Granola Observability
Overview
Monitor Granola usage, track meeting patterns, and build analytics dashboards. Granola Enterprise includes a usage analytics dashboard. For deeper insights, build custom pipelines using Zapier to stream meeting metadata to BigQuery, Metabase, or other analytics platforms.
Prerequisites
- Granola Business or Enterprise plan
- Admin access for organization-level analytics
- Optional: BigQuery/Metabase for custom dashboards, Zapier for data pipeline
Instructions
Step 1 — Built-in Analytics (Enterprise)
Access the analytics dashboard at Settings > Analytics (Enterprise plan):
| Metric | What It Shows |
|---|
| Total meetings captured | Meeting volume over time |
| Active users | Users who recorded meetings this period |
| Hours captured | Total meeting hours transcribed |
| Notes shared | How often notes are distributed |
| Action items created | Extracted action items across org |
| Adoption rate | Active users / total licensed seats |
Step 2 — Define Key Metrics
Track these metrics to measure Granola's impact:
| Category | Metric | Target | Formula |
|---|
| Adoption | Activation rate | >80% | Users with 1+ meeting / total seats |
| Adoption | Weekly active users | >70% | Users recording this week / total seats |
| Quality | Capture rate | >70% | Meetings captured / total calendar meetings |
| Quality | Share rate | >50% | Notes shared / notes created |
| Efficiency | Time saved | >10 min/meeting | Survey: manual notes time - Granola time |
| Efficiency | Action completion | >80% | Actions completed / actions created |
| Health | Processing success | >99% | Successful enhancements / total attempts |
| Health | Integration uptime | >99% | Successful syncs / total sync attempts |
Step 3 — Build a Custom Analytics Pipeline
Stream meeting metadata from Granola to a data warehouse via Zapier:
Trigger: Granola — Note Added to Folder ("All Meetings")
Step 1 — Code by Zapier (extract metadata):
const data = {
meeting_id: inputData.title + '_' + inputData.calendar_event_datetime,
title: inputData.title,
date: inputData.calendar_event_datetime,
creator: inputData.creator_email,
attendee_count: JSON.parse(inputData.attendees || '[]').length,
has_action_items: inputData.note_content.includes('- [ ]'),
action_item_count: (inputData.note_content.match(/- \[ \]/g) || []).length,
has_decisions: inputData.note_content.includes('
inputData.note_content.includes('## Key Decision'),
word_count: inputData.note_content.split(/\s+/).length,
is_external: JSON.parse(inputData.attendees || '[]')
.some(a =>
,
,
}
[]
{{ }}
BigQuery schema:
CREATE TABLE meeting_analytics.granola_meetings (
meeting_id STRING NOT NULL,
title STRING,
date TIMESTAMP,
creator STRING,
attendee_count INT64,
has_action_items BOOL,
action_item_count INT64,
has_decisions BOOL,
word_count INT64,
is_external BOOL,
workspace STRING,
captured_at TIMESTAMP
);
Step 4 — Analytics Queries
SELECT
workspace,
DATE_TRUNC(date, WEEK) AS week,
COUNT(*) AS meeting_count,
SUM(action_item_count) AS total_actions,
AVG(attendee_count) AS avg_attendees
FROM meeting_analytics.granola_meetings
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 12 WEEK)
GROUP BY workspace, week
ORDER BY week DESC, workspace;
SELECT
DATE_TRUNC(date, WEEK) AS week,
COUNT(DISTINCT creator) AS active_users
FROM meeting_analytics.granola_meetings
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 8 WEEK)
GROUP BY week
ORDER BY week DESC;
SELECT
title,
date,
CASE
WHEN has_action_items AND has_decisions AND attendee_count <=
has_action_items has_decisions
efficiency_rating
meeting_analytics.granola_meetings
LIMIT ;
DATE_TRUNC(, ) ,
COUNTIF(is_external) external_meetings,
COUNTIF( is_external) internal_meetings,
ROUND(COUNTIF(is_external) (), ) external_pct
meeting_analytics.granola_meetings
;
Step 5 — Automated Reporting
Weekly Slack digest (via Zapier Schedule):
Trigger: Schedule by Zapier — Every Friday at 5 PM
Step 1 — BigQuery: Run Query
Query: "SELECT COUNT(*) as meetings, SUM(action_item_count) as actions,
COUNT(DISTINCT creator) as active_users
FROM meeting_analytics.granola_meetings
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)"
Step 2 — Slack: Send Message to
Message: |
:bar_chart: *Weekly Granola Report*
*This Week:*
- Meetings captured: {{meetings}}
- Action items created: {{actions}}
- Active users: {{active_users}}
[View full dashboard →]
Step 6 — Health Monitoring and Alerts
Set up alerts for operational issues:
| Alert | Condition | Channel |
|---|
| Low adoption | Active users <50% of seats (weekly) | Slack #it-alerts |
| Processing failures | >5% enhancement failures (daily) | PagerDuty |
| Integration outage | Slack/Notion/CRM sync failures >3 (hourly) | Slack #it-alerts |
| Zero meetings captured | No meetings for any workspace (daily) | Email to workspace admin |
Status monitoring:
curl -s https://status.granola.ai/api/v2/status.json | python3 -c "
import json, sys
data = json.load(sys.stdin)
status = data.get('status', {}).get('description', 'Unknown')
print(f'Granola Status: {status}')
"
Output
- Built-in analytics reviewed and baselines established
- Custom analytics pipeline streaming to data warehouse
- Dashboard visualizing adoption, efficiency, and meeting patterns
- Automated weekly/monthly reports delivered to stakeholders
- Health monitoring alerts configured for operational issues
Error Handling
| Error | Cause | Fix |
|---|
| Missing data in pipeline | Zapier trigger failed | Check Zap history, reconnect if needed |
| Duplicate entries in BigQuery | Zapier retry on timeout | Add deduplication (MERGE or INSERT IGNORE) |
| Dashboard shows stale data | Pipeline paused | Monitor Zapier health, restart paused Zaps |
| Low adoption alert false positive | New seats just added | Adjust alert threshold, use percentage not absolute |
Resources
Next Steps
Proceed to granola-incident-runbook for incident response procedures.
