Remote Moderator Productivity Metrics
This dataset provides granular productivity metrics for remote social media moderators, including task completion rates, flagged and escalated content, time spent per task, and AI assistance usage. It enables detailed analysis of individual and team performance, supports operational efficiency initiatives, and informs workforce management strategies in distributed moderation environments.
Sample rows
preview · 8 of 75 rows · all 16 columns| moderator_idstring | ai_assist_percentagefloat | team_idstring | moderator_namestring | datedate | shift_start_timedatetime | shift_end_timedatetime | total_tasks_completedinteger | tasks_flaggedinteger | tasks_approvedinteger | tasks_escalatedinteger | average_time_per_task_secondsfloat | total_time_on_task_secondsinteger | ai_assist_usage_countinteger | manual_override_countinteger | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MOD001 | 52.4 | TEAM_A | Jessica Warren | 2024-05-01 | 2024-05-01T08:00:00 | 2024-05-01T16:00:00 | 82 | 17 | 55 | 6 | 78.2 | 6400 | 43 | 3 | Routine shift, moderate flagged content. |
| MOD002 | 42.9 | TEAM_B | Carlos Vega | 2024-05-01 | 2024-05-01T18:00:00 | 2024-05-01T02:00:00 | 77 | 21 | 50 | 4 | 70.1 | 5400 | 33 | 2 | High volume flagged tasks during late shift. |
| MOD003 | 58.8 | TEAM_C | Priya Desai | 2024-05-01 | 2024-05-01T09:00:00 | 2024-05-01T17:00:00 | 85 | 14 | 60 | 7 | 63.5 | 5400 | 50 | 1 | Shift smooth, AI assist increased efficiency. |
| MOD004 | 36.8 | TEAM_A | Mikhail Petrov | 2024-05-02 | 2024-05-02T07:00:00 | 2024-05-02T15:00:00 | 68 | 9 | 52 | 4 | 81.6 | 5550 | 25 | 2 | Fewer escalations, mostly approvals. |
| MOD005 | 27.4 | TEAM_B | Helena Schmidt | 2024-05-02 | 2024-05-02T16:00:00 | 2024-05-02T00:00:00 | 73 | 16 | 54 | 3 | 76.7 | 5600 | 20 | 1 | Evening shift, flagged content lower than average. |
| MOD006 | 66.7 | TEAM_C | Fatima Nasser | 2024-05-02 | 2024-05-02T10:00:00 | 2024-05-02T18:00:00 | 90 | 20 | 62 | 6 | 55.6 | 5000 | 60 | 4 | High AI assist usage, efficient shift. |
| MOD007 | 48.4 | TEAM_A | Tommy Lee | 2024-05-03 | 2024-05-03T08:00:00 | 2024-05-03T16:00:00 | 62 | 13 | 44 | 4 | 80.6 | 5000 | 30 | 0 | AI assist mostly reliable, low overrides. |
| MOD008 | 60 | TEAM_B | Emily Chen | 2024-05-03 | 2024-05-03T17:00:00 | 2024-05-03T01:00:00 | 70 | 19 | 48 | 3 | 60 | 4200 | 42 | 5 | Late shift, more flagged than usual. |
| MOD009 | 61.4 | TEAM_C | Brian O'Connor | 2024-05-03 | 2024-05-03T09:00:00 | 2024-05-03T17:00:00 | 88 | 18 | 65 | 5 | 68.2 | 6000 | 54 | 2 | Routine day, moderate escalation. |
| MOD010 | 46.7 | TEAM_A | Sara Kim | 2024-05-04 | 2024-05-04T08:30:00 | 2024-05-04T16:30:00 | 60 | 11 | 45 | 4 | 86.7 | 5200 | 28 | 0 | Light shift, mostly approvals. |
| MOD011 | 48.6 | TEAM_B | Mohammed Al-Farsi | 2024-05-04 | 2024-05-04T17:00:00 | 2024-05-04T01:00:00 | 74 | 21 | 50 | 3 | 67.6 | 5000 | 36 | 2 | Shift with high flagged content. |
| MOD012 | 71.4 | TEAM_C | Anna Ivanova | 2024-05-04 | 2024-05-04T10:00:00 | 2024-05-04T18:00:00 | 84 | 15 | 63 | 6 | 61.9 | 5200 | 60 | 6 | AI assist use high, several overrides. |
| MOD013 | 42.7 | TEAM_A | David Martinez | 2024-05-05 | 2024-05-05T08:00:00 | 2024-05-05T16:00:00 | 75 | 17 | 52 | 5 | 75.3 | 5650 | 32 | 3 | Shift smooth, flagged content moderate. |
| MOD014 | 23.2 | TEAM_B | Isabelle Dupont | 2024-05-05 | 2024-05-05T16:00:00 | 2024-05-05T00:00:00 | 69 | 12 | 53 | 4 | 79.7 | 5500 | 16 | 1 | Low AI assist, mostly manual moderation. |
| MOD015 | 75 | TEAM_C | Jamal Greene | 2024-05-05 | 2024-05-05T10:00:00 | 2024-05-05T18:00:00 | 88 | 22 | 60 | 6 | 56.8 | 5000 | 66 | 2 | AI assist contributed to high productivity. |
| MOD016 | 31.3 | TEAM_A | Karin Andersson | 2024-05-06 | 2024-05-06T07:30:00 | 2024-05-06T15:30:00 | 64 | 9 | 50 | 5 | 87.5 | 5600 | 20 | 0 | Morning shift, low flagged content. |
