ab-test-plan

name	ab-test-plan
description	Generate structured A/B test plans for DTC funnels — hypothesis, control vs variant, primary metric, sample size estimate, test duration, and success criteria using RMBC principles.
model	sonnet
user-invocable	true

ab-test-plan

Purpose

Generate structured A/B test plans for DTC funnels. Testing is how you turn opinions into revenue data. Most teams waste tests by changing too many variables, running too short, or measuring the wrong metric. This skill produces a single, clean test plan with a falsifiable hypothesis, defined control and variant, primary metric, sample size estimate, expected duration, and success criteria. Every test plan connects back to RMBC — you're testing Research assumptions, Mechanism angles, Brief strategies, or Copy execution.

Inputs

Input	Required	Description
page_type	Yes	What you're testing: landing_page, order_form, upsell, email, ad, checkout
current_metric	Yes	Baseline performance: conversion rate, CTR, AOV, or revenue per visitor
hypothesis	Yes	What you believe will improve performance and why
traffic_volume	Yes	Daily unique visitors or impressions to the test page
test_element	Yes	One of: headline, price, offer, layout, copy_length, mechanism, cta, guarantee, social_proof

Execution Protocol

Step 1 — Load Framework Context

Read rmbc-context/SKILL.md to load RMBC framework definitions. A/B testing validates RMBC decisions with data — Research assumptions get tested through audience-facing copy, Mechanism strength gets tested through conversion lift, Brief strategy gets tested through engagement, Copy execution gets tested through click and purchase behavior.

Step 2 — Classify the Test Type

Test Type	What Changes	Typical Lift	Risk Level
Headline	Lead/hook copy only	5-30%	Low — copy swap, no structural change
Price	Price point or framing	10-50%	Medium — affects AOV and refund rate
Offer	What's included in the deal	15-60%	Medium — may affect fulfillment
Layout	Page structure, element order	5-20%	Low-Medium — design change only
Copy Length	Long vs short form	10-40%	Low — same offer, different depth
Mechanism	Which "why it works" angle	10-35%	Low — copy change, different angle
CTA	Button text, placement, urgency	5-15%	Low — smallest change, quickest test
Guarantee	Risk reversal type or duration	5-25%	Low-Medium — may affect refund rate
Social Proof	Testimonials, numbers, authority	5-20%	Low — additive element

Step 3 — Build the Test Plan

3a: Hypothesis Statement

Write a falsifiable hypothesis in this format: "Changing [element] from [control version] to [variant version] will increase [primary metric] by [estimated %] because [RMBC-grounded reason]."

The reason must connect to an RMBC phase:

• Research-based: "Our ICP research shows the audience responds more to fear than desire"
• Mechanism-based: "The current mechanism angle is too generic — the variant names a specific process"
• Brief-based: "The page leads with features when proof should come first for this awareness level"
• Copy-based: "The CTA is vague — benefit-driven button text reduces friction"

3b: Control vs Variant

Define exactly what stays the same and what changes. Only ONE variable changes per test.

3c: Primary Metric

Choose ONE primary metric. Secondary metrics are tracked but don't determine the winner.

Page Type	Primary Metric	Secondary Metrics
Landing page	Conversion rate (visitor → buyer)	Bounce rate, time on page, scroll depth
Order form	Checkout completion rate	Cart abandonment rate, AOV
Upsell	Take rate (% who accept)	Revenue per visitor, refund rate
Email	Click-through rate	Open rate, unsubscribe rate, conversion
Ad	CTR or CPA	CPM, frequency, relevance score

3d: Sample Size Estimate

Calculate minimum sample size per variation using:

• Baseline conversion rate (from current_metric)
• Minimum detectable effect (MDE): 10-20% relative improvement (default 15%)
• Statistical significance: 95% confidence (p < 0.05)
• Statistical power: 80%

Provide the estimate and the formula reasoning. Flag if traffic volume means the test will take longer than 4 weeks — long tests accumulate confounding variables.

3e: Test Duration

Duration = (Sample size per variation × 2) / Daily traffic

Rules:

• Minimum 7 days (capture full weekly cycle)
• Maximum 4 weeks (diminishing returns, external variables)
• Must include at least one full weekend cycle
• If duration exceeds 4 weeks at 15% MDE, recommend increasing MDE to 20-25% or testing a higher-impact element

3f: Success Criteria

Define before launch — never move the goalposts mid-test:

• Primary metric improvement at 95% confidence
• Minimum sample size reached
• No significant negative movement in secondary metrics (especially refund rate for price/offer tests)

Step 4 — Risk Assessment

Flag risks specific to this test type:

• Price tests: Monitor refund rate for 30 days post-test
• Offer tests: Verify fulfillment capacity for the variant
• Layout tests: Check mobile rendering before launch
• Copy length tests: Ensure tracking captures scroll depth

Output Format

## A/B Test Plan: [Test Name]

**Page:** [page_type]
**Element:** [test_element]
**Current Metric:** [baseline]
**Daily Traffic:** [volume]

---

### HYPOTHESIS
[Falsifiable hypothesis statement with RMBC reasoning]

### CONTROL (A)
[Exact description of current version]

### VARIANT (B)
[Exact description of changed version — only ONE variable different]

### METRICS

| | Primary | Secondary |
|---|---------|-----------|
| **Metric** | [metric] | [metric 1], [metric 2] |
| **Current** | [baseline] | [baselines if known] |
| **Target** | [+X% improvement] | [monitor only] |

### SAMPLE SIZE & DURATION

- **Sample per variation:** [number]
- **Total sample needed:** [number]
- **Estimated duration:** [X days]
- **MDE:** [X%]
- **Confidence:** 95%

### SUCCESS CRITERIA
1. [Primary metric criteria]
2. [Sample size criteria]
3. [Secondary metric guardrails]

### RISKS
- [Risk 1 + mitigation]
- [Risk 2 + mitigation]

### NEXT STEPS
- [ ] Implement variant
- [ ] QA on mobile and desktop
- [ ] Set up tracking and dashboards
- [ ] Launch test on [recommended day]
- [ ] Check results at [midpoint] — do NOT stop early
- [ ] Call winner at [end date] if criteria met

Quality Criteria

• Hypothesis must be falsifiable and grounded in a specific RMBC phase — "I think this will work better" is not a hypothesis

• Only ONE variable may change between control and variant — multi-variable tests produce unusable data

• Sample size must be calculated, not guessed — underpowered tests declare false winners

• Duration must include at least one full weekly cycle — weekday-only data skews results

• Success criteria must be defined before launch — post-hoc criteria are just confirmation bias

• Price and offer tests must track refund rates for 30 days — a "winning" price that doubles refunds loses money

• Specificity gate: Every recommendation must include a number, name, or timeframe — no "test for improvement" or "optimize results"

• Mechanism quantification: When referencing the mechanism, include at least one specific data point (number, timeframe, study reference)

• Audience journey: Each recommendation must reference where the reader IS (what they've tried, what's failing) — not just who they are demographically

• Proof diversity: Use at least 2 different proof types (testimonial, statistical, authority, case study) — do not rely on a single proof mode

Related Skills

• Run /funnel-audit to identify which funnel step needs testing most
• Run /hook-battery to generate headline variants for headline tests
• Run /order-form-cro for checkout element test ideas
• Run /lander-copy to generate landing page variant copy
• Run /guarantee-writer for guarantee variant options
• Validate test copy with /rmbc-copy-audit

Attribution

Generated using RMBC framework by Stefan Georgi. Learn more: copyaccelerator.com/join