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ACQUISITION → ACTIVATION → RETENTION → REFERRAL → REVENUE
Acquisition: How do users find us?
├── Channels: SEO, Paid, Social, Content
├── Metrics: Traffic, CAC, Channel mix
└── Goal: Efficient user acquisition
Activation: Do users have a great first experience?
├── Triggers: Aha moment, value realization
├── Metrics: Activation rate, Time to value
└── Goal: 40%+ activation rate
Retention: Do users come back?
├── Drivers: Habit formation, value delivery
├── Metrics: D1/D7/D30 retention, Churn
└── Goal: Strong retention curves
Referral: Do users tell others?
├── Mechanisms: Invite systems, sharing
├── Metrics: Viral coefficient, NPS
└── Goal: K-factor > 0.5
Revenue: How do we make money?
├── Models: Subscription, Usage, Freemium
├── Metrics: ARPU, LTV, Conversion rate
└── Goal: LTV:CAC > 3:1ACQUISITION → ACTIVATION → RETENTION → REFERRAL → REVENUE
获客：用户如何找到我们？
├── 渠道：SEO、付费投放、社交媒体、内容营销
├── 指标：流量、CAC、渠道组合
└── 目标：高效获取用户
激活：用户是否拥有良好的首次体验？
├── 触发点：惊喜时刻、价值感知
├── 指标：激活率、价值实现时长
└── 目标：激活率达40%以上
留存：用户是否会再次回访？
├── 驱动因素：习惯养成、价值交付
├── 指标：D1/D7/D30留存率、流失率
└── 目标：构建稳健的留存曲线
推荐：用户是否会推荐他人？
├── 机制：邀请系统、分享功能
├── 指标：病毒系数、NPS
└── 目标：K系数>0.5
变现：我们如何实现盈利？
├── 模式：订阅制、按使用量付费、免费增值
├── 指标：ARPU、LTV、转化率
└── 目标：LTV:CAC>3:1NORTH STAR METRIC: [Metric Name]
Definition: [How it's calculated]
Why it matters:
1. Reflects customer value
2. Leads to revenue
3. Measurable
4. Actionable
Supporting Metrics:
├── Input 1: [Metric]
├── Input 2: [Metric]
└── Input 3: [Metric]
Current: [Value]
Target: [Value] by [Date]NORTH STAR METRIC: [指标名称]
定义：[计算方式]
重要性：
1. 反映客户价值
2. 指向营收增长
3. 可量化
4. 可落地执行
支撑指标：
├── 输入指标1：[指标]
├── 输入指标2：[指标]
└── 输入指标3：[指标]
当前值：[数值]
目标值：[日期]前达到[数值]undefinedundefined| Variant | Users | Conversion | Lift | Significance |
|---|---|---|---|---|
| Control | X | Y% | - | - |
| Treatment | X | Y% | +Z% | 95% |
| 变体 | 用户数 | 转化率 | 提升幅度 | 显著性 |
|---|---|---|---|---|
| 对照组 | X | Y% | - | - |
| 实验组 | X | Y% | +Z% | 95% |
undefinedundefinedundefinedundefinedeffect_size = mde * baseline_rate
z_alpha = stats.norm.ppf(1 - alpha/2)
z_beta = stats.norm.ppf(power)
n = 2 * ((z_alpha + z_beta) ** 2) * baseline_rate * (1 - baseline_rate) / (effect_size ** 2)
return int(n)effect_size = mde * baseline_rate
z_alpha = stats.norm.ppf(1 - alpha/2)
z_beta = stats.norm.ppf(power)
n = 2 * ((z_alpha + z_beta) ** 2) * baseline_rate * (1 - baseline_rate) / (effect_size ** 2)
return int(n)undefinedundefined| Experiment | Impact | Confidence | Ease | ICE Score |
|---|---|---|---|---|
| [Exp 1] | 8 | 7 | 9 | 24 |
| [Exp 2] | 6 | 8 | 7 | 21 |
| [Exp 3] | 9 | 5 | 6 | 20 |
| 实验 | 影响力 | 置信度 | 易实现度 | ICE得分 |
|---|---|---|---|---|
| [实验1] | 8 | 7 | 9 | 24 |
| [实验2] | 6 | 8 | 7 | 21 |
| [实验3] | 9 | 5 | 6 | 20 |
| Channel | CAC | Volume | Quality | Scalability |
|---|---|---|---|---|
| Organic Search | $20 | High | High | Medium |
| Paid Search | $50 | Medium | High | High |
| Social Organic | $10 | Medium | Medium | Low |
| Social Paid | $40 | High | Medium | High |
| Content | $15 | Medium | High | Medium |
