Growth Marketer

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

Clarify First

Before designing experiments or a growth plan, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• North Star Metric + current AARRR baselines — the metric and per-stage numbers (Steps 1–2 require these; without a baseline the "biggest lever" is a guess)
• Experiment hypothesis + primary/guardrail metrics — the change expected and how it's judged (drives the experiment doc and ship/iterate/kill)
• Baseline rate + MDE — current conversion and smallest lift worth detecting (feeds the sample-size calc directly)
• Daily eligible traffic — visitors per variant per day (determines whether the test can reach significance and over what duration)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Workflow

1. 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.
2. 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.
3. Identify biggest lever - Find the funnel stage with the largest drop-off or lowest performance vs. benchmark. This becomes the focus area.
4. Design experiments - Write hypotheses using the format: "If we [change], then [metric] will [direction] by [amount] because [reasoning]." Prioritize using ICE scoring.
5. Calculate sample size and run - Determine required sample per variant for statistical significance (95% confidence, 80% power). Launch the experiment.
6. Analyze results - Evaluate lift, p-value, and guardrail metrics. Decision: Ship, Iterate, or Kill.
7. 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

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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

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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

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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.

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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