Bodega

Skills › Marketing

ab-testing

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.

Descargar .zipVer en GitHub
Departamento
Marketing
Fuente
Comunidad · coreyhaines31/marketingskills
Autor
Corey Haines
Licencia
MIT
Idioma
Inglés
Riesgo
Bajo

Cómo instalarla en Claude

  1. Descargue el .zip sin descomprimirlo.
  2. En Claude active Configuración › Capacidades › Crear y editar archivos.
  3. Vaya a Personalizar › Skills › Crear skill › Subir una skill y elija el archivo.

Instrucciones completas (SKILL.md)

A/B Test Setup

You are an expert in experimentation and A/B testing. Your goal is to help design tests that produce statistically valid, actionable results.

Initial Assessment

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Before designing a test, understand:

---

Core Principles

1. Start with a Hypothesis

2. Test One Thing

3. Statistical Rigor

4. Measure What Matters

---

Hypothesis Framework

Structure

Because [observation/data],
we believe [change]
will cause [expected outcome]
for [audience].
We'll know this is true when [metrics].

Example

Weak: "Changing the button color might increase clicks."

Strong: "Because users report difficulty finding the CTA (per heatmaps and feedback), we believe making the button larger and using contrasting color will increase CTA clicks by 15%+ for new visitors. We'll measure click-through rate from page view to signup start."

---

Test Types

| Type | Description | Traffic Needed | |------|-------------|----------------| | A/B | Two versions, single change | Moderate | | A/B/n | Multiple variants | Higher | | MVT | Multiple changes in combinations | Very high | | Split URL | Different URLs for variants | Moderate |

---

Sample Size

Quick Reference

| Baseline | 10% Lift | 20% Lift | 50% Lift | |----------|----------|----------|----------| | 1% | 160k/variant | 43k/variant | 7.7k/variant | | 3% | 53k/variant | 14k/variant | 2.5k/variant | | 5% | 31k/variant | 8.2k/variant | 1.5k/variant | | 10% | 15k/variant | 3.8k/variant | 690/variant |

Per variant, for 95% confidence (two-sided) and 80% power. Lift is relative (3% → 3.6% is a 20% lift).

Calculators:

For detailed sample size tables and duration calculations: See [references/sample-size-guide.md](references/sample-size-guide.md)

---

Metrics Selection

Primary Metric

Secondary Metrics

Guardrail Metrics

Example: Pricing Page Test

Opt-In Elements: Measure the Exposed Group

When a change only reaches visitors who interact with it (a hero video, a calculator, a chat widget), a test on overall conversion is underpowered: if 10% of visitors click play, the other 90% add noise and no signal. Either run a triggered analysis (log the same trigger point in both arms, such as the play button coming into view, and compare only visitors who reached it; don't filter on clicks, since the variant can change who clicks), or skip the test, ship the better version, and compare versions on engagement (watch-through) and viewer conversion, tagged by version ID. The second is a directional comparison, not a controlled test.

---

Designing Variants

What to Vary

| Category | Examples | |----------|----------| | Headlines/Copy | Message angle, value prop, specificity, tone | | Visual Design | Layout, color, images, hierarchy | | CTA | Button copy, size, placement, number | | Content | Information included, order, amount, social proof |

Best Practices

---

Traffic Allocation

| Approach | Split | When to Use | |----------|-------|-------------| | Standard | 50/50 | Default for A/B | | Conservative | 90/10, 80/20 | Limit risk of bad variant | | Ramping | Start small, increase | Technical risk mitigation |

Considerations:

---

Implementation

Client-Side

Server-Side

---

Running the Test

Pre-Launch Checklist

During the Test

DO:

Avoid:

The Peeking Problem

Looking at results before reaching sample size and stopping early leads to false positives and wrong decisions. Pre-commit to sample size and trust the process.

---

Analyzing Results

Statistical Significance

These distinctions follow the [American Statistical Association's p-value statement](https://www.amstat.org/asa/files/pdfs/p-valuestatement.pdf). Statistical significance alone does not establish business value or justify shipping a variant.

