The homepage loads 160 scripts, which is the usual root cause of slow interaction readiness on mobile.
CURRENT — Heavy script load
scroll ↕ — this is the live page
⚠ Heavy third-party script loadThe homepage loads 160 scripts, including 78 inline and 82 external, totaling 2239 KB of HTML. This heavy third-party script load is the usual root …
FixAudit and defer scripts by identifying which third-party tags are essential for core functionality and which can be loaded asynchronously or deferred. Remove duplicated analytics or marketing pixels to reduce the total script count. This will decrease the time to interactive, making the page feel faster and more responsive, thereby improving user experience and conversion rates.
Evidence
Observed on the live site Verified by measurement
✓ Flagged independently by 1 of 7 analysts
Measured on the live homepage: 78 inline and 82 external <script> tags, 2239 KB of HTML.
Business case
If this change wins
0.2 – 1.0
orders per 1,000 visitors
Medium
Effort — 2-5 days
Not A/B testable
At typical traffic
Detecting this needs about 315,206 visitors per variant. Ship it as a sequenced change and log the date instead.
Prioritisation
PXL score: 5 / 10
Assessed from the live page
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?
No
0
Verified by direct technical measurement?
Yes
1
Criteria that need your data
4 of these could not be answered from the live page.
Session recordings, survey or support themes, and analytics access would
raise the confidence of this ranking — they do not indicate a weaker
finding.
Question
Answer
Points
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 homepage loads 160 scripts, which is the usual root cause of slow interaction readiness on mobile.
Current state
notes: 160 scripts, 2239 KB HTML
Change
notes: Audit third-party tags, defer non-critical scripts, and remove duplicated analytics or marketing pixels.
Acceptance criteria
GIVEN a first-time visitor on the affected page
WHEN the change is live
THEN Audit third-party tags, defer non-critical scripts, and remove duplicated analytics or marketing pixels,
with no regression at 375px and 1280px widths.
Tracking
event: cro_heavy_third_party_script_load 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
315,206 visitors per variant
(95% significance, 80% power);
duration depends on traffic.
Rollback
Feature-flag the change; revert the flag if the primary metric
drops for 3 consecutive days.
1,000 visitors × baseline conversion × relative lift
= per 1,000 visitors at an assumed 2.00% baseline conversion rate and a 1.0-5.0% relative lift
The 1.0–5.0% relative-lift band is an assumption bounded by severity (High), not a prediction.
The baseline conversion rate is an assumption — the client did not supply one, so an industry-typical 2.0% is used and labelled as such.
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
2.0% (assumed)
Significance / power
95% / 80%
Sample per variant, 5.0% lift
315,206
Sample per variant, 1.0% lift
7,729,571
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.