FixEmphasize SHOP MEN as the primary CTA with a solid dark background and a 'Primary' badge, while de-emphasizing SHOP WOMEN to guide users toward a single next step.
Both are photographs of the live page — the right side has the
change applied in the browser.
Evidence
Observed on the live site
✓ Flagged independently by 2 of 7 analysts
Hero section contains both 'SHOP MEN' and 'SHOP WOMEN' CTAs, with no single primary action; the page also has 'Shop All' in the nav and 'Shop + -' in the cart drawer.
H1: 'Wildly Comfortable. Super Natural.' CTAs: 'SHOP MEN', 'SHOP WOMEN'
Business case
If this change wins
$70,762 – $283,046
annual opportunity if it wins
Medium
Effort — 2-5 days
40 days
A/B test duration
Prioritisation
PXL score: 5 / 10
Question
Answer
Points
Is the change above the fold?
Yes
1
Is the change noticeable in under 5 seconds?
Yes
2
Does it add or remove an element?
Yes
1
Does it run on a high-traffic page?
Yes
1
Discovered via user testing?
No
0
Discovered via qualitative feedback (survey, support)?
No
0
Supported by heatmaps or session recordings?
No
0
Found via digital analytics or real-user field data?
The hero presents two equally prominent CTAs (SHOP MEN and SHOP WOMEN) that split user intent and delay the path to purchase.
Current state
h1: Wildly Comfortable. Super Natural.; cta: SHOP MEN / SHOP WOMEN; notes: Two CTAs of equal weight; user must choose a gender before seeing products, adding friction.
Change
h1: Wildly Comfortable. Super Natural.; cta: Shop All; notes: Make 'Shop All' the primary CTA to reduce decision load; keep gender links as secondary nav.
Acceptance criteria
GIVEN a first-time visitor on the affected page
WHEN the change is live
THEN Make 'Shop All' the primary CTA to reduce decision load; keep gender links as secondary nav,
with no regression at 375px and 1280px widths.
Tracking
event: cro_competing_primary_ctas on interaction with the
changed element; verify it fires in BOTH variants before opening traffic.
Success metric
conversion rate on the affected step
QA checklist
Chrome / Safari / Firefox · 375px + 1280px ·
keyboard reachable · screen-reader name present · no CLS introduced
Effort
Medium —
2-5 days
Test plan
118,817 visitors per variant
(95% significance, 80% power);
40 days.
Rollback
Feature-flag the change; revert the flag if the primary metric
drops for 3 consecutive days.
annual visits × baseline conversion × relative lift × order value
= 2,160,000 annual visits x 2.10% conversion x 2.0-8.0% relative lift x $78 order value
The 2.0–8.0% relative-lift band is an assumption bounded by severity (Urgent), not a prediction.
Why the range is conservative: Across 127,000 experiments, 12% produced a statistically significant improvement on the primary metric; a healthy programme win rate is 10-30%. Winning tests also overstate their true effect, so treat this as the prize if the change works — never expected revenue.
How effort is estimated
Bracket
Days
Typical scope
Low
0.5–2
copy, colour, labels, image swaps
Medium
2–5
layout shifts, form changes, new modules
High
5+
personalisation, funnel work, integrations
Assigned by matching the recommended change against these scopes; the estimate is a bracket, not a quote.
Sample size math
Two-proportion z-test, the standard pre-test sample-size calculation:
n = [z₀.₀₂₅·√(2·p̄·(1−p̄)) + z₀.₂₀·√(p₁(1−p₁)+p₂(1−p₂))]² / (p₁−p₂)²
Baseline conversion
2.1%
Significance / power
95% / 80%
Sample per variant, 8.0% lift
118,817
Sample per variant, 2.0% lift
1,847,435
Halving the minimum detectable effect roughly quadruples the sample required, which is why the conservative figure is so much larger.
What PXL is
PXL is CXL's prioritisation framework: binary questions instead of subjective 1–10 guesses, weighted toward evidence.
Findings discovered by inspection score zero on all four evidence questions — that is the honest signal, not a defect. Findings verified by direct measurement earn the extension point and, where real-user field data is involved, the analytics point.