/nw-outcome-kpi-framework

name: nw-outcome-kpi-framework
description: Outcome KPI definition methodology - synthesizes Who Does What By How Much (Gothelf/Seiden), Running Lean (Maurya), and Measure What Matters (Doerr) into a practical framework for measurable outcome KPIs
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Outcome KPI Framework

"Doing stuff isn't the point. Achieving stuff is." -- Jeff Gothelf

Defines measurable outcome KPIs for user stories and features. Synthesizes three frameworks: customer-centric OKRs, lean metrics, and OKR methodology.

**Output scope**: supply the actor and observable KPI result, supplied targets, baselines and measurement choices. Distinguish confirmed facts, proposals, assumptions and open questions; name an owner for open decisions. Keep unknown baselines or targets open. The template below shapes semantic facts; use it as a file layout only when authorized to author documents.

The Outcome KPI Formula

Primary template from Gothelf/Seiden. Every KPI answers five questions:

| Component | Question | Example | |-----------|----------|---------| | **Who** | Which user segment? | Returning customers with 2+ orders | | **Does What** | What observable behavior changes? | Complete checkout without contacting support | | **By How Much** | What is the measurable target? | 40% reduction in support tickets | | **Measured By** | How do we collect the data? | Support ticket system + checkout analytics | | **Timeframe** | When do we measure? | 30 days post-release, then weekly |

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

| Bad (Output) | Good (Outcome) | |-------------|----------------| | Launch mobile app v2 | Mobile users complete purchases 40% more often | | Build recommendation engine | Users purchase from recommendations, increasing from 10% to 25% | | Deploy onboarding redesign | New users complete onboarding within 24 hours 30% more often | | Ship CSV export | Analysts resolve data questions without engineering support 60% of the time |

Leading vs Lagging Indicators

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

| Type | Definition | Examples | Actionable? | |------|-----------|----------|-------------| | **Lagging** (Impact) | Business results already happened | Revenue, NPS, market share, churn rate | No -- too slow, too many variables | | **Leading** (Outcome) | Behavior changes predicting business results | Purchase completion rate, feature adoption, retention | Yes -- teams can run experiments | | **Leading** (Secondary) | Behaviors predicting primary leading indicators | Page visits, trial starts, onboarding steps completed | Yes -- most granular, fastest signal |

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."

| Dimension | Vanity | Actionable | |-----------|--------|------------| | Measures | Business size (totals) | Individual behavior (rates) | | Data type | Gross aggregates | Ratios and unit economics | | Cause/effect | No insight into why | Directly signal product-market fit | | Examples | Total users, page views, downloads | Activation rate, retention cohort, churn rate | | Decision value | Cannot inform action | Drives specific experiments |

The OMTM (One Metric That Matters)

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

| Stage | Focus | Example OMTM | |-------|-------|--------------| | Empathy | Problem validation | Interview pain intensity (qualitative) | | Stickiness | Retention | Churn rate, DAU/MAU ratio | | Virality | Organic growth | Viral coefficient, referral rate | | Revenue | Monetization | Customer Lifetime Value, MRR | | Scale | Growth efficiency | CAC/LTV ratio, payback period |

**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.

| Stage | Key Question | Example Metric | |-------|-------------|----------------| | **Acquisition** | Are we reaching the right people? | Visitor-to-signup conversion rate | | **Activation** | Do users get the "aha moment"? | % completing core action in first session | | **Retention** | Do users come back? | Week-1 return rate, DAU/MAU | | **Revenue** | Do users pay? | Trial-to-paid conversion rate | | **Referral** | Do users tell others? | Referral rate, viral coefficient |

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