How to Prioritize Experiments: Building Your Test Backlog

Why Experiment Prioritization Matters

You have more ideas than time. Without prioritization, you'll either:

• Test random things
• Test easy things instead of important things
• Never test anything (analysis paralysis)

A good experiment backlog helps you focus on tests that actually matter.

Building Your Experiment Backlog

Step 1: Generate Hypotheses

Start by listing what you want to learn:

• "We believe [segment] has [problem]"
• "We believe [message] will resonate with [audience]"
• "We believe [channel] will work because [reason]"

Get everything out of your head and into a list.

Step 2: Define Success Metrics

For each hypothesis, define:

• What will you measure?
• What result would validate the hypothesis?
• What result would invalidate it?

Step 3: Score Each Experiment

Use a simple scoring framework:

Impact (1-5): If this works, how big is the impact? Confidence (1-5): How confident are we this will work? Effort (1-5): How much time/money/resources needed?

Calculate: (Impact × Confidence) / Effort

Step 4: Rank and Sequence

Sort by score. But also consider:

• Dependencies (some tests need to happen first)
• Timing (seasonality, launches, etc.)
• Resources (what can you actually run right now?)

Prioritization Frameworks

ICE Score

• Impact: How much will this move the needle?
• Confidence: How sure are we this will work?
• Ease: How easy is this to implement?

Score each 1-10, multiply together.

RICE Score

• Reach: How many people does this affect?
• Impact: How much does it affect them?
• Confidence: How sure are we?
• Effort: How much work is this?

Calculate: (Reach × Impact × Confidence) / Effort

Two-by-Two Matrix

Plot experiments on:

• X-axis: Effort (low to high)
• Y-axis: Impact (low to high)

Focus on high-impact, low-effort first.

Types of Experiments

Research Experiments

• Customer interviews
• Competitor analysis
• Market sizing

Message Experiments

• Headline A/B tests
• Ad copy variations
• Email subject lines

Channel Experiments

• New acquisition channels
• Content types
• Partnership tests

Product Experiments

• Feature validation
• Pricing tests
• Onboarding variations

Running Experiments Efficiently

Batch Similar Tests

Run related experiments together to share setup costs.

Set Time Limits

"We'll test this for 2 weeks" prevents experiments from running forever.

Define Kill Criteria

Know when to stop a losing experiment.

Document Everything

Record hypothesis, method, results, and learnings.

Common Prioritization Mistakes

Testing what's easy, not important: Quick wins feel productive but may not matter.

Over-engineering experiments: You don't need statistical significance for everything.

Never killing experiments: Sunk cost fallacy kills velocity.

Not learning from failures: Failed experiments are still valuable data.

Sample Experiment Backlog

| Experiment | Hypothesis | Success Metric | ICE Score | |------------|-----------|----------------|-----------| | Homepage headline test | "Clarity" will outperform "clever" | Click-through rate +20% | 8 | | LinkedIn outreach | Decision-makers respond to problem-led messages | 10% response rate | 7 | | Case study on homepage | Social proof increases demo requests | Demo requests +15% | 6 | | New pricing page | Simpler pricing increases conversions | Conversion +10% | 5 |

Getting Started

List your top 10 experiment ideas. Score each on Impact, Confidence, and Ease. Pick the top 3 and run them this month.