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performance:testes

Plan A/B tests and allocate experiment budgets for performance campaigns.

Updated Apr 9, 2026
Search Tags:#hypothesis testing#conversion optimization#a/b testing#test planning#performance marketing#budget allocation
One-click install
npx skills add https://github.com/eusouwillnunes/sistema-maestro --skill performance-testes
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: performance:testes
Source: https://github.com/eusouwillnunes/sistema-maestro/tree/main/skills/performance/testes
Command: npx skills add https://github.com/eusouwillnunes/sistema-maestro --skill performance-testes

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Helps marketing teams and analysts design structured A/B tests and allocate experiment budget so tests target the highest-impact bottlenecks and avoid wasted spend.

Core Features & Use Cases

  • Hierarchical Test Prioritization: Prioritizes tests by impact (offer, audience, creative, copy, landing page, format) to focus efforts where they matter most.
  • Test Plan Template: Produces hypotheses, variables, minimum durations, sample-volume rules, success metrics, and Go/No-Go decision criteria.
  • Budget Allocation Guidance: Recommends percentage splits for channel, tests, and remarketing based on business stage (start, growth, maturity).
  • Use Case: Turn campaign metrics (CTR, CPC, CPA, LP conversion) into a prioritized testing roadmap with required sample sizes and a recommended split of test budget.

Quick Start

Ask the Maestro to analyze my campaign metrics and produce a prioritized A/B test plan with hypotheses, minimum durations, success metrics, Go/No-Go criteria, and recommended budget allocation.

Frequently Asked Questions about performance:testes

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prioritize A/B tests for performance marketing campaigns?▼

A/B test prioritization ranks experiments by impact across offer, audience, creative, copy, and landing page variables. This hierarchical approach focuses testing efforts on the highest-impact bottlenecks to maximize conversion optimization results.

What's the best way to allocate experiment budget across marketing channels?▼

Experiment budget allocation recommends percentage splits for channels, tests, and remarketing based on business stage. Whether starting, growing, or mature, budget allocation guidance distributes test spend to avoid wasted ad budget.

How do I structure A/B test hypotheses and Go/No-Go criteria?▼

A/B test plan templates produce explicit hypotheses, variables, minimum durations, sample-volume rules, success metrics, and Go/No-Go decision criteria. This structured test planning ensures campaigns target bottlenecks with measurable significance thresholds.

Can I use A/B testing for audience segmentation and remarketing experiments?▼

A/B testing applies to audience segmentation, creative variations, landing page experiments, and remarketing across campaign stages. Performance marketing scenarios include offer testing and copy variations to validate hypotheses before scaling spend.

How do I calculate minimum test duration and sample volume for ad experiments?▼

Minimum test durations and sample-volume rules are generated alongside success metrics and significance criteria. These calculations prevent premature test conclusions and ensure statistical reliability for performance marketing experiments.

Why does my A/B test planning fail to improve campaign metrics?▼

A/B test planning fails when tests lack hierarchical prioritization, explicit hypotheses, or proper budget allocation. Without structured Go/No-Go criteria and sample-volume rules, experiments waste budget on low-impact variables instead of high-impact bottlenecks.

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