| Referral | $5 | Low | Very High | Medium |
| Partnerships | $30 | Medium | High | Medium |
| 渠道 | CAC | 量级 | 质量 | 可扩展性 |
|---|---|---|---|---|
| 自然搜索 | $20 | 高 | 高 | 中 |
| 付费搜索 | $50 | 中 | 高 | 高 |
| 自然社交 | $10 | 中 | 中 | 低 |
| 付费社交 | $40 | 高 | 中 | 高 |
| 内容营销 | $15 | 中 | 高 | 中 |
| 推荐 referral | $5 | 低 | 极高 | 中 |
| 合作伙伴 | $30 | 中 | 高 | 中 |
undefinedundefinedundefinedundefinedDAY 1 RETENTION: 40%
DAY 7 RETENTION: 25%
DAY 30 RETENTION: 15%
DAY 90 RETENTION: 10%
Benchmarks (by category):
├── Social: D1 50%, D7 30%, D30 20%
├── E-commerce: D1 25%, D7 15%, D30 10%
├── SaaS: D1 60%, D7 40%, D30 30%
└── Games: D1 35%, D7 15%, D30 8%首日留存率：40%
7日留存率：25%
30日留存率：15%
90日留存率：10%
行业基准（按品类）：
├── 社交类：首日50%，7日30%，30日20%
├── 电商类：首日25%，7日15%，30日10%
├── SaaS类：首日60%，7日40%，30日30%
└── 游戏类：首日35%，7日15%，30日8% Week 0 Week 1 Week 2 Week 3 Week 4
Jan W1 100% 45% 35% 28% 25%
Jan W2 100% 48% 38% 32% 28%
Jan W3 100% 52% 42% 35% 31%
Jan W4 100% 55% 45% 38% 34%
Insight: Improving week-over-week, likely due to
onboarding changes in Jan W3. 第0周 第1周 第2周 第3周 第4周
1月第1周 100% 45% 35% 28% 25%
1月第2周 100% 48% 38% 32% 28%
1月第3周 100% 52% 42% 35% 31%
1月第4周 100% 55% 45% 38% 34%
洞察：留存率逐周提升，可能得益于1月第3周优化的新用户引导流程。K = i × c
i = number of invites per user
c = conversion rate of invites
Example:
i = 5 invites per user
c = 20% convert
K = 5 × 0.20 = 1.0
K > 1: Viral growth
K = 0.5-1: Viral boost
K < 0.5: Minimal viralK = i × c
i = 每位用户发出的邀请数
c = 邀请转化率
示例：
i = 每位用户发出5个邀请
c = 20%的转化率
K = 5 × 0.20 = 1.0
K > 1：病毒式增长
K = 0.5-1：病毒式助推
K < 0.5：弱病毒效应USER → MOTIVATE → INVITE → CONVERT → NEW USER
1. MOTIVATE: Why should users invite?
- Intrinsic: Product is better with friends
- Extrinsic: Rewards, credits, features
2. INVITE: Make it easy
- Pre-written messages
- Multiple channels
- Low friction
3. CONVERT: Optimize landing
- Social proof
- Clear value prop
- Easy sign-up用户 → 激励 → 邀请 → 转化 → 新用户
1. 激励：用户为什么要邀请他人？
- 内在动机：和朋友一起使用产品体验更好
- 外在动机：奖励、积分、专属功能
2. 邀请：降低操作门槛
- 预设文案
- 多渠道分享
- 低摩擦流程
3. 转化：优化落地页
- 社交证明
- 清晰的价值主张
- 简易注册流程New Users = Acquisition + Referrals - Churn
Monthly Growth Rate = (New Users - Churned Users) / Total Users
Sustainable Growth requires:
- Positive unit economics (LTV > CAC)
- Manageable churn (<5% monthly for SaaS)
- Scalable acquisition channels新增用户数 = 获客数 + 推荐用户数 - 流失用户数
月度增长率 = (新增用户数 - 流失用户数) / 总用户数
可持续增长需满足：
- 正向单位经济效益（LTV > CAC）
- 可控的流失率（SaaS类月度流失率<5%）
- 可规模化的获客渠道def growth_forecast(current_users, monthly_growth_rate, months):
users = [current_users]
for m in range(months):
new_users = users[-1] * (1 + monthly_growth_rate)
users.append(new_users)
return usersdef growth_forecast(current_users, monthly_growth_rate, months):
users = [current_users]
for m in range(months):
new_users = users[-1] * (1 + monthly_growth_rate)
users.append(new_users)
return usersundefinedundefinedreferences/experimentation.mdreferences/acquisition.mdreferences/retention.mdreferences/viral.mdreferences/experimentation.mdreferences/acquisition.mdreferences/retention.mdreferences/viral.mdundefinedundefinedundefinedundefined