success-metrics

Success Metrics

Detect the user's language and reply in it; the framework below is authored in English.

Provenance: when you produce success-metrics output, contribute the framework tags North Star, Aha Moment, and Sean Ellis to the meta-skill's provenance line (— Frameworks: … · North Star · Aha Moment · Sean Ellis · …).

Framework

4.2 Success Metrics Framework (North Star + Three-Layer Signals)

A North Star metric must satisfy:

• Reflects the real value users receive (not a vanity metric)
• Can grow continuously (doesn't hit a natural ceiling)
• Aligns the entire team around a single objective

| Company | North Star Metric | Why This Metric |
|---------|-------------------|-----------------|
| Airbnb | Nights booked | Represents value delivered to both hosts and guests |
| Spotify | Monthly listening hours | Represents users genuinely using and enjoying music |
| Facebook | DAU / MAU ratio | Represents habitual return visits |
| Slack | Messages sent per week | Represents teams genuinely collaborating |
| Salesforce | Active customer ACV (Annual Contract Value) | Represents customers continuously deriving business value (B2B) |

Your North Star Metric:

North Star Metric: [A single number representing the core value created for users and the product]
Definition: [Precise calculation method]
Why this metric: [Explain why it represents real user value beyond a business outcome]

📝 North Star Quality Checklist

• ✅ Does it reflect the value users receive? (Revenue and DAU fail this test)
• ✅ Can it grow continuously? (Doesn't hit a natural ceiling)
• ✅ Does everyone on the team know what to do when they see this metric?
• ✅ Can it be gamed? (If yes, guardrail metrics are needed)
• ✅ B2B products: Does it reflect value at the organizational level, beyond individual users?

Common pitfalls worth double-checking: using revenue as the North Star (revenue is a lagging business outcome; a North Star metric should track the upstream user behavior that creates that value), or landing on a metric too composite to act on.

Three-Layer Signal System (the layers build on each other):

| Layer | Metric Type | Definition | B2C Target | B2B Target |
|---|---|---|---|---|
| Layer 1 (Prerequisite) | Core Action Success Rate | Did the user complete the product's core action? | 30–40%+ | 60–80%+ (users are more motivated) |
| Layer 2 (Value Proxy) | D14 / D28 Retention Rate | Do users keep coming back? | Consumer products 15–20%+ | Logo retention 90%+; Net Revenue Retention 100%+ |
| Layer 3 (Passion Signal) | Sean Ellis Score | "If you could no longer use this product, how disappointed would you be?" | 40%+ answer "very disappointed" | 40%+ answer "very disappointed" |
| Guardrail Metrics | Prevent over-optimization | Ensure other important dimensions aren't harmed | Depends on context | Depends on context |

Note: Layer 1 is the prerequisite for Layer 2. If the core action success rate is very low, retention data is meaningless because users never had the chance to experience the product's value.

4.4 Aha Moment Design

Aha Moment Definition:
When a user completes [specific behavior], they have experienced this product's core value.
Goal: Get users to this moment within [X minutes / X steps] of entering the product.

Aha Moment Reach Rate: [target %]
Current Barriers: [What prevents users from reaching the Aha Moment faster?]
Improvement Plan: [How to remove the barriers?]

Examples:

Product	Aha Moment	Time Target
Slack	Team sends its 2,000th message	First two weeks
Dropbox	First file synced to a second device	Within 10 minutes of first use
Zoom	First one-click join with smooth video	First use

📝 Aha Moment Quality Checklist

• ✅ Is it a specific, trackable behavior? (Not "feels like the product is useful")
• ✅ Is it directly tied to the JTBD's functional job?
• ✅ Is the time target reasonable? (B2C should be within first use; B2B may be within the trial period)
• ✅ Can onboarding be designed to help users reach it faster?