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A/B testing strategy and implementation for funnel pages. Defines what to test, how to structure variants, statistical significance thresholds, and common testing patterns.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Decision-tree skill that recommends the optimal funnel type based on the user's business model, price point, audience, and goals. Returns a ranked recommendation with reasoning.
Builds a 2-page community group growth funnel. Drives free opt-ins to a group (Skool, Whop, Facebook Group, Discord, or any community platform) using a lead magnet. Use when the goal is to grow a free or paid community.
Reverse-engineer any live public sales funnel and rebuild it — adapted for the user's brand, copy, and offer — without copyright infringement. Handles single pages or entire multi-page funnels. Uses Russell Brunson's Hook/Story/Offer framework to deconstruct what makes a funnel convert, then delegates the build to the funnel-builder agent.
Automated evergreen webinar funnel. Simulates a "live" experience on-demand so leads can register and watch immediately (or at scheduled slots). Best for scaling a proven webinar without live hosting.
High-ticket sales funnel for offers $3,000+. Combines authority content, social proof, and a booked-call CTA. Best for coaching programs, masterminds, done-for-you services, and enterprise solutions.
Recurring membership/subscription funnel. Acquires members with a free trial or low entry, then retains with ongoing value. Best for communities, content libraries, and recurring SaaS.
| name | ab-testing |
| description | A/B testing strategy and implementation for funnel pages. Defines what to test, how to structure variants, statistical significance thresholds, and common testing patterns. |
Test everything. Opinions are nice — data is better.
| Priority | Element | Expected Impact |
|---|---|---|
| 🔴 P0 | Headline | 10–50% lift |
| 🔴 P0 | CTA text + color | 5–30% lift |
| 🟡 P1 | Hero image/video | 5–20% lift |
| 🟡 P1 | Form fields (fewer vs. more) | 10–40% lift |
| 🟡 P1 | Social proof placement | 5–15% lift |
| 🟢 P2 | Page layout (long vs. short) | 5–20% lift |
| 🟢 P2 | Pricing display | 5–25% lift |
| 🟢 P2 | Urgency messaging | 3–15% lift |
| 🔵 P3 | Color scheme | 2–10% lift |
| 🔵 P3 | Font choices | 1–5% lift |
HYPOTHESIS: If we change [element] from [current] to [proposed],
then [metric] will [increase/decrease] by [estimated %]
because [reasoning based on conversion principles].
TEST SETUP:
- Control (A): [Current version description]
- Variant (B): [New version description]
- Primary metric: [Conversion rate / Click rate / etc.]
- Secondary metric: [Revenue / Engagement / etc.]
- Required sample: [Number] visitors per variant
- Estimated duration: [X] days at [Y] daily visitors
After each test, log:
TEST: [Test Name]
DATE: [Start] → [End]
TRAFFIC: [Total visitors] ([Per variant])
RESULTS:
Control: [X]% conversion ([N] conversions)
Variant: [Y]% conversion ([N] conversions)
WINNER: [Control/Variant]
LIFT: [+/- X]%
CONFIDENCE: [X]%
NEXT: [What to test next based on learnings]