Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
中文简介正在准备中。当前显示原文说明。
Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Apply Dave McClure's AARRR framework to measure and optimize growth through the five stages: Acquisition, Activation, Retention, Revenue, and Referral.
Based on Dave McClure's AARRR framework (500 Startups), providing:
| Claude Does | You Decide |
|---|---|
| Defines metrics per stage | Specific definitions for your product |
| Identifies bottlenecks | Experiment priorities |
| Suggests experiments | Resource allocation |
| Creates dashboards | Tool selection |
| Benchmarks performance | Acceptable thresholds |
AARRR Stages:
| Stage | Question | Focus |
|---|---|---|
| Acquisition | How do users find you? | Traffic, channels |
| Activation | Do they have a great first experience? | Onboarding, aha moment |
| Retention | Do they come back? | Engagement, habit |
| Revenue | Do they pay? | Conversion, monetization |
| Referral | Do they tell others? | Virality, NPS |
Metrics Framework:
| Stage | Primary Metric | Supporting Metrics |
|---|---|---|
| Acquisition | CAC, Unique visitors | Channel mix, CTR, CPM |
| Activation | Activation rate | Time to activate, drop-off points |
| Retention | D7/D30 retention | DAU/MAU, churn rate |
| Revenue | LTV, ARPU | Conversion rate, ACV |
| Referral | Viral coefficient | NPS, referral rate |
Funnel Template:
Acquisition → Activation → Retention → Revenue → Referral
100% → 30% → 15% → 5% → 1%
Calculate conversion rates between each stage.
Bottleneck Analysis:
| Drop-off | Diagnosis |
|---|---|
| Acquisition → Activation | Poor onboarding or wrong traffic |
| Activation → Retention | Not finding core value |
| Retention → Revenue | Pricing or value misalignment |
| Revenue → Referral | Not delighting paying users |
ICE Framework:
Input:
Build an AARRR dashboard for a B2B SaaS product.
Monthly visitors: 50,000
Signups: 3,000
Activated users: 900
Paying customers: 150
Referrals generating signups: 45
Output:
## AARRR Dashboard: B2B SaaS
### Funnel Overview
┌──────────────────────────────────────────────────────────────┐ │ AARRR FUNNEL │ ├──────────────────────────────────────────────────────────────┤ │ │ │ ACQUISITION ACTIVATION RETENTION REVENUE REF │ │ 50,000 3,000 900 150 45 │ │ 100% 6% 30% 17% 30% │ │ │ │ ████████████ ████████ ████ ██ █ │ │ │ │ Visitors → Signups → Activated → Paid → Referral │ │ (-94%) (-70%) (-83%) (-70%) │ │ ⚠️ ⚠️ ✅ ⚠️ │ │ │ └──────────────────────────────────────────────────────────────┘
---
### Stage-by-Stage Analysis
#### 1. ACQUISITION
**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Monthly visitors | 50,000 | - | Baseline |
| Visitor → Signup | 6% | 2-5% | ✅ Good |
| CAC | $45 | <$50 | ✅ Good |
| Signups | 3,000 | - | - |
**Channel Breakdown:**
| Channel | Visitors | Signups | Conv % | CAC |
|---------|----------|---------|--------|-----|
| Organic | 20,000 | 1,500 | 7.5% | $0 |
| Paid Search | 15,000 | 900 | 6% | $60 |
| Content | 10,000 | 450 | 4.5% | $20 |
| Social | 5,000 | 150 | 3% | $80 |
**Analysis:** Acquisition is healthy. Organic performing best.
Paid social has high CAC—consider reallocating.
---
#### 2. ACTIVATION ⚠️ BOTTLENECK
**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Signup → Activated | 30% | 40-60% | ⚠️ Below |
| Time to activate | 3.2 days | <1 day | ⚠️ Slow |
| Activation rate | 900/3,000 | - | - |
**Activation Definition:**
"Activated" = Created first project + invited 1 team member
**Drop-off Analysis:**
| Step | Users | Drop-off |
|------|-------|----------|
| Signup complete | 3,000 | - |
| Email verified | 2,400 | -20% |
| Created project | 1,500 | -38% |
| Invited team | 900 | -40% ⚠️ |
**Primary Bottleneck:** "Invite team member" step losing 40%
**Experiment Ideas:**
| Experiment | Hypothesis | ICE |
|------------|------------|-----|
| Skip team invite in onboarding | Removes friction, activate solo first | 8/8/9 = 8.3 |
| In-app invite prompt (day 2) | Right timing, after value seen | 7/7/8 = 7.3 |
| Email team invite reminder | Low effort, catches drop-offs | 5/6/9 = 6.7 |
---
#### 3. RETENTION
**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Week 1 retention | 65% | 60%+ | ✅ Good |
| Month 1 retention | 45% | 40%+ | ✅ Good |
| DAU/MAU ratio | 28% | 20%+ | ✅ Good |
| Churn rate | 5%/month | <5% | ✅ OK |
**Retention Curve:**
Day 1: 100% ████████████████████ Day 7: 65% █████████████ Day 14: 52% ██████████ Day 30: 45% █████████ Day 60: 38% ████████ Day 90: 32% ██████
**Analysis:** Retention is solid. Users who activate tend to stick.
This confirms activation is the primary bottleneck.
