North Star Metric Finder

North Star Metric Finder

Identify a candidate North Star Metric (NSM) that satisfies five strict criteria, then map the 3-5 input metrics that drive it. The output is a defensible recommendation, not a brainstormed list.

Core Principle

A North Star Metric is a forcing function, not a slogan. Adopting the wrong one quietly skews two years of roadmap decisions. The five criteria below exist to eliminate candidates that look reasonable but fail under stress.

The Five Criteria

| Criterion | The Question | Failure Example | |-----------|--------------|-----------------| | Value-capturing | Does this metric only go up when customers receive real value? | "Logins per week" - easy to game with notification spam | | Predictive | Does this metric lead revenue and retention by ≥1 quarter? | "MRR" - lagging, not leading | | Actionable | Can the team move this metric with deliberate actions? | "NPS" - moves slowly, hard to attribute changes | | Understandable | Can every employee state the metric and why it matters in one sentence? | "Weekly active accounts with ≥3 successful API calls in their primary workspace" | | Singular | Is this a single metric, not an index of three? | A weighted composite hides which input is broken |

A candidate must pass all five. Three out of five is not a NSM - it's a useful KPI.

Output

Save to outputs/nsm-[product]-[YYYY-MM-DD].md

• Recommended NSM + the five-criteria pass/fail evaluation
• Runner-up candidates with the criterion they failed and why
• Input metrics (3-5) that drive the NSM, with the direction and expected magnitude
• Counter-metrics that prevent gaming (e.g., NSM ↑ + churn ↑ = bad)
• Cadence - how often to review the NSM and re-validate it

Process

Step 1: Generate Candidates

I'll ask: > "What does your product do for the customer? What's the moment they get value? Share your current top-line metrics and a one-sentence pitch."

I generate 6-10 candidate NSMs grounded in the value moment, not the business model.

Step 2: Five-Criteria Pass

Score each candidate pass/fail on all five criteria. Eliminate any with a fail.

Step 3: Map Input Metrics

For the top candidate, identify the 3-5 metrics that cause it to move (e.g., "weekly active value-events" might be driven by activation rate, frequency, expansion of use cases).

Step 4: Counter-Metric Pairing

Every NSM needs a guardrail. Pair the NSM with 1-2 counter-metrics that catch the most likely gaming pattern.

Step 5: Stress Test

Walk through three scenarios and ask: would adopting this NSM lead to a better decision than the current top metric? If not, re-open candidates.

Tips

1. The value moment matters more than the business moment. "Subscription started" is the business moment; "first successful workflow completed" is the value moment. The latter is almost always the better NSM.
2. Beware composite metrics. They feel comprehensive but obscure root causes. If you can't avoid one, score the components separately too.
3. Re-validate annually. Product evolves; the value moment can shift.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: varunk130
• Source: varunk130/ai-customer-discovery-skills
• License: MIT
• Homepage: https://github.com/varunk130

Install and usage instructions live in the source repository linked above.