返回 Skill 列表
extension
分类: 营销与增长无需 API Key

prd-v03-outcome-definition

在PRD v0.3商业模型中定义与产品类型相关的可衡量的成功指标(KPI)。当请求定义成功指标、设置KPI目标、确定需要测量的内容、建立继续或停止的阈值,或者用户询问“我们如何衡量成功?”、“哪些指标重要?”、“我们的目标是什么?”、“我们怎么知道这是否有效?”、“定义KPI”、“成功标准”时触发。消费来自v0.2的产品类型分类(BR-)。输出带有阈值、证据来源和下游关卡链接的KPI条目。

person作者: jakexiaohubgithub

Outcome Definition

Position in HORIZON workflow: v0.2 Product Type Classification → v0.3 Outcome Definition → v0.3 Pricing Model Selection

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