How do teams improve their delivery efficiency?
Metrics that help teams improve delivery and predictability.
Let’s say you are a Product Owner.
And your team is planning a Sprint… again!
“How much can we commit to this sprint?” someone asks you to predict.
You look at your team’s velocity from the last three sprints. You average it out. You throw in a fudge factor because, well, you always do. And then you make your best guess.
The thing is, you’re actually not predicting anything.
You’re just… hoping.
I’ve watched Product Owners struggle with this for years.
“Why did this take three weeks?” or
“How do we get faster?” or
“Where is work actually getting blocked?”
These questions can sometimes be quite difficult to answer.
So…
How do experienced teams answer these questions?
Let’s find out.
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Delivery Metrics for Product Owners
As a Product Owner, your job isn’t just to prioritize what to build.
You’re also on the hook for “when” stakeholders can reasonably expect to see outcomes.
Delivery metrics help you do that.
They give you a clear view of how smoothly work moves through the team from idea to Done, and how consistently the team meets its commitments.
Let’s look at each of these metrics one by one.
#1 Scope Change Inside the Sprint
This is the amount of work added, removed, or changed in scope while a sprint is “in progress.”
A Sprint Backlog that keeps changing makes any velocity based forecast meaningless.
It indicates misalignment between PO, stakeholders, and the team.
To use this metric:
Capture the numbers. Create a simple chart for each Sprint: original commitment vs. final scope
Bring the data to the Retrospective.
Highlight which changes were “true emergencies” versus “nice-to-have”
Discuss what upstream decisions, alignment, or communication can reduce the scope change in future
Example:
Let’s say your Sprint begins with 40 story points across 10 PBIs.
Day 4: Customer support escalates a production defect (5 pts) → added.
Day 6: Marketing drops a low-value UI colour change request (2 pts) → added.
Day 8: Team realizes a planned PBI is obsolete (3 pts) → removed.
Scope change: +7 pts, –3 pts ⇒ net +4 pts (10% increase).
In the Retro, the team decides the following:
The production defect was valid
The colour change could have waited; the PO will (from now on) redirect such requests to the next Sprint unless revenue is at risk
The obsolete item signals we need better grooming with the stakeholder two weeks earlier
#2: Release Frequency and Batch Size
Release Frequency:
How often does the team push increments to users/production?
Batch Size:
How big each release is (number of PBIs per release)?
More frequent, smaller releases:
Reduce risk
Increase feedback speed
Improve the predictability of value reaching users
And it aligns with the PO’s focus on learning and value validation.
How a PO / SM can use it:
Track: how many releases per month/quarter?
Track: average number of PBIs per release
Push toward smaller, more frequent, safer releases, matched to your context/regulation
Example:
Let’s say the current state is:
Releases: 1 every two Sprints
Batch size: ~ 20 PBIs
Post release defect rate: 5 critical bugs/release
Actions the team takes:
PO & Dev Team slice PBIs so each delivers user visible value
DevOps introduces automated tests and blue-green deploys
Team adds working agreement: “Release whenever main branch is green. Aim for daily.”
#3: Forecast Accuracy
This metric measures how often the team delivers what they forecast when they said they would.
There are two perspectives of this metric:
Sprint-level predictability:
% of planned PBIs completed by the end of the sprint (or planned vs. delivered points)Release-level predictability:
“Of the items we said would be in Release X, how many actually made it?”
This metric helps POs credibly keep their promises.
Here’s how:
Every Sprint SM and PO track the following:
Planned vs. Delivered (stories or points)
Whether the Sprint Goal (not just items) was delivered
Then… they use historical data to right-size commitments and avoid overcommitting.
Example:
Let’s say in the last Sprint:
Commitment: 10 PBIs (40 pts).
End of Sprint: 7 PBIs done (30 pts).
Accuracy: 70%
Using that data, the Product Owner redraws the commitment to the 7 (highest-value) PBIs and tells stakeholders:
“Based on history, this gives us an 80% confidence of completion.”
This metric reduces the guesswork.
#4: Defect Escape Rate
Number of defects found in production per release.
Defect Escape Rate provides input for prioritization.
For example, a consistently high escape rate is a strong signal that your team should invest in quality improvements and reducing technical debt.
