prd-v03-outcome-definition
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npx mdskill add mattgierhart/PRD-driven-context-engineering/prd-v03-outcome-definitionPosition in HORIZON workflow: v0.2 Product Type Classification → **v0.3 Outcome Definition** → v0.3 Pricing Model Selection
SKILL.md
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--- name: prd-v03-outcome-definition description: Define measurable success metrics (KPIs) tied to product type during PRD v0.3 Commercial Model. Triggers on requests to define success metrics, set KPI targets, determine what to measure, establish go/no-go thresholds, or when user asks "how do we measure success?", "what metrics matter?", "what's our target?", "how do we know if this works?", "define KPIs", "success criteria". Consumes Product Type Classification (BR-) from v0.2. Outputs KPI- entries with thresholds, evidence sources, and downstream gate linkages. context: fork allowed-tools: - Read - Write - Edit - Glob - Grep - WebSearch - WebFetch --- # Outcome Definition Position in HORIZON workflow: v0.2 Product Type Classification → **v0.3 Outcome Definition** → v0.3 Pricing Model Selection ## Consumes This skill requires prior work from v0.2: - **BR-\* product type entry** (from Product Type Classification) — Classification determines which metrics are relevant - **CFD-\* entries** (from Problem Framing and Competitive Landscape) — Customer evidence about desired outcomes - **Market benchmarks and competitor metrics** — Reference data for Tier 1/2 targets This skill assumes v0.2 classification is complete. ## Produces This skill creates/updates: - **KPI-\* entries** (outcome definitions) — Measurable success metrics tied to product type - **BR-\* outcome rules** (optional) — Constraints derived from KPI thresholds (e.g., "Launch blocked if LTV:CAC < 3:1") - **Success criteria artifact** — Dashboard of leading + lagging indicators that define product-market fit All KPI entries should include: - `confidence: 2-3/5` (based on benchmark evidence, not just assumptions) - Evidence source (competitor benchmarks, CFD validation, industry reports) - Forward target: "Would move to 4/5 if we observe real customer data" Example KPI entry with confidence: ```markdown KPI-001: Time to First Revenue Type: Tier 1 (Revenue) Category: Lagging Definition: Days from market signal identification to first paying customer Target: ≤14 days Confidence: 2/5 (source: GearHeart-methodology + 0-customer-validation) Evidence: BR-001 (GearHeart standard); No pre-customer validation yet Next Target: "Would move to 4/5 if actual customer reaches paying status in ≤14 days" Downstream Gate: v0.5 Red Team — if not hit by Day 21, evaluate pivot --- KPI-002: Conversion Rate (Trial → Paid) Type: Tier 2 (Leading Indicator) Category: Leading Definition: (Paid customers / Trial signups) × 100, measured over 60-day trial period Target: ≥15% (benchmark: SaaS median 10-15%) Confidence: 3/5 (source: SaaS-benchmarks + 1-SMB-validation-conversation) Evidence: CFD-042 (competitive landscape shows SMB conversion patterns) Next Target: "Would move to 4/5 if we see actual cohort conversion in our product" Downstream Gate: v0.7 Build Execution — EPIC complete when KPI-002 validated ``` ## Metric Quality Hierarchy Not all metrics are equal. Use this tier system: | Tier | Metric Types | Why It Matters | |------|--------------|----------------| | **Tier 1** | Revenue (MRR, first dollar, ACV), Churn (logo, NRR), LTV:CAC | Revenue validates market fit. "First dollar IS the proof." | | **Tier 2** | Conversion rates (trial→paid, lead→customer), Time to Value, Activation | Leading indicators that predict Tier 1 outcomes | | **Tier 3** | Engagement (DAU, sessions), Feature adoption, NPS | "Nice to know" — only track if tied to Tier 1/2 | **Rule**: Every product needs at least one Tier 1 metric. Tier 3 metrics without Tier 1/2 correlation are vanity metrics. ## Product Type × Metric Selection Metrics