| MOD017 | 43.2 | TEAM_B | Ravi Singh | 2024-05-06 | 2024-05-06T18:00:00 | 2024-05-07T02:00:00 | 81 | 18 | 56 | 7 | 66.7 | 5400 | 35 | 3 | Efficient moderation, flagged items high. |
| MOD018 | 73.1 | TEAM_C | Linda Johnson | 2024-05-06 | 2024-05-06T09:00:00 | 2024-05-06T17:00:00 | 93 | 23 | 64 | 6 | 53.8 | 5000 | 68 | 5 | AI assist heavily relied upon. |
| MOD019 | 33.3 | TEAM_A | Elena Rivera | 2024-05-07 | 2024-05-07T08:00:00 | 2024-05-07T16:00:00 | 57 | 10 | 41 | 4 | 94.7 | 5400 | 19 | 1 | Light shift, slow content flow. |
| MOD020 | 54.1 | TEAM_B | George Papadopoulos | 2024-05-07 | 2024-05-07T17:00:00 | 2024-05-07T01:00:00 | 74 | 21 | 50 | 3 | 70.3 | 5200 | 40 | 2 | Late shift, flagged items up. |
What the 75 rows show
from the 75-row sample- 38.3median ai_
assist_ percentage - 68median total_
tasks_ completed - 13median tasks_
flagged - 50median tasks_
approved - 6median tasks_
escalated - 62.2median average_
time_ per_ task_ seconds
Median 38.3, from 17.4 to 76.4.
- string 4
- integer 7
- float 2
- date 1
- datetime 2
Columns
16 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 4 columns | |||
moderator_id | string | Unique identifier for each remote moderator | MOD001 |
moderator_name | string | Full name of the moderatoroptional | Jessica Warren |
team_id | string | Identifier for the moderator's team or groupoptional | TEAM_A |
notes | string | Additional notes or comments regarding the shift or moderation activityoptional | No escalations required. |
| Numbers 9 columns | |||
total_tasks_completed | integer | Total number of moderation tasks completed during the shift0 or more | 82 |
tasks_flagged | integer | Number of content items flagged as inappropriate or requiring review0 or more | 17 |
tasks_approved | integer | Number of content items approved as appropriate0 or more · optional | 55 |
tasks_escalated | integer | Number of content items escalated to higher-level review0 or more · optional | 6 |
average_time_per_task_seconds | float | Average time spent per moderation task, in seconds0 or more | 78.2 |
total_time_on_task_seconds | integer | Total time spent on moderation tasks during the shift, in seconds0 or more | 6400 |
ai_assist_usage_count | integer | Number of times AI assistance was used during moderation0 or more | 43 |
ai_assist_percentage | float | Percentage of tasks where AI assistance was used (0-100)0 to 100 · optional | 52.4 |
manual_override_count | integer | Number of times the moderator overrode an AI recommendation0 or more · optional | 3 |
| Dates and times 3 columns | |||
date | date | Date for which the productivity metrics are recorded | 2024-05-01 |
shift_start_time | datetime | Start time of the moderator's shiftoptional | 2024-05-01T08:00:00 |
shift_end_time | datetime | End time of the moderator's shiftoptional | 2024-05-01T16:00:00 |
Use it for
A social media dashboard
Ai_
assist_ percentage and a breakdown of team_ id. Excel, Power BI or Tableau. What pattern can you find in 75 rows?
A class exercise
Hand out the rows and one question. Everyone works from the same 75 rows.
- Moderators75MOD00152.4MOD00242.9MOD00358.8
A software demo
Believable moderators with moderator_
name, team_ id and date to fill a screen in front of a buyer.
This dataset75 rows16 columns
Yours1,000 rows16 columns
blueprint · remote-moderator-productivity-metrics
Behind this dataset
Same schema. As many rows as you need.
These 75 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
- Each row represents one moderator's daily activity summary.
- Metrics include tasks completed, flagged posts, review accuracy, and break duration.
- Moderators are anonymized using unique IDs.
- AI-assist frequency and escalation rates are tracked as separate columns.
- Entries include date and work shift type for temporal analysis.
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
- remote-moderator-productivity-metrics