 1, but can be batched)1 or more | 1 |
| Dates and times 1 column | |||
usage_timestamp | datetime | Date and time when the feature was used | 2024-05-17T09:21:33 |
Use it for
A technology dashboard
Usage_
count by user_ role and a breakdown of app_ version. Excel, Power BI or Tableau. What pattern can you find in 100 rows?
A class exercise
Hand out the rows and one question. Everyone works from the same 100 rows.
- Usages100c1a2b3d4-1e2f-4a5b-9c1d-2e3f4a5b6c7d1viewerd3e2f1a4-973b-4c1a-8f2d-7c6b5a4e3d2c2editora5b6c7d8-2e1f-4c3b-9a0b-1c2d3e4f5a6b1admin
A software demo
Believable usages with user_
id, feature_ id and feature_ name to fill a screen in front of a buyer.
This dataset100 rows10 columns
Yours1,000 rows10 columns
blueprint · software-feature-usage-frequency
Behind this dataset
Same schema. As many rows as you need.
These 100 rows came out of a blueprint — 10 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
- Record feature accessed with timestamp
- Track user type (admin, basic)
- Flag features used by <10% of users
- Log session duration
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
- software-feature-usage-frequency