must align with product type from v0.2 classification: | Product Type | Primary Metrics | Anti-Metrics (Avoid) | |--------------|-----------------|----------------------| | **Clone** | Feature parity score, Price delta vs. leader, TTFV vs. leader | Generic engagement (doesn't prove you beat leader) | | **Undercut** | Price per [unit] vs. leader, Niche conversion rate, CAC in target segment | Broad market share (you're niche by design) | | **Unbundle** | Category NPS vs. platform, Vertical retention, Feature depth usage | Platform-level metrics (irrelevant to your slice) | | **Slice** | Marketplace ranking, Install→activate rate, Platform retention lift | TAM metrics (platform owns the market) | | **Wrapper** | Time saved per workflow, API reliability, Integration adoption | Standalone usage (value is in connection) | | **Innovation** | Education→activation conversion, Behavioral change rate, Reference customers | User counts without activation (people try, don't convert) | ## Leading vs. Lagging Framework Every product needs BOTH: **Leading Indicators** (actionable now, predict outcomes): - Sequences sent, open rates, trial starts - Time to first value, activation rate - Feature adoption in first 7 days **Lagging Indicators** (confirm strategy worked): - MRR, churn rate, LTV:CAC - Net Revenue Retention (NRR) - Customer count, logo churn **Pattern**: Track leading weekly, lagging monthly. If leading indicators fail, you can pivot before lagging indicators confirm disaster. ## Target-Setting Rules Targets must be evidence-based, never arbitrary: **Good targets** (use these approaches): - Competitor benchmark × safety margin: "SMB churn benchmark 3-5% → use 5%" - Revenue gates: "First dollar by Day 14" (Signal → $1: 14 days) - Ratio thresholds: "LTV:CAC ≥ 3:1" - Time bounds: "TTFV < 5 minutes for self-serve" **Bad targets** (anti-patterns): - Round numbers without evidence: "10% improvement" - Engagement without revenue tie: "1000 DAU" - Aspirational without baseline: "Best in class retention" ## Output Template Create KPI- entries in this format: ``` KPI-XXX: [Metric Name] Type: [Tier 1 | Tier 2 | Tier 3] Category: [Leading | Lagging] Definition: [Exact calculation formula] Target: [Specific threshold with evidence source] Evidence: [CFD-XXX or benchmark source] Downstream Gate: [Which decision uses this — e.g., "v0.5 Red Team kill criteria"] Measurement: [How/when measured — e.g., "Weekly via Mixpanel"] ``` **Example KPI- entry:** ``` KPI-001: Time to First Revenue Type: Tier 1 Category: Lagging Definition: Days from market signal identification to first paying customer Target: ≤14 days (GearHeart standard: Signal → $1: 14 days) Evidence: BR-001 (GearHeart methodology) Downstream Gate: v0.5 Red Team — if not hit by Day 21, evaluate pivot Measurement: Manual tracking in PRD changelog ``` ## Anti-Patterns to Avoid 1. **Vanity metrics as primary**: "50K users" means nothing if only 500 pay 2. **Traffic without quality**: High volume + low engagement = quality problem 3. **Arbitrary targets**: "10% improvement" without baseline or benchmark 4. **All lagging, no leading**: Can't course-correct if you only see outcomes monthly 5. **Ignoring product type**: Clone metrics ≠ Innovation metrics 6. **Unmeasurable outcomes**: "Better experience" — how do you know? ## Downstream Connections KPI- entries feed into: | Consumer | What It Uses | Example | |----------|--------------|---------| | **v0.5 Red Team** | Kill thresholds | "If KPI-001 not hit by Day 21, pivot" | | **v0.7 Build Execution** | EPIC acceptance criteria | "EPIC complete when KPI-002 validated" | | **v0.9 GTM** | Launch dashboard | Track KPI-001, KPI-003 post-launch | | **BR- Business Rules** | Derived constraints | "BR-XXX: No launch if LTV:CAC <3:1" | ## Detailed References - **Good/bad examples**: See `references/examples.md` - **Benchmark sources**: See `references/benchmarks.md` - **KPI template worksheet**: See `assets/kpi.md`