growth-marketer

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Growth Marketer

增长营销专家

Expert-level growth marketing for scalable user acquisition.

专业级增长营销方案，助力可规模化用户获取。

Core Competencies

核心能力

Growth experimentation
Funnel optimization
Acquisition channels
Retention strategies
Viral mechanics
Data analytics
A/B testing
Growth modeling

增长实验
漏斗优化
获客渠道
留存策略
病毒传播机制
数据分析
A/B测试
增长建模

Growth Framework

增长框架

AARRR Funnel (Pirate Metrics)

AARRR漏斗模型（海盗指标）

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 > 3:1

ACQUISITION → 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>3:1

North Star Metric

北极星指标

markdown

NORTH 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]

markdown

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]

Experimentation

实验体系

Experiment Framework

实验框架

markdown

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Experiment: [Name]

实验：[名称]

Hypothesis

假设

If we [change], then [metric] will [increase/decrease] by [amount] because [reasoning].

如果我们[做出改动]，那么[指标]将[提升/下降][幅度]，因为[推理依据]。

Metrics

指标

Primary: [Metric]
Secondary: [Metrics]
Guardrails: [Metrics we don't want to hurt]

核心指标：[指标]
次要指标：[指标列表]
防护指标：[我们不希望受损的指标]

Design

设计

Type: A/B / Multivariate / Holdout
Sample: [Size calculation]
Duration: [Days/Weeks]
Segments: [User segments]

类型：A/B测试 / 多变量测试 / 对照组测试
样本量：[计算结果]
时长：[天/周]
用户细分：[用户群体]

Variants

变体

Control: [Description]
Treatment A: [Description]
Treatment B: [Description] (if applicable)

对照组：[描述]
实验组A：[描述]
实验组B：[描述]（如适用）

Results

结果

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%

Decision

决策

[Ship / Iterate / Kill]

[上线 / 迭代 / 终止]

Learnings

经验总结

[What we learned]

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[我们学到的内容]

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Statistical Significance

统计显著性

python

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python

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Sample size calculator

样本量计算器

def sample_size(baseline_rate, mde, alpha=0.05, power=0.8): """ baseline_rate: Current conversion rate mde: Minimum detectable effect (e.g., 0.1 for 10%) alpha: Significance level (0.05 = 95% confidence) power: Statistical power (0.8 = 80%) """ from scipy import stats

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)

def sample_size(baseline_rate, mde, alpha=0.05, power=0.8): """ baseline_rate: 当前转化率 mde: 最小可检测效果（如0.1代表10%） alpha: 显著性水平（0.05 = 95%置信度） power: 统计功效（0.8 = 80%） """ from scipy import stats

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)

Example: 5% baseline, 10% MDE

示例：5%基准转化率，10%最小可检测效果

sample_size(0.05, 0.1) = ~31,000 per variant

sample_size(0.05, 0.1) = ~31,000 每组样本量

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Experiment Prioritization (ICE)

实验优先级排序（ICE模型）

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

Acquisition Channels

获客渠道

Channel Analysis

渠道分析

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

Channel Optimization

渠道优化

markdown

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markdown

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Channel: [Channel Name]

渠道：[渠道名称]

Current Performance

当前表现

Spend: $[X]/month
Users: [X]
CAC: $[X]
Quality Score: [X]/10

投入：$[X]/月
用户数：[X]
CAC：$[X]
质量得分：[X]/10

Optimization Levers

优化方向

[Lever 1]: [Current → Target]
[Lever 2]: [Current → Target]
[Lever 3]: [Current → Target]

[方向1]：[当前值 → 目标值]
[方向2]：[当前值 → 目标值]
[方向3]：[当前值 → 目标值]

Experiments

实验计划

[实验1]：[假设]
[实验2]：[假设]

90-Day Target

90天目标

CAC: $[X] → $[Y]
Volume: [X] → [Y]

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CAC：$[X] → $[Y]
量级：[X] → [Y]

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Retention Strategies

留存策略

Retention Curves

留存曲线

DAY 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%

Retention Tactics

留存策略

Onboarding:

Progressive disclosure
Personalized setup
Quick wins
Social proof

Engagement:

Push notifications
Email sequences
In-app messages
Feature education

Re-engagement:

Win-back campaigns
New feature announcements
Special offers
Community events

新用户引导：

渐进式信息披露
个性化设置
快速获得成就感
社交证明

用户活跃：

推送通知
邮件序列
应用内消息
功能使用教学

召回用户：

赢回活动
新功能公告
专属优惠
社区活动

Cohort Analysis

同期群分析

         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。

Viral Growth

病毒式增长

Viral Coefficient (K-Factor)

病毒系数（K系数）

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 viral

K = 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

Viral Loop Optimization

病毒循环优化

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

Growth Modeling

增长建模

Growth Equation

增长公式

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

Forecast Model

预测模型

python

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 users

python

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 users

Example: 10,000 users, 10% monthly growth, 12 months

示例：当前10000用户，月度增长率10%，预测12个月后

Result: 31,384 users at month 12

结果：第12个月用户数约为31384

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Reference Materials

参考资料

```
references/experimentation.md
```
- A/B testing guide
```
references/acquisition.md
```
- Channel playbooks
```
references/retention.md
```
- Retention strategies
```
references/viral.md
```
- Viral mechanics

```
references/experimentation.md
```
- A/B测试指南
```
references/acquisition.md
```
- 渠道执行手册
```
references/retention.md
```
- 留存策略指南
```
references/viral.md
```
- 病毒传播机制

Scripts

脚本工具

bash

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bash

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Experiment analyzer

实验分析器

python scripts/experiment_analyzer.py --experiment exp_001 --data results.csv

Funnel analyzer

漏斗分析器

python scripts/funnel_analyzer.py --events events.csv --output funnel.html

Cohort generator

同期群生成器

python scripts/cohort_generator.py --users users.csv --metric retention

Growth model

增长模型计算器

python scripts/growth_model.py --current 10000 --growth 0.1 --months 12

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python scripts/growth_model.py --current 10000 --growth 0.1 --months 12

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