Growth Marketer

Expert-level growth marketing for scalable user acquisition.

Core Competencies Growth experimentation Funnel optimization Acquisition channels Retention strategies Viral mechanics Data analytics A/B testing Growth modeling Growth Framework AARRR Funnel (Pirate Metrics) 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

North Star Metric 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]

Experimentation Experiment Framework

Experiment: [Name]

Hypothesis

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

Metrics

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

Design

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

Variants

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

Results

|---------|-------|------------|------|--------------|

| Control | X | Y% | - | - |

| Treatment | X | Y% | +Z% | 95% |

Decision

[Ship / Iterate / Kill]

Learnings

[What we learned]

Statistical Significance

Sample size calculator

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)

Example: 5% baseline, 10% MDE

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

Experiment Prioritization (ICE) Experiment Impact Confidence Ease ICE Score [Exp 1] 8 7 9 24 [Exp 2] 6 8 7 21 [Exp 3] 9 5 6 20 Acquisition Channels Channel Analysis 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 Channel Optimization

Channel: [Channel Name]

Current Performance

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

Optimization Levers

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

Experiments

90-Day Target

CAC: $[X] → $[Y]
Volume: [X] → [Y]

Retention Strategies Retention Curves 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%

Retention Tactics

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 Cohort Analysis 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.

Viral Growth Viral Coefficient (K-Factor) 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

Viral Loop Optimization USER → MOTIVATE → INVITE → CONVERT → NEW USER

MOTIVATE: Why should users invite?
Intrinsic: Product is better with friends
Extrinsic: Rewards, credits, features
INVITE: Make it easy
Pre-written messages
Multiple channels
Low friction
CONVERT: Optimize landing
Social proof
Clear value prop
Easy sign-up

Growth Modeling Growth Equation 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

Forecast Model 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

Result: 31,384 users at month 12

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

Experiment analyzer

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

Funnel analyzer

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

Cohort generator

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

Growth model

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

growth-marketer

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