At a glance
Per-URL pass/fail status across all three Core Web Vitals simultaneously. A URL passes only if all three of LCP, INP, and CLS meet Google’s “good” thresholds; failing any single CWV fails the row. The unified per-URL CWV ledger that pairs with the three per-URL rankings (psi_slowest_lcp_urls,psi_worst_inp_urls,psi_worst_cls_urls) to surface “which URLs pass everything” vs “which URLs need work”. Critical for pre-launch readiness gating: a URL passing all three CWVs is launch-ready; failing any one is not.
| What it counts | List of audited URLs with: pass/fail status for each CWV individually, the all-three composite (pass only if all three pass), traffic share, and the dragging metric(s) for failed URLs. |
| Sample type | Lab data primarily (synthetic per-URL audits); field data overlay where CrUX URL-level is available for high-traffic URLs. |
| The “all-three” gate | A URL must satisfy: LCP ≤ 2500ms AND INP ≤ 200ms AND CLS ≤ 0.1. Failing any one fails the gate; partial passes don’t count. This matches Google’s CWV pass-rate calculation, so the per-URL view aligns with the origin-level psi_cwv_pass_rate headline. |
| Why all-three matters commercially | Google’s CWV ranking signal is binary at the URL level: pass or fail. A page passing 2 of 3 CWVs gets the same ranking treatment as a page passing 0 of 3 (failure). The dichotomy means: brands optimising should target the worst-failing CWV first to flip the URL from fail to pass; partial improvements that don’t cross the threshold deliver no ranking benefit. |
| The pre-launch gate | For BC merchants approaching go-live, this card answers “which URLs are ready and which aren’t?” Recommended pre-launch threshold: 80 percent of audited URLs passing all three CWVs, with no high-traffic URL (>10 percent traffic share) failing. Brands launching below this threshold typically face customer complaints within 7-14 days of launch and ranking degradation within 30-60 days. |
| Currency | n/a, list with pass/fail flags + per-CWV values. |
| Time window | T (current state); 28D for field-data validation. |
| Alert trigger | > 30 percent of URLs failing all-three (red) OR any URL with > 10 percent traffic share failing (red regardless of overall percentage). |
| Sentiment key | psi_cwv_pass |
| Roles | owner, marketing, operations |
Calculation
Calculated automatically from your Website Performance (PageSpeed + CrUX) data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
A UK-based BigCommerce fashion store, 30-URL audit, mobile, Wednesday 15 May 26.| URL | LCP | INP | CLS | All-3 | Traffic | Dragging metric |
|---|---|---|---|---|---|---|
/ (homepage) | 4,820ms ❌ | 380ms ❌ | 0.21 ❌ | ❌ | 22% | All three (urgent) |
/products/spring-floral-maxi-dress | 5,820ms ❌ | 220ms ❌ | 0.18 ❌ | ❌ | 8.4% | All three (urgent) |
/products/leather-tote-bag | 4,480ms ❌ | 240ms ❌ | 0.20 ❌ | ❌ | 6.8% | All three |
/collections/new-arrivals | 4,610ms ❌ | 480ms ❌ | 0.24 ❌ | ❌ | 12.0% | All three |
/collections/sale | 4,180ms ❌ | 460ms ❌ | 0.22 ❌ | ❌ | 7.8% | All three |
/collections/dresses | 3,820ms ❌ | 420ms ❌ | 0.16 ❌ | ❌ | 9.2% | All three |
/cart | 2,180ms ✅ | 220ms ❌ | 0.04 ✅ | ❌ | 8.0% | INP only |
/checkout | 1,940ms ✅ | 180ms ✅ | 0.04 ✅ | ✅ | 4.0% | Passes all |
/about | 3,420ms ❌ | 180ms ✅ | 0.32 ❌ | ❌ | 0.8% | LCP + CLS |
/contact | 2,380ms ✅ | 160ms ✅ | 0.28 ❌ | ❌ | 0.5% | CLS only |
| … (20 more URLs analysed) | ||||||
| Summary | ||||||
| URLs passing all 3 | 3 of 30 (10%) | |||||
| Top-traffic URLs failing | 6 of top 6 fail | |||||
| Pre-launch readiness | NOT READY |
- Only 10 percent of audited URLs pass all three CWVs. This is below the 80 percent pre-launch threshold by a wide margin. The site is not ready to ship: at this state, post-launch traffic will hit a site Google’s algorithm classifies as failing CWV across most surfaces.
- All 6 of the top-traffic URLs fail. The homepage, top-2 PDPs, and top-3 collection pages all fail all-three CWV. Top-traffic failures are more damaging than tail failures: 22 percent of mobile traffic hits the failing homepage; 8 percent hits the failing PDP; failures here directly cost ranking and conversion.
- Cart and checkout passes/near-passes are the silver lining. Checkout passes all three (✅); cart fails only on INP (single dragging metric). These pages are positioned for healthy CWV; the merchant should not destabilise them while fixing other pages.
