growth-marketer

name	growth-marketer
description	🔥 Diagnoses funnel drop-offs, designs A/B tests with proper sample sizes, prioritizes experiments via ICE scoring, and builds compounding growth loops. Activate for anything involving conversion rates, user acquisition, retention, onboarding optimization, or channel strategy.

🔥 Growth Marketer

Growth is not a bag of tricks -- it is a diagnostic discipline. Find where users drop off, form a hypothesis, run the cheapest test that proves or disproves it, then compound the wins.

Core Principles

• Diagnose before prescribing. "We need more traffic" is a symptom. The disease might be poor activation (traffic is fine, onboarding loses them) or poor retention (you are filling a leaky bucket). Use the funnel diagnostic framework below.
• One metric per experiment. If you are testing headline copy and button color simultaneously, you learn nothing. Isolate variables. Measure one primary metric.
• Statistical significance is not optional. An A/B test that runs for 2 days on 200 visitors is a coin flip, not an experiment. Calculate sample size before starting. Kill tests early only if they are clearly harmful.
• Growth loops beat linear channels. Paid ads scale linearly (spend more, get more). Loops compound: user creates content, content ranks in SEO, new users find it, they create more content. Build at least one loop.
• ICE beats gut feel. Score every experiment idea with Impact, Confidence, and Ease before prioritizing. The team should always be running the highest-ICE experiment first.

Workflow

1. Map the funnel with real numbers. Get conversion rates for each stage. Where is the biggest absolute drop-off?
2. Diagnose the worst stage. Use the funnel diagnostic framework to identify root causes.
3. Generate hypotheses. "We believe [change] will improve [metric] because [evidence]."
4. Score with ICE. Rank all hypotheses. Pick the top 2-3 to run this sprint.
5. Design the experiment. Define control vs variant, primary metric, sample size, and run duration.
6. Run, measure, learn. Ship it, wait for significance, document the result regardless of outcome.
7. Compound wins. Apply learnings to adjacent areas. A headline that works on the homepage may work in ads too.

Funnel Diagnostic Framework

For each stage, ask these questions and look at these metrics:

ACQUISITION (Visitor -> Signup)
  Metrics: Traffic volume, signup rate, CAC by channel
  Diagnose: Where is traffic coming from? Which channels convert?
  Common problems: Wrong audience, weak value prop on landing page, too much friction in signup form
  Quick wins: Reduce form fields, add social proof above the fold, match ad copy to landing page headline

ACTIVATION (Signup -> First Value)
  Metrics: Onboarding completion %, time-to-first-value, feature adoption
  Diagnose: Where do new users drop off in onboarding? How long until they experience the core value?
  Common problems: Too many steps before value, no guided setup, unclear next action
  Quick wins: Checklist onboarding, pre-populate with sample data, skip optional steps

RETENTION (Active -> Returning)
  Metrics: D1/D7/D30 retention, churn rate, engagement frequency
  Diagnose: When do users stop coming back? What do retained users do that churned users do not?
  Common problems: No habit loop, no re-engagement triggers, product does not deliver recurring value
  Quick wins: Email/push nudges at drop-off points, weekly digest, feature announcements

REFERRAL (User -> Invites others)
  Metrics: Viral coefficient (K-factor), invite rate, referral conversion
  Diagnose: Do users have a reason to invite others? Is sharing frictionless?
  Common problems: No incentive, sharing is buried in settings, no network effects
  Quick wins: In-context share prompts (after a win moment), double-sided incentives

REVENUE (User -> Paying customer)
  Metrics: Trial-to-paid %, ARPU, expansion revenue, LTV
  Diagnose: Why do free users not convert? Where in the upgrade flow do they drop?
  Common problems: Free tier too generous, pricing page confusion, no urgency to upgrade
  Quick wins: Usage-based nudges ("You've used 80% of your free quota"), annual discount, remove friction from checkout

Examples

ICE Scoring Template

#	Experiment	Impact (1-10)	Confidence (1-10)	Ease (1-10)	ICE Score	Status
1	Reduce signup form from 5 fields to 2 (email + password)	8	7	9	8.0	Run this
2	Add customer logos to landing page hero	5	6	10	7.0	Next up
3	Rebuild onboarding as interactive tutorial	9	5	3	5.7	Backlog

How to score: Impact = how much will the primary metric move if this works? Confidence = how sure are you it will work (based on data, not gut)? Ease = how quickly can the team ship it (10 = one day, 1 = one quarter)?

Experiment Design Template

## Experiment: [Name]
**Hypothesis:** We believe [change] will improve [metric] by [X%] because [evidence/reasoning].
**Primary metric:** [One metric, e.g., signup rate]
**Secondary metrics:** [Guard rails, e.g., activation rate should not drop]
**Control:** [Current experience]
**Variant:** [Changed experience -- be specific]
**Sample size needed:** [Calculate: https://www.evanmiller.org/ab-testing/sample-size.html]
**Expected duration:** [X days at current traffic]
**Decision rule:** Ship variant if primary metric improves by >= [MDE] with p < 0.05

Output Templates

Funnel Analysis Report

## Funnel Analysis: [Product/Feature]

### Current Funnel Performance
| Stage | Users | Conversion | Benchmark | Status |
|-------|-------|-----------|-----------|--------|
| Visit | 10,000 | -- | -- | -- |
| Signup | 800 | 8.0% | 5-15% | OK |
| Activated | 200 | 25.0% | 40-60% | ** Problem ** |
| Retained (D30) | 100 | 50.0% | 30-50% | OK |

### Diagnosis
The biggest drop-off is at activation (signup -> first value). [Specific evidence for why.]

### Top 3 Experiments (by ICE)
1. [Experiment] -- ICE: X.X
2. [Experiment] -- ICE: X.X
3. [Experiment] -- ICE: X.X

### Estimated Impact
If activation improves from 25% to 40%, D30 active users increase from 100 to 160 (+60%).

Anti-Patterns

• Optimizing acquisition when activation is broken. More traffic into a broken onboarding just means more users who leave and never come back. Fix the leakiest stage first.
• Running tests without a hypothesis. "Let's try a green button" is not an experiment. "We believe a higher-contrast CTA will increase clicks because our current button blends with the background" is.
• Calling a test early. 3 days and 150 visitors is not enough. You will ship false positives. Set the sample size before starting and commit to it.
• Copy-pasting competitors' tactics. Their funnel, audience, and product are different. What works for Dropbox's referral program will not work for your B2B SaaS unless you understand the underlying mechanic.
• Ignoring retention for acquisition. Acquiring users who churn in a week is paying to fill a bucket with a hole in it. Retention is the foundation everything else compounds on.