---
#### 4. REVENUE
**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Activated → Paid | 17% | 5-15% | ✅ Strong |
| Trial → Paid | 17% | 10-25% | ✅ Good |
| ARPU | $89/mo | - | Baseline |
| LTV | $1,068 | - | 12mo × ARPU |
| LTV:CAC | 24:1 | >3:1 | ✅ Excellent |
**Revenue Breakdown:**
| Plan | Customers | % | MRR |
|------|-----------|---|-----|
| Starter ($29) | 60 | 40% | $1,740 |
| Pro ($89) | 75 | 50% | $6,675 |
| Enterprise ($249) | 15 | 10% | $3,735 |
| **Total** | **150** | - | **$12,150** |
**Analysis:** Strong conversion and healthy LTV:CAC.
Not a bottleneck—maintain current approach.
---
#### 5. REFERRAL ⚠️ OPPORTUNITY
**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Referral rate | 30% | 20%+ | ✅ Good |
| Viral coefficient | 0.45 | >1 = viral | ⚠️ Below |
| NPS | +32 | >30 | ✅ Good |
| Referral signups | 45 | - | - |
**Calculation:**
- 150 paying customers
- 30% refer (45 referrals)
- Each referral generates 1 signup
- Viral coefficient = 45/150 × (6% activation) = 0.018
- Not organic virality, but healthy referral base
**Experiment Ideas:**
| Experiment | Hypothesis | ICE |
|------------|------------|-----|
| Referral incentive (2-way) | Motivation for both parties | 7/8/7 = 7.3 |
| In-app share prompts | Right moment capture | 6/7/8 = 7.0 |
| Case study program | Social proof + referral | 5/6/6 = 5.7 |
---
### Priority Recommendations
| Priority | Focus | Current | Target | Impact |
|----------|-------|---------|--------|--------|
| 1 | Activation | 30% | 45% | +450 activated/mo |
| 2 | Referral | 30% | 50% | +30 referral signups |
| 3 | Paid channel mix | 6% conv | 8% conv | -$10 CAC |
**90-Day Goal:**
If activation improves 30% → 45%:
- +450 more activated users
- At 17% conversion = +76 new customers
- At $89 ARPU = +$6,800 MRR
---
### Dashboard Template
┌────────────────────────────────────────────────────────────┐ │ AARRR MONTHLY DASHBOARD January 2026 │ ├────────────────────────────────────────────────────────────┤ │ │ │ ACQUISITION ACTIVATION RETENTION │ │ Visitors: 50K Activated: 900 D30: 45% │ │ Signups: 3K Rate: 30% ⚠️ Churn: 5% │ │ CAC: $45 Time: 3.2d DAU/MAU: 28% │ │ │ │ REVENUE REFERRAL OVERALL │ │ Customers: 150 Referrals: 45 MRR: $12.2K │ │ Conv: 17% Rate: 30% LTV:CAC: 24:1 │ │ ARPU: $89 NPS: +32 Health: 🟢 │ │ │ │ BOTTLENECK: Activation (30% vs 45% target) │ │ EXPERIMENT: Skip team invite onboarding │ │ │ └────────────────────────────────────────────────────────────┘
Input:
Diagnose this funnel:
- 100K visitors, 5K signups (5%)
- 4K activated (80%)
- 1K retained at D30 (25%)
- 200 paid (20%)
Output:
## Quick AARRR Diagnosis
### Funnel
| Stage | Users | Conv | Benchmark | Status |
|-------|-------|------|-----------|--------|
| Acquisition | 100K → 5K | 5% | 2-5% | ✅ Good |
| Activation | 5K → 4K | 80% | 40-60% | ✅ Excellent |
| Retention | 4K → 1K | 25% | 40%+ | ❌ Problem |
| Revenue | 1K → 200 | 20% | 5-15% | ✅ Strong |
### Bottleneck: RETENTION
**Problem:** Only 25% retained at D30 (should be 40%+)
**What this means:**
- Great at attracting and activating
- Users try it, find value initially
- But don't form a habit / come back
- Losing 3,000 activated users monthly
**Likely Causes:**
1. Single-use case (solved problem, left)
2. Not enough ongoing value
3. Poor re-engagement
4. Competitor switching
**Recommended Experiments:**
1. User interviews with churned users
2. Email re-engagement sequence
3. Weekly value summary email
4. Add recurring use case
**Impact if fixed:**
If retention → 40%: 1,600 retained → 320 paid
That's +120 customers/month (+60%)
Follow-up Prompts:
product-led-growth - PLG motionsgrowth-loops - Sustainable growthstartup-metrics - Investor metrics还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
中文简介正在准备中。当前显示原文说明。
Assess customer account health using product usage, support sentiment, payment status, and relationship signals
中文简介正在准备中。当前显示原文说明。
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
中文简介正在准备中。当前显示原文说明。
Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
中文简介正在准备中。当前显示原文说明。
Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
中文简介正在准备中。当前显示原文说明。
Créez des storyboards visuellement cohérents en utilisant la technique des 2x2 Grid Shots de PJ Ace, garantissant éclairage, personnages et décors uniformes entre les plans. Use when: **Après avoir finalisé un script vidéo** - Transformer le concept en visuels; **Besoin de cohérence visuelle** - Personnages et éclairage constants entre les plans; **Préparer des assets pour animation** - Frames prêtes pour Veo, Runway, Kling; **Présenter un storyboard client** - Visualisation avant production;...
中文简介正在准备中。当前显示原文说明。