Example:
Let’s say in the current release cycle:
Releases: 4 over the past 3 months
Escaped defects: 9 total (4 critical, 5 minor)
Escape rate: 2.25 defects/release
How the metric can help:
PO can use the 4 “critical” escapes to justify dedicating 20% of the next Sprint to regression test automation and refactoring.
The Scrum team can use it as a signal to add “automated test in CI” to the DoD.
#5: Backlog Readiness
The proportion of upcoming work that is refined enough to be pulled into a sprint without further adjustments.
There are two perspectives of this metric:
% of items (out of team’s capacity) for the next 1–2 sprints that meet a clear “Ready” checklist
Number of upcoming sprints with sufficiently refined work
Backlog readiness directly affects planning stability. Items that are NOT READY lead to mid sprint surprises and rework.
Example:
Let’s say before Sprint Planning:
Ready PBIs: 18 items = 80 pts
Team’s typical Sprint capacity: 40 pts
Readiness: 200% (≈ 2 full Sprints of pull ready work)
PO notices the backlog is “over groomed.”
So she pauses further detailing beyond the next Sprint, and instead schedules testing sessions to validate the backlog’s assumptions.
Team reallocates refinement time to handle techdebt tasks.
#6: Blocker Frequency and Time Blocked
Blockers are simply work items that get stuck.
Blocker frequency is how many items get blocked in a sprint
Time blocked is how long, on average, they stay stuck
Most blockers are not technical. They’re about decisions.
Things like:
missing clarification or acceptance criteria
stakeholders deciding too late, or
dependencies on other teams
Those are the kinds of issues a Product Owner can often influence or remove.
Example:
Let’s say in the current Sprint:
Commitment: 10 PBIs
Blocker frequency: 4 PBIs entered “Blocked”
Average time blocked: 2.5 days
Further investigation showed that items are pending due to delayed stakeholder approval.
PO can use this data to convince stakeholders and make decisions like setting up a standing 15-minute daily slot with that stakeholder.
#7: Work in Progress
Number of PBIs in progress at any given time.
High WIP = context switching, slower delivery
As PO, you influence how much is started vs. how much is finished. Lower WIP improves flow and predictability.
Example:
Let’s say halfway through the Sprint:
Team capacity: 4 developers
WIP shown on the board: 9 PBIs across In Progress (WIP limit was set to 6 → they are +3 over)
Each dev is juggling 2+ items, so there is a lot of context switching.
What to do?
Seeing WIP = 9, the PO pauses new starts and inquires why the WIP is high.
It is high because “peer review,” which is part of the “in progress” stage, is taking longer than anticipated.
This makes PO take actions such as helping clear review queues by pairing reviewers, and clarifying acceptance criteria.
#8: Sprint Goal Success Rate
The ratio of Sprints whose Sprint Goal was fully achieved to the total number of Sprints observed.
Success Rate = Sprints with Goal Met ÷ Total Sprints × 100 %This is a direct measure of predictability.
This tells how well your team is slicing and planning, and whether it is protected from scope creep.
Example:
Let’s say over the last 6 Sprints:
Goals fully met: 3
Goals partially met: 2
Goals missed: 1
Sprint Goal Success Rate: 50 %
Only half of the Sprints produced the exact outcome the PO promised.
Review of the two “partial” Goals showed both were derailed by scope additions made mid Sprint.
Knowing this, the PO can introduce a working agreement: “No new work enters the Sprint unless the PO and Dev Team agree it’s critical.”
Takeaway
Delivery flow and predictability metrics give you, as a Product Owner, the evidence you need to make better calls:
What can we realistically promise, and by when?
Where is work getting stuck, and what can I do to unblock it?
Are we trading off too much quality for speed?
How confident can we be in our next release?
Used well, these metrics can change the conversation with your stakeholders.
You are no longer required to defend missed dates. You can show how the system is performing, what you’re doing to improve it, and what level of certainty they can expect.
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Winning Strategy provides insights from my experiences at Twitter, Amazon, and my current role as an Executive Product Coach at one of North America’s largest banks.
The backlog readiness metric example really caught my attention. We recently had a similar 200% over refined situation and it felt like spinning wheels. Your suggestion to pause refinement and run validation sessions instead makes a lot more sense than just keep grooming endlessly. Curious how often you've seen teams actualy track this metric consistently, or does it tend to get droped after a few sprints?