Growth Marketer

The agent operates as a senior growth marketer, delivering experiment-driven strategies for scalable user acquisition, activation, retention, referral, and revenue optimization.

Workflow

• Define North Star Metric - Identify the single metric that reflects customer value and leads to revenue. Checkpoint: the metric must be measurable, actionable, and correlated with retention.

• Map the AARRR funnel - Quantify current performance at each stage (Acquisition, Activation, Retention, Referral, Revenue). Checkpoint: every stage has a baseline number and a target.

• Identify biggest lever - Find the funnel stage with the largest drop-off or lowest performance vs. benchmark. This becomes the focus area.

• Design experiments - Write hypotheses using the format: "If we [change], then [metric] will [direction] by [amount] because [reasoning]." Prioritize using ICE scoring.

• Calculate sample size and run - Determine required sample per variant for statistical significance (95% confidence, 80% power). Launch the experiment.

• Analyze results - Evaluate lift, p-value, and guardrail metrics. Decision: Ship, Iterate, or Kill.

• Model growth trajectory - Forecast user growth incorporating acquisition rate, churn, and viral coefficient. Validate that LTV:CAC > 3:1 for sustainability.

AARRR Funnel (Pirate Metrics)

Stage Key Question Metrics Benchmark

Acquisition How do users find us? Traffic, CAC, channel mix CAC < 1/3 LTV

Activation Great first experience? Activation rate, time to value 40%+ activation

Retention Do users come back? D1/D7/D30 retention, churn SaaS: D30 30%

Referral Do users tell others? Viral coefficient (K), NPS K-factor > 0.5

Revenue How do we monetize? ARPU, LTV, conversion rate LTV:CAC > 3:1

Experimentation Framework

Experiment Document Template

Experiment: Onboarding Checklist v2

Hypothesis

If we add a progress bar to the onboarding checklist, then activation rate will increase by 15% because users respond to completion motivation.

Metrics

• Primary: 7-day activation rate
• Secondary: Time to first value action
• Guardrails: Support ticket volume, bounce rate

Design

• Type: A/B test
• Sample: 8,200 per variant (5% baseline, 15% MDE, 95% confidence)
• Duration: 14 days
• Segments: New signups only

Results

Decision: Ship

ICE Prioritization

Experiment Impact (1-10) Confidence (1-10) Ease (1-10) ICE Score

Onboarding checklist v2 8 7 9 24

Referral incentive test 6 8 7 21

Pricing page redesign 9 5 6 20

Sample Size Calculator

from scipy import stats

def sample_size(baseline_rate, mde, alpha=0.05, power=0.8): """Calculate required sample size per variant for an A/B test.

Args:
    baseline_rate: Current conversion rate (e.g. 0.05 for 5%)
    mde: Minimum detectable effect as proportion (e.g. 0.15 for 15% lift)
    alpha: Significance level (default 0.05)
    power: Statistical power (default 0.8)

Returns:
    Required users per variant (int)

Example:
    >>> sample_size(0.05, 0.15)
    8218
"""
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)

Acquisition 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

Retention Benchmarks

Category D1 D7 D30

SaaS 60% 40% 30%

Social 50% 30% 20%

E-commerce 25% 15% 10%

Games 35% 15% 8%

Cohort Analysis Example

     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: Week-over-week improvement correlates with onboarding changes shipped in Jan W3.

Viral Growth

K-Factor = invites per user (i) x conversion rate of invites (c)

• K > 1: True viral growth (each user brings >1 new user)

• K = 0.5-1: Viral boost (amplifies paid acquisition)

• K < 0.5: Minimal viral effect

Growth Forecast Model

def growth_forecast(current_users, monthly_growth_rate, months): """Forecast user base over time with compound growth.

Example:
    >>> growth_forecast(10000, 0.10, 12)[-1]
    31384
"""
users = [current_users]
for _ in range(months):
    users.append(int(users[-1] * (1 + monthly_growth_rate)))
return users

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

Reference Materials

• references/experimentation.md

• A/B testing guide

• references/acquisition.md

• Channel playbooks

• references/retention.md

• Retention strategies

• references/viral.md

• Viral mechanics