Hypothesis Prioritization ranks experiment ideas to determine testing order. With limited testing capacity, prioritization ensures high-impact experiments run first. Several frameworks exist to score and compare hypotheses objectively.
What is Hypothesis Prioritization?
Not all test ideas are equal. Some have high potential impact but uncertain outcomes. Others are easy to implement but may not move metrics significantly. Prioritization frameworks help teams choose wisely.
Prioritization inputs:
- Expected impact on key metrics
- Confidence in the hypothesis
- Implementation effort required
- Test duration needed
- Resource availability
Common Prioritization Frameworks
ICE Score:
ICE = Impact × Confidence × Ease
Each factor scored 1-10. Simple but subjective.
PIE Framework:
- Potential: How much improvement is possible?
- Importance: How valuable is the traffic/page?
- Ease: How difficult to implement?
RICE Score:
RICE = (Reach × Impact × Confidence) / Effort
Adds reach to account for how many users see the change.
Building a Test Backlog
- Collect hypothesis ideas from analytics, user research, and team input
- Score each using chosen framework
- Rank by score
- Review top candidates for feasibility
- Move highest-priority items to testing queue
Avoiding Prioritization Pitfalls
HiPPO effect: Highest Paid Person's Opinion should not override data-driven prioritization.
Recency bias: Recent ideas feel more important. Score all ideas equally.
Complexity avoidance: Hard experiments may have the biggest impact. Do not always choose easy tests.
