Two primary CTAs in the hero split user focus and dilute the main conversion path.
CURRENT — Two competing hero CTAs
⚠ Competing hero CTAsThe hero presents two equally prominent CTAs, 'SHOP MEN' and 'SHOP WOMEN', forcing users to choose a gender before …
RECOMMENDED — Primary CTA emphasized
Dim secondary CTAAdd primary badge
FixDimming the 'SHOP MEN' CTA and adding a 'Primary' badge to 'SHOP WOMEN' demonstrates how emphasizing a single action can reduce choice overload and guide users toward a primary conversion.
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 dominant action.
H1: 'Wildly Comfortable. Super Natural.' CTAs: 'SHOP MEN' and 'SHOP WOMEN' with no subhead visible in the crawl.
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?
Two primary CTAs in the hero split user focus and dilute the main conversion path.
Current state
h1: Wildly Comfortable. Super Natural.; cta: SHOP MEN | SHOP WOMEN; notes: Both CTAs are equally prominent, forcing users to choose a gender before exploring products.
Change
h1: Wildly Comfortable. Super Natural.; cta: Shop All; notes: Single primary CTA to browse all products, with secondary gender links in nav.
Acceptance criteria
GIVEN a first-time visitor on the affected page
WHEN the change is live
THEN Single primary CTA to browse all products, with secondary gender links in nav,
with no regression at 375px and 1280px widths.
Tracking
event: cro_competing_hero_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.