Suede Ab Testing

Suede A/B Test Setup

Use this Suede experimentation playbook to design tests that produce statistically valid, actionable results.

The Iron Law

Predeclare three things before a test launches: sample per variant,
minimum duration, and the decision rule: and read the result only once
all three are satisfied. A result read before then is preliminary.
Never a winner.

• Sample per variant: the Sample Size table below, or a calculator run on your actual baseline.
• Minimum duration: 1 full week (day-of-week variation), 2 business cycles (B2B), through paydays (e-commerce): see the "Minimum Duration Rules" section of references/sample-size-guide.md.
• Decision rule: which metric, at which threshold, decides the call: written down before launch, not after.

Two carve-outs, and only these two:

• A predeclared sequential or always-valid design may look early under its own stopping rule (see "Sequential Testing" in the sample-size guide). Declaring it sequential after the peek does not count.
• A guardrail-triggered stop for harm is a stop, not a winner call. Kill the variant, report no result.

Initial Assessment

Check for .agents/product-marketing.md (or .claude/product-marketing.md, or the legacy product-marketing-context.md) and read it if present, baseline conversion rate, traffic volume, and available tooling decide whether a test is even powerable, and they are usually already written down there.

Then work the intake list under Task-Specific Questions below; ask only what the context file did not already answer.

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

Sample Size

Quick Reference

Calculators:

• Evan Miller's
• Optimizely's

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

Metrics Selection

Primary Metric

• Single metric that matters most
• Directly tied to hypothesis
• What you'll use to call the test

Secondary Metrics

• Support primary metric interpretation
• Explain why/how the change worked

Guardrail Metrics

• Things that shouldn't get worse
• Stop test if significantly negative

Example: Pricing Page Test

• Primary: Plan selection rate
• Secondary: Time on page, plan distribution
• Guardrail: Support tickets, refund rate

Designing Variants

What to Vary

Best Practices

• Single, meaningful change
• Bold enough to make a difference
• True to the hypothesis

Traffic Allocation

Considerations:

• Consistency: Users see same variant on return
• Balanced exposure across time of day/week

Implementation

Client-Side

• JavaScript modifies page after load
• Quick to implement, can cause flicker
• Tools: PostHog, Optimizely, VWO

Server-Side

• Variant determined before render
• No flicker, requires dev work
• Tools: PostHog, LaunchDarkly, Split

Running the Test

Pre-Launch Checklist

Each box names the artifact that closes it. An unchecked box means the test is running unvalidated: any result it produces is reportable only as unverified, and a silently broken variant invalidates the entire run's traffic.

• Hypothesis documented: written in the framework structure above, saved with the test record
• Primary metric defined: the metric name plus the predeclared decision rule
• Sample size calculated: n per variant and the projected end date, from the table or a calculator
• Variants implemented correctly: a screenshot or recording of each variant exactly as served
• Tracking verified: a fired-event readback showing the exposure and conversion events with correct properties (use suede-analytics for the instrumentation and the readback)
• QA completed on all variants: a pass on every browser and device class the test will serve

During the Test

DO:

• Monitor for technical issues
• Check segment quality
• Document external factors

Avoid:

• Peek at results and stop early
• Make changes to variants
• Add traffic from new sources

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

• 95% confidence = p-value < 0.05
• Means <5% chance result is random
• Not a guarantee: just a threshold

Analysis Checklist

1. Reach sample size? If not, result is preliminary
2. Statistically significant? Check confidence intervals
3. Effect size meaningful? Compare to MDE, project impact
4. Secondary metrics consistent? Support the primary?
5. Guardrail concerns? Anything get worse?
6. Segment differences? Mobile vs. desktop? New vs. returning?

Interpreting Results

Documentation

Document every test with:

• Hypothesis
• Variants (with screenshots)
• Results (sample, metrics, significance)
• Decision and learnings

For templates: See 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:

ICE Prioritization

Score each hypothesis 1-10 on three dimensions:

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

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