Mvp Definer Growth

mvp-definer-growth

Agent: Growth Lead

L1 growth leader (1x) responsible for distribution strategy, activation signal definition, retention modelling, growth model design, and growth loop optimisation.

Department ethos: ideal-data-growth.md

Skill Description

The MVP growth definer scopes the smallest possible experiments that can validate or invalidate the growth model's key assumptions — channel CAC, activation rate, viral coefficient — before committing resources to full-scale execution.

When to Use

• When the growth model contains untested assumptions about channel economics or activation rates.
• When a new acquisition channel is proposed and needs a minimum viable test before budget allocation.
• When the team plans the first growth sprint and needs to prioritize which hypotheses to test.
• When a growth initiative has been running without clear experiment structure or success criteria.

Workflow

1. Extract assumptions: List the top 5 assumptions in the growth model ranked by uncertainty and impact. Each assumption becomes a candidate experiment.
2. Define hypotheses: For each assumption, write a falsifiable hypothesis: "We believe [channel/mechanic] will produce [metric] at [threshold] because [rationale]."
3. Scope minimum viable test: Design the smallest experiment that can validate the hypothesis. Define the sample size, duration, budget cap, and success/failure threshold.
4. Calculate required sample: Use power analysis to determine the minimum sample size for the target MDE (minimum detectable effect) at 80% power and 95% confidence.
5. Prioritize experiments: Rank by learning value (which assumption, if wrong, most endangers the growth model) divided by cost (time + budget). Run the highest-value experiments first.
6. Deliver experiment briefs: Produce a one-page brief per experiment with hypothesis, test design, success criteria, sample size, budget, timeline, and kill criteria.

Anti-Patterns

• Over-scoping the MVP test: Running a full campaign instead of a minimum viable test consumes budget that could validate three other assumptions. Why: the goal is learning, not performance; keep tests as small as statistical validity allows.
• No kill criteria: Running experiments without pre-defined failure thresholds leads to indefinite continuation of losing tests. Why: sunk cost bias keeps bad experiments running; kill criteria enforce disciplined capital allocation.
• Testing everything at once: Running all experiments simultaneously makes it impossible to attribute results when they interact. Why: concurrent experiments on overlapping audiences produce confounded results.

Output

Success:

• A set of experiment briefs (1 per hypothesis) with falsifiable hypotheses, test designs, sample size calculations, budgets, timelines, and kill criteria.

Failure:

• The hypothesis cannot be tested at minimum viable scale due to insufficient traffic or budget. Report the constraint, the minimum required scale, and recommend a proxy test or qualitative validation approach.

Related Skills

• growth-model-designer -- the growth model's assumptions generate the hypotheses this skill tests.
• activation-signal-definer -- activation rate is a common hypothesis requiring MVP validation.
• statistical-significance-tracker -- tracks significance of the experiments this skill defines.