Nw Outcome Kpi Framework

Formula: [Who] [Does what] [By how much]

Apply as litmus test: if a KPI cannot answer all five components, it measures an output (feature delivery), not an outcome (behavior change).

Good vs Bad KPIs

Leading vs Lagging Indicators

From Gothelf/Seiden: business results are lagging -- teams cannot directly influence them. Target leading indicators instead.

Outcome Mapping Chain

Map every KPI through this chain to ensure traceability:

Business KPI (Lagging/Impact)
    Example: "Increase quarterly revenue by 15%"
        |
        v
    Customer Behavior (Leading/Outcome)
        +-- Users complete purchases from recommendations (+25%)
        +-- Users return within 7 days (+20%)
        |
        v
    Secondary Behavior (Leading/Secondary)
        +-- Users browse recommendation pages (+30%)
        +-- Users enable push notifications (+15%)

Each layer decomposes into more granular behavioral metrics. Teams target the highest-leverage behavior.

Actionable vs Vanity Metrics

From Maurya (Running Lean): actionable metrics "tie specific and repeatable actions to observed results."

The OMTM (One Metric That Matters)

Pick ONE metric per product stage. Optimizing one metric reveals the next.

Good metric characteristics: rate or ratio (not absolute number) | comparable across time | simple enough to remember | predictive | behavior-changing.

Customer Factory (AARRR) Constraint Mapping

From Maurya: model the business as a production line. Identify the bottleneck, then focus KPIs there.

Activation is causal -- it drives retention, revenue, and referral. Prioritize activation KPIs when uncertain.

OKR Integration

From Doerr (Measure What Matters): connect KPIs to strategic objectives.

Writing Key Results

Every Key Result uses the outcome formula. Quality criteria:

1. Measurable: "It's not a Key Result unless it has a number" (Marissa Mayer)
2. Outcome-focused: "Increase email subscribers by 20%" not "Launch newsletter"
3. Time-bound: deadline (typically end of quarter)
4. Verifiable: no ambiguity about whether met
5. Aggressive yet realistic: stretch without demoralizing

Committed vs Aspirational

Sweet spot: blended aggregate of 0.6-0.7. Consistently hitting 1.0 = not ambitious enough.

OKR Anti-Patterns

Mapping: Objective to User Stories

Objective (qualitative, inspirational, timeboxed)
    |
    Key Results (2-4 per Objective, [Who][Does what][By how much])
    |
    Epics (weeks of work, aligned to Key Results)
    |
    User Stories (days of work, with measurable acceptance criteria)

Every story traces back to a Key Result. Orphan stories (no KR link) are potential waste.

KPI Template

Use this exact structure in outcome-kpis.md:

## Feature: {feature-name}

### Objective
{What success looks like in one sentence -- qualitative, inspirational, timeboxed}

### Outcome KPIs

| # | Who | Does What | By How Much | Baseline | Measured By | Type |
|---|-----|-----------|-------------|----------|-------------|------|
| 1 | {segment} | {behavior} | {target} | {current} | {method} | Leading/Lagging |

### Metric Hierarchy
- **North Star**: {the ONE metric that matters most for this feature}
- **Leading Indicators**: {behaviors that predict the north star}
- **Guardrail Metrics**: {metrics that must NOT degrade}

### Measurement Plan
| KPI | Data Source | Collection Method | Frequency | Owner |
|-----|------------|-------------------|-----------|-------|

### Hypothesis
We believe that {proposed solution} for {user segment} will achieve {key result}.
We will know this is true when {who} {does what} {by how much}.

KPI Granularity

• Per Epic: Define 2-3 north-star KPIs that all contributing stories aim to move
• Per Story: Add story-level success criteria tied to the epic-level KPIs
• Guardrails: Define at epic level, apply consistently across all stories
• Rule of thumb: If the feature has 1-3 stories, one KPI table suffices. If 4+, group by epic.

Smell Tests

Before finalizing KPIs, verify each one passes:

Handoff to DEVOPS

The platform-architect needs these from outcome-kpis.md to plan instrumentation:

1. Data collection requirements: what events/behaviors to instrument, what data points to capture
2. Dashboard/monitoring needs: which metrics need real-time dashboards vs. weekly reports
3. Alerting thresholds: guardrail metric boundaries that trigger alerts when breached
4. Baseline measurement: any metrics needing baseline collection before feature release

References

For deeper reading on source frameworks:

• Running Lean (Maurya): docs/research/running-lean-research.md
• Measure What Matters (Doerr): docs/research/measure-what-matters-research.md
• Who Does What By How Much (Gothelf/Seiden): docs/research/who-does-what-research.md