Product Metrics

Metrics that matter for product decisions

Investment Metrics

API Available

Engineering Investment by Category

FTE by Investment Category

Feature Development12.4 FTE

Keep the Lights On5.8 FTE

Tech Debt3.2 FTE

Growth / Scaling2.1 FTE

Unallocated1.5 FTE

Allocation by Team

Team allocation breakdown by investment category
Team	Total FTE	Features %	KTLO %	Tech Debt %
Platform	8.2	52%	28%	20%
Mobile	6.1	45%	35%	20%
Data	5.4	60%	20%	20%
Frontend	5.3	48%	32%	20%

Flow Metrics

API Available

Avg Cycle Time

3.2d

across all teams

Avg Lead Time

5.9d

intake to deploy

Avg Deploy Freq

4.3/wk

deployments per week

Teams Tracked

Platform, Mobile, Data, Frontend

Flow by Team

Flow metrics by team — cycle time, lead time, deployment frequency
Team	Cycle Time	Lead Time	Deploy Freq/wk
Platform	3.2d	5.8d	4/wk
Mobile	4.8d	8.2d	2/wk
Data	★ 2.1d	★ 4.5d	★ 6/wk
Frontend	2.8d	5.1d	5/wk

★ = best in class. Lower is better for Cycle Time and Lead Time. Higher is better for Deploy Frequency.

Quality Metrics

Platform Only

Quality by Product

Quality metrics are tracked in the Jellyfish platform and your monitoring tools. The data below is sample data for illustration.

Quality metrics by product — bugs, critical issues, resolution time, uptime
Product	Bugs	Critical	Avg Resolution	Uptime %
Core Platform	12	2	3.5d	99.8%
Mobile App	28	5	5.2d	99.2%
Data Pipeline	6	0	1.8d	99.9%
Public API	9	1	2.4d	99.7%

Progress Metrics

Mixed — some API, some platform

Sprint Completion Rate

API Available

88%

committed vs completed

+5% improvement

Pulled via team_sprint_summary endpoint.

Predicted Ship Dates

Platform Only

Ship date predictions are calculated by the Jellyfish Capacity Planner using velocity history, remaining scope, and team availability. They update automatically as scope changes.

Access predictions in the Capacity Planner module of the Jellyfish platform, or on the Delivery Forecast page in this tool.

As a Scrum Master, you influence many of these metrics through ceremony quality and process health. Cycle time improves when reviews are timely. Deployment frequency rises when release processes are streamlined.

Help your Product Owner interpret these numbers in context. A spike in bugs after a large release is different from a steady upward trend. A low completion rate during onboarding weeks tells a different story than one during a stable sprint.