I have the privilege of working with diverse teams and leaders across a wide range of organizations. Nearly every time, I hear some variation of, “Well, here we’re unique.” They’re right; every organization has its own culture, personality, and story. What’s fascinating is that while people and contexts differ, the core challenges rarely do.
One of the most common? Dashboards that look great but don’t drive better decisions. Most teams excel at tracking output, how much they did, and how fast, but struggle to connect those numbers to outcomes that actually matter. We talk about “measuring outcomes” all the time, yet output remains the comfort zone for most organizations.
When it comes to Product Development, leaders typically do not set out to build “feature factories”. They set out to create certainty. You would, too. Roadmaps become contracts. Status becomes success. Metrics become theater, because that is what we know. Unpredictability doesn’t play well in business, so most of us are trained to avoid it.
To me, though, the focus in 2026 is on credibility: showing evidence that customer behavior changed, and learning quickly when it did not. Let's start making that the norm; I just don't see it being very common... yet.
DORA 2024 is the EU’s Digital Operational Resilience Act, a regulation that raises the bar for how financial institutions and their tech providers manage cyber and operational risk. It standardizes expectations for ICT risk management, incident reporting, and resilience testing so the financial system can keep running even when critical digital services fail or are attacked.
The DORA 2024 research reinforces a broader view of performance: high-performing organizations focus on user-centricity, stable priorities, and feedback loops that help teams improve both delivery and organizational outcomes. DORA 2024 report
And if you favor a plain-English definition of vanity metrics, Nielsen Norman Group nails it; vanity metrics appear impressive but fail to reveal true performance because they are not actionable or not tied to meaningful outcomes. NN/g on vanity metrics
The Starter Kit: 8 outcome signals that beat vanity every time
I don’t think you need 40 KPIs. You need a small set of signals that map cleanly to behavior change. Below are eight outcome signals you can mix and match. Pick 6 to start, then adjust once you learn what actually moves. Most of them will be applicable to your organization, but obviously tweak them to your needs.
What it tells you: How quickly a user reaches a meaningful “aha” moment.
Feature-to-behavior mapping: You add a guided setup wizard. You do not measure “wizard completed.” You measure “first successful workflow completed in under X minutes.”
Example metric: Median time from account creation to first successful workflow; trend weekly.
What it tells you: Whether users can complete the core job without friction.
Feature-to-behavior mapping: You redesign a form. You do not measure “new UI shipped.” You measure successful completion without retries, errors, or escalations.
Example metric: Percent of users who complete the task on the first attempt, plus median time-on-task.
What it tells you: Whether users choose the new behavior, or just see the feature.
Feature-to-behavior mapping: You launch “bulk actions.” Do not track clicks. Track whether the bulk action replaces the old manual workflow.
Example metric: Percent of eligible accounts using the new behavior weekly, broken down by segment.
What it tells you: Whether the behavior sticks after the novelty wears off.
Feature-to-behavior mapping: You add reminders and in-product prompts. Track whether cohorts continue the behavior at 2, 4, or 8 weeks.
Example metric: 4-week retention of “weekly active users who complete the core workflow at least twice.”
What it tells you: Whether you are eliminating avoidable support demand and rework.
Feature-to-behavior mapping: You add in-product guidance or validations. Track whether the top two preventable ticket categories drop.
Example metric: Weekly count of “how do I” and “I am stuck” tickets per 100 active accounts; plus top drivers.
What it tells you: Whether you are buying back time for customers, which is a real outcome in most B2B contexts.
Feature-to-behavior mapping: You add automation. Track reduction in manual steps and time spent, not the number of rules configured.
Example metric: Median minutes per workflow per week, or percent of workflows completed with zero manual intervention.
What it tells you: Whether behavior change shows up in revenue, renewal, risk reduction, or cost.
Feature-to-behavior mapping: You improve onboarding. Link it to renewal rates, expansion, or reduced churn for cohorts exposed to the change.
Example metric: 90-day retention or renewal lift in the cohort that reached first value in under X minutes.
What it tells you: Whether users believe the system is dependable enough to change their behavior.
Feature-to-behavior mapping: You ship reliability improvements or clearer status reporting. Track whether users stop building workarounds.
Example metric: Survey-based confidence score for “I trust this workflow”; plus reduction in manual bypass behaviors.
How to spot vanity metrics fast
Here are three quick tests. If a metric fails any of them, it is probably vanity.
- Decision test: If it moved 20% up or down, would you change what you do next week?
- Behavior test: Does it reflect a real user behavior change, or just activity inside the product?
- Context test: Can you segment it by user type, journey stage, or cohort, or is it just one big number?
Do this Monday morning: 25 minutes - no excuses
- Pick one roadmap item. Write it as a one-sentence hypothesis: “We believe [change] will cause [behavior] for [user], measured by [signal] in [time].”
- Choose two outcome signals. One leading, one lagging. Example: task success rate (leading) and renewal lift (lagging) for the cohort.
- Choose one guardrail. Pick one DORA metric you will not sacrifice while you learn.
- Define the “prove it” threshold. What change would convince you to continue; what result would cause you to pivot or stop?
- Schedule a 30-minute Outcome Review. Same time, every week. Make one decision per hypothesis. That is how feature factories die.
Outcome hypothesis:
We believe __________ will cause __________ for __________,
measured by __________ within __________.
Leading outcome signal (behavior):
- __________ (definition, segment, time window)
Lagging outcome signal (result):
- __________ (definition, cohort, time window)
Delivery guardrail (DORA):
- __________ (lead time | deploy frequency | change failure rate | time to restore)
Decision rule:
- Continue if __________
- Pivot if __________
- Stop if __________
A closing thought
The point of outcome metrics is not to build a prettier dashboard. It is to make decisions easier, faster, and more honest. When you measure real outcomes, trade‑offs become clearer, priorities stop shifting with the loudest voice in the room, and teams can align around evidence instead of opinions.
Outcome metrics turn strategy into something you can actually steer with. They help you see whether your hypotheses are paying off, when to pivot or persevere, and where to stop investing altogether, long before vanity graphs would ever reveal a problem.
If your metrics do not change what you do next week, they are not helping. They are decorations, and that’s normal. Let’s not be normal.