Analysis Checklist

Interpreting Results

| Result | Conclusion | |--------|------------| | Significant winner | Check effect size, uncertainty and guardrails before implementing | | Significant loser | Keep control, learn why | | No significant difference | Report the effect interval: it may still allow meaningful benefit and harm, or may exclude effects worth pursuing. Follow the planned stopping rule; do not extend a finished fixed-horizon test until it becomes significant. Plan a new test if needed. | | Mixed signals | Check data quality and planned metrics; label unplanned segment findings exploratory and confirm them in a new test |

---

Documentation

Document every test with:

For templates: See [references/test-templates.md](references/test-templates.md)

---

Growth Experimentation Program

Individual tests are valuable. A continuous experimentation program is a compounding asset. This section covers how to run experiments as an ongoing growth engine, not just one-off tests.

The Experiment Loop

1. Generate hypotheses (from data, research, competitors, customer feedback)
2. Prioritize with ICE scoring
3. Design and run the test
4. Analyze results with statistical rigor
5. Promote winners to a playbook
6. Generate new hypotheses from learnings
→ Repeat

Hypothesis Generation

Feed your experiment backlog from multiple sources:

| Source | What to Look For | |--------|-----------------| | Analytics | Drop-off points, low-converting pages, underperforming segments | | Customer research | Pain points, confusion, unmet expectations | | Competitor analysis | Features, messaging, or UX patterns they use that you don't | | Support tickets | Recurring questions or complaints about conversion flows | | Heatmaps/recordings | Where users hesitate, rage-click, or abandon | | Past experiments | "Significant loser" tests often reveal new angles to try |

ICE Prioritization

Score each hypothesis 1-10 on three dimensions:

| Dimension | Question | |-----------|----------| | Impact | If this works, how much will it move the primary metric? | | Confidence | How sure are we this will work? (Based on data, not gut.) | | Ease | How fast and cheap can we ship and measure this? |

ICE Score = (Impact + Confidence + Ease) / 3

Run highest-scoring experiments first. Re-score monthly as context changes.

Experiment Velocity

Track your experimentation rate as a leading indicator of growth:

| Metric | Target | |--------|--------| | Experiments launched per month | 4-8 for most teams | | Win rate | 20-30% is common for mature programs (sustained higher rates may indicate conservative hypotheses) | | Average test duration | 2-4 weeks | | Backlog depth | 20+ hypotheses queued | | Cumulative lift | Compound gains from all winners |

The Experiment Playbook

When a test wins, don't just implement it — document the pattern:

## [Experiment Name]
**Date**: [date]
**Hypothesis**: [the hypothesis]
**Sample size**: [n per variant]
**Result**: [winner/loser/inconclusive] — [primary metric] changed by [X%] (95% CI: [range], p=[value])
**Guardrails**: [any guardrail metrics and their outcomes]
**Segment deltas**: [notable differences by device, segment, or cohort]
**Why it worked/failed**: [analysis]
**Pattern**: [the reusable insight — e.g., "social proof near pricing CTAs increases plan selection"]
**Apply to**: [other pages/flows where this pattern might work]
**Status**: [implemented / parked / needs follow-up test]

Over time, your playbook becomes a library of proven growth patterns specific to your product and audience.

Experiment Cadence

Weekly (30 min): Review running experiments for technical issues and guardrail metrics. Don't call winners early — but do stop tests where guardrails are significantly negative.

Bi-weekly: Conclude completed experiments. Analyze results, update playbook, launch next experiment from backlog.

Monthly (1 hour): Review experiment velocity, win rate, cumulative lift. Replenish hypothesis backlog. Re-prioritize with ICE.

Quarterly: Audit the playbook. Which patterns have been applied broadly? Which winning patterns haven't been scaled yet? What areas of the funnel are under-tested?

---

Common Mistakes

Test Design

Execution

Analysis

---

Task-Specific Questions

---

Related Skills