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Review a page before you start testing

Use this playbook when you have a live or nearly finished page and want to decide what is actually worth changing. The goal of a CRO review is not to generate a long list of subjective design opinions. It is to identify customer decisions that may be difficult, unclear, unsupported, or distracting, then turn those observations into testable hypotheses.

Start with the visitor, not the page

Before reviewing the design, define:
A page can be “good” in isolation and still be wrong for its traffic.

Review the page through six questions

1. Does the first screen make sense after the click?

Check:
  • campaign message match
  • product continuity
  • offer continuity
  • clear primary action
  • enough context to know what the page is about

2. Is the value proposition specific?

Look for language that could belong to any competitor:
  • “premium quality”
  • “designed for you”
  • “game-changing”
  • “feel your best”
Replace generic language only when you have a more specific, approved product truth.

3. Is proof close to the claim it supports?

Ask whether the visitor sees enough credible evidence at the moment skepticism is likely to appear. Proof can include approved:
  • product facts
  • ratings and reviews
  • creator or founder material
  • comparison evidence
  • guarantees or policies
  • certifications or awards
Do not score a page higher simply because it has many badges. Relevance matters more than quantity.

4. Is the buying decision easy to understand?

Check:
  • what the visitor is buying
  • variant or option labels
  • one-time versus subscription choice
  • actual offer
  • CTA action
  • next step after the click
A visual product card can look clear while still sending the wrong variant or purchase choice to cart, so final QA remains necessary.

5. Is the page asking for too much attention?

Look for:
  • several competing CTAs
  • repeated benefits
  • long copy without new information
  • decorative UI that competes with product choice
  • sections that exist because “landing pages have this section” rather than because the visitor needs it

6. Does mobile preserve the same hierarchy?

Mobile is not a smaller desktop screenshot. Check whether the important message, proof, and action still appear in a useful order without excessive scrolling or cramped interactions.

Ask Jurni for a diagnosis, not an automatic redesign

Use:

Separate evidence from opinion

Not all observations have the same confidence. Useful evidence can come from:
  • experiment results
  • page analytics
  • high drop-off before a key step
  • recurring support or customer objections
  • campaign-to-page mismatch you can directly observe
  • repeated usability issues
  • clear inconsistencies in product/offer information
Lower-confidence ideas can still be worth testing, but label them honestly.

Prioritize by decision impact

A practical prioritization question is:
If this hypothesis is right, how important is the customer decision it changes?
Higher-impact decisions often include:
  • whether the page feels relevant after the click
  • whether the visitor understands the product
  • whether they trust the main claim
  • whether the offer is clear
  • whether they understand the purchase choices
  • whether the CTA takes them where expected
Changing a border radius can matter if it solves a real comprehension issue. Usually it does not deserve the same priority as the questions above.

Turn one finding into a test brief

Example:
Now you have a useful test—not “make the hero better.”

What good looks like

A good CRO review ends with fewer, stronger hypotheses. You should know:
  • what problem you observed
  • what customer decision may be affected
  • what single change you want to test
  • what will remain constant
  • which metric matches the hypothesis

Common mistakes

Asking AI to optimize the entire page. You may get a nicer page but learn very little. Treating best practices as evidence. “Put the CTA above the fold” is a hypothesis unless your own context makes the issue obvious. Choosing tests because they are easy. Test important decisions first. Using analytics without context. A number does not tell you why it happened. Changing several major things in one variant. If it wins, you will not know which change mattered.

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