| name | growth-marketer |
| description | Expert growth marketing covering experimentation, funnel optimization, acquisition channels, retention strategies, and viral growth. Use when designing A/B experiments, optimizing AARRR funnel stages, calculating viral coefficients, building growth models, or prioritizing acquisition channels by CAC and LTV.
|
| license | MIT + Commons Clause |
| metadata | {"version":"1.0.0","author":"borghei","category":"marketing-growth","updated":"2026-03-31T00:00:00.000Z","tags":["growth","experimentation","acquisition","retention","viral"]} |
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
| Variant | Users | Activation | Lift | p-value |
|-----------|--------|------------|-------|---------|
| Control | 8,350 | 5.1% | - | - |
| Treatment | 8,280 | 6.2% | +21% | 0.003 |
## 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
python scripts/experiment_analyzer.py --experiment exp_001 --data results.csv
python scripts/funnel_analyzer.py --events events.csv --output funnel.html
python scripts/cohort_generator.py --users users.csv --metric retention
python scripts/growth_model.py --current 10000 --growth 0.1 --months 12
Reference Materials
references/experimentation.md - A/B testing guidereferences/acquisition.md - Channel playbooksreferences/retention.md - Retention strategiesreferences/viral.md - Viral mechanics
Troubleshooting
| Symptom | Likely Cause | Resolution |
|---|
| K-factor below 0.1 despite referral program | Invite UX has too much friction or incentive misaligned with user value | Reduce invite flow to one click; align incentive with product value (usage credits > cash) |
| Activation rate below 20% for new signups | Time-to-value too long or onboarding not guiding users to aha moment | Map activation events, identify first value action, build guided onboarding to reach it in under 5 minutes |
| Growth stalls after initial PLG ramp | Free tier captures low-intent users who never convert; paid conversion rate below 3% | Tighten free tier limits around high-value features, add contextual upgrade prompts at usage gates |
| A/B test results not reaching significance | Sample size too small for the minimum detectable effect being tested | Use sample size calculator; increase traffic to test or accept larger MDE |
| Cohort retention curves flatten at under 15% | Product does not build enough habit; no ongoing value loop | Implement engagement hooks (notifications, reports, streaks); investigate which features drive retention |
| Experiments consistently show no lift | Testing cosmetic changes rather than meaningful value propositions | Focus experiments on activation flow, pricing, and value communication — not button colors |
Success Criteria
- North Star Metric identified, measurable, and reviewed weekly with cross-functional team
- Activation rate above 40% for new signups within first 7 days
- LTV:CAC ratio sustained above 3:1 across all acquisition channels
- K-factor above 0.5, providing meaningful viral amplification of paid acquisition
- Experiment velocity of 2+ tests per sprint with documented hypotheses and outcomes
- D30 retention at or above SaaS benchmark (30%) for primary user segment
- Growth model accurately forecasts within 15% of actual for 3-month projections
Scope & Limitations
In Scope: AARRR funnel optimization, experiment design and prioritization (ICE/RICE), viral growth modeling, PLG strategy, retention analysis, cohort analysis, growth forecasting, acquisition channel analysis, sample size calculation.
Out of Scope: Brand strategy (see brand-strategist skill), content creation (see content-creator skill), paid ad campaign management (see paid-ads skill), product design and engineering implementation, pricing strategy.
Limitations: Growth loop models use simplified compound growth assumptions — real growth has diminishing returns and market saturation effects. Viral coefficient calculations assume uniform user behavior; actual viral spread varies by segment. Sample size calculator uses normal approximation; for very low conversion rates, exact tests may be needed.
Scripts
| Script | Purpose | Usage |
|---|
scripts/growth_loop_modeler.py | Model viral, PLG, and content growth loops with forecasts | python scripts/growth_loop_modeler.py --type viral --users 1000 --k-factor 0.6 --months 12 |
scripts/viral_coefficient_calculator.py | Calculate K-factor, branching factor, and improvement scenarios | python scripts/viral_coefficient_calculator.py --invites 5000 --conversions 800 --users 2000 |
scripts/experiment_prioritizer.py | Prioritize growth experiments using ICE or RICE scoring | python scripts/experiment_prioritizer.py experiments.json --framework ice --demo |
