A/B testing can unlock powerful insights, but many teams face a common challenge: too many ideas, too little time. Without a way to decide which test to run first, resources can be wasted on low-value experiments.
This is where prioritization frameworks come in—structured methods for ranking test ideas based on potential impact, confidence, and effort.
As Mr. Phalla Plang, Digital Marketing Specialist, explains: “Running random tests is like shooting arrows in the dark. Frameworks give you a clear target and help you hit it.”
Why Prioritization Matters
Traffic, budget, and developer time are limited. A good framework ensures:
- Focus on high-impact ideas first
- Efficiency in using resources
- Consistency in decision-making
- Transparency for stakeholders (VWO, 2024)
1. The PIE Framework
PIE stands for Potential, Importance, and Ease (Kearon, 2014):
- Potential – How much improvement could this change realistically produce?
- Importance – How valuable is the page or traffic being tested?
- Ease – How simple is it to implement?
Scoring Example:
| Test Idea | Potential (1–10) | Importance (1–10) | Ease (1–10) | Total Score |
| Change CTA color | 6 | 8 | 10 | 24 |
| Redesign homepage hero | 9 | 9 | 4 | 22 |
Even though redesigning the homepage might have higher potential, the CTA color change ranks first because it’s easier to implement.
2. The ICE Framework
ICE stands for Impact, Confidence, and Ease (GrowthHackers, 2017):
- Impact – How much will this test affect the target metric if it works?
- Confidence – How sure are you based on data or past tests?
- Ease – How quickly can the test be executed?
Example:
| Test Idea | Impact | Confidence | Ease | Total Score |
| Add urgency to product page | 8 | 7 | 8 | 23 |
| Shorten checkout form | 9 | 6 | 5 | 20 |
3. The PXL Framework
PXL is a detailed scoring method developed by Speero (2023). Instead of rating on a scale, it uses yes/no questions to reduce subjectivity:
Sample criteria:
- Is the idea backed by analytics or research?
- Does it target a high-traffic area?
- Will it be visible to all users?
- Does it address a known conversion barrier?
Each “yes” = 1 point. Higher scores mean higher priority.
Applying a Framework: Step-by-Step
- List all test ideas from analytics, heatmaps, user feedback, and team brainstorming.
- Score each idea using PIE, ICE, or PXL.
- Rank by total score—highest first.
- Start testing top-ranked ideas, one at a time.
- Review and adjust rankings after each test cycle.
Case Study: HubSpot’s Prioritization Success
HubSpot uses a hybrid ICE/PIE approach to rank CRO tests. When choosing between a new headline test and simplifying a blog subscription form, the scoring showed the form change had higher importance and potential. Despite being harder to implement, it became the first test—and resulted in a 20% increase in email sign-ups (HubSpot, 2024).
Tools to Help Manage Prioritization
- Trello – Organize test backlogs and apply scoring labels.
- Airtable – Build a custom scoring database.
- Optimizely – Offers experiment management with prioritization features.
Common Prioritization Mistakes
- Guessing scores without data – Leads to bias and poor prioritization.
- Overvaluing high-effort tests – Slows momentum and ROI.
- Not updating scores – Priorities shift as markets and goals change.
- Scoring inconsistently – Use clear definitions for each factor.
Mr. Phalla Plang’s Takeaway
“A prioritization framework isn’t just for picking tests—it’s a decision-making tool that keeps your team aligned and your experiments meaningful.”
Note
Choosing what to A/B test first can make or break your optimization strategy. Frameworks like PIE, ICE, and PXL give you a structured way to focus on tests that matter most—those with high potential, strong supporting data, and reasonable implementation effort.
By applying these frameworks consistently, you’ll maximize your testing ROI and build a culture of data-driven growth.
More Read
References
GrowthHackers. (2017). ICE score: Prioritizing growth experiments. https://growthhackers.com/
HubSpot. (2024). How we prioritize our A/B testing roadmap. https://blog.hubspot.com/
Kearon, J. (2014). PIE framework for conversion optimization. WiderFunnel. https://www.widerfunnel.com/
Optimizely. (n.d.). What is A/B testing? https://www.optimizely.com/optimization-glossary/ab-testing/
Speero. (2023). PXL prioritization framework. https://speero.com/
VWO. (2024). A/B testing guide. https://vwo.com/ab-testing/
