The mega-menu presents an overwhelming number of categories and subcategories, risking choice paralysis and navigation abandonment.
CURRENT — Overwhelming menu
scroll ↕ — this is the live page
⚠ Mega-menu choice overloadThe mega-menu presents an overwhelming number of categories and subcategories, risking choice paralysis and navigation abandonment. With 20+ items …
RECOMMENDED — Simplified menu
Reduced menu itemsRemoved deep nestingClearer category hierarchy
Navigation
Wildly Comfortable. Super Natural.
Shop by category
New Arrivals
Shop All
Bestsellers
Men's Shoes
Women's Shoes
Apparel
SHOP MENSHOP WOMEN
✓ Simplified menuTop-level categories only, reducing cognitive load and helping users find what they need faster.
FixSimplify the mega-menu to top-level categories with limited subcategories, or use a 'Shop All' as primary CTA to reduce cognitive load. By trimming the list to essential categories like Men's Shoes, Women's Shoes, and Apparel, users can quickly identify their desired section. This reduces the number of choices and streamlines the navigation experience, encouraging deeper exploration and higher conversion rates.
Evidence
Observed on the live site
✓ Flagged independently by 1 of 7 analysts
The homepage nav_items list includes 20+ items such as 'New Arrivals', 'Shop All', 'Bestsellers', 'LEATHER ALTERNATIVES', 'Men's Shoes', 'Sneakers', 'Slip Ons', 'Sandals', 'Active', 'All-Weather', 'Runner NZ', 'Cruiser', 'Tree Runner NZ', 'Socks', 'Men's Apparel', 'Women's Shoes', 'Trainers', 'Flats', 'Canvas Cruiser', 'Women's Apparel'.
Business case
If this change wins
$35,381 – $176,904
annual opportunity if it wins
Medium
Effort — 2-5 days
not testable at current traffic
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 mega-menu presents an overwhelming number of categories and subcategories, risking choice paralysis and navigation abandonment.
Current state
h1: Wildly Comfortable. Super Natural.; cta: SHOP MEN / SHOP WOMEN; notes: Mega-menu has multiple columns and deep nesting, potentially overwhelming users.
Change
h1: Wildly Comfortable. Super Natural.; cta: SHOP MEN / SHOP WOMEN; notes: Simplify mega-menu to top-level categories with limited subcategories, or use a 'Shop All' as primary CTA to reduce cognitive load.
Acceptance criteria
GIVEN a first-time visitor on the affected page
WHEN the change is live
THEN Simplify mega-menu to top-level categories with limited subcategories, or use a 'Shop All' as primary CTA to reduce cognitive load,
with no regression at 375px and 1280px widths.
Tracking
event: cro_mega_menu_choice_overload 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
299,882 visitors per variant
(95% significance, 80% power);
not testable at current traffic.
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 1.0-5.0% relative lift x $78 order value
The 1.0–5.0% relative-lift band is an assumption bounded by severity (High), 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, 5.0% lift
299,882
Sample per variant, 1.0% lift
7,353,946
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.