- About and Contact (low traffic) fail differently. About has bad LCP + CLS (banner image without aspect-ratio + cookie banner shift); Contact has only CLS failure (cookie banner). A single global cookie-banner repositioning fix passes both pages, taking the all-three pass count from 3/30 → 5/30 in 30 minutes of work.
-
Pattern recognition: 6 of 6 top-traffic URLs fail all three. The same dominant causes (hero image weight, filter widget cost, missing aspect-ratio CSS) appear across all of them. One template-level optimisation cycle (covered in
psi_image_optimisation,psi_render_blocking,psi_worst_cls_urls,psi_worst_inp_urls) can flip many URLs simultaneously. - Estimated post-fix-cycle pass rate: implementing the four-week optimisation sequence detailed across the per-URL ranking cards lifts the all-three pass rate from 10 percent → 70-85 percent. Crosses the 80 percent pre-launch threshold; site becomes launch-ready.
- Commercial impact estimation: at current 10 percent pass rate, the site is leaking ~1-3 ranking positions on competitive queries (CWV penalty band) and ~10-20 percent of mobile sessions are bouncing due to performance perception. At post-fix 80 percent pass rate, both penalties largely disappear; estimated organic traffic recovery 8-15 percent over 60-90 days.
- Categorise URLs by failure pattern. All-three-failing URLs have systemic issues; single-CWV-failing URLs have specific issues with cleaner fixes.
- Concentrate on top-traffic failing URLs first. Per-URL passing rate matters less than weighted-traffic passing rate.
- Apply template-level fixes when patterns repeat. The same dominant cause across multiple URLs implies template work, not per-URL work.
- Re-audit after each fix-cycle. Confirm URLs flip from fail to pass; track the 80 percent threshold.
- For pre-launch, gate the launch on the threshold. Don’t launch with under 80 percent pass rate plus no top-traffic URL failing.
| Time horizon | Action |
|---|---|
| First 1 hour after audit | Identify top-traffic failing URLs; categorise their failure patterns. |
| First week | Apply template-level fixes for the dominant patterns. |
| Day 28 | Field-data CWV pass rate fully reflects fixes. Re-evaluate launch readiness. |
| Day 60+ | Organic search ranking recovery shows in GSC reports. |
Sibling cards merchants should reference together
| Card | Why merchants reach for it |
|---|---|
psi_cwv_pass_rate | Origin-level all-three pass rate; this card decomposes per URL. |
psi_slowest_lcp_urls | Per-URL LCP ranking; drill-down for LCP-failing URLs. |
psi_worst_inp_urls | Per-URL INP ranking. |
psi_worst_cls_urls | Per-URL CLS ranking. |
psi_biggest_regression | URLs whose CWV pass status flipped recently. |
crux_lcp_p75 | Origin-level LCP. |
crux_inp_p75 | Origin-level INP. |
crux_cls_p75 | Origin-level CLS. |
psi_perf_score_summary | Lab composite; correlates with field CWV pass rate. |
GSC gsc_mobile_usable_pages | Mobile usability is a separate Google ranking signal that pairs with CWV. |
Reconciling against the vendor’s own dashboard
Where to look:- Google Search Console → Core Web Vitals, the canonical view for which URL groups Google considers passing or failing CWV.
- PageSpeed Insights, per-URL pass/fail at the field-data level for high-traffic URLs.
- CrUX URL-level dataset, per-URL CWV measurements for high-traffic URLs.
| Reason | Direction | What to do |
|---|---|---|
| Lab vs field. Vortex IQ uses lab measurements per URL; GSC uses field-data CrUX. | Field is truth source for ranking | Use GSC for ranking-impact decisions; this card for engineering iteration. |
| URL granularity. GSC groups URLs by similarity; Vortex IQ shows per-URL. | Different aggregation | The grouped view is faster to scan; the per-URL view is more actionable. |
| Window timing. Vortex IQ refreshes daily; GSC uses 28-day rolling CrUX. | Vortex IQ leads GSC | Use Vortex IQ for early signal; GSC for confirmation. |
psi_cwv_pass_rate, crux_lcp_p75, crux_inp_p75, crux_cls_p75).
Quick rule for support tickets: if a merchant says “my GSC report shows my pages passing CWV but your card shows them failing”, the most common cause is lab-vs-field divergence. Real users on real devices may experience worse CWV than lab emulation, OR the lab profile may be stricter than the average real-user case. The truth source for ranking is GSC; the truth source for engineering iteration is this card.
Known limitations / merchant FAQs
Only 3 of 30 URLs pass all three CWV. Is that bad? Yes; the site is in the failing band. Google’s CWV ranking signal kicks in below 75 percent origin pass rate; at 10 percent (3/30), the site is well into the failing zone and paying ranking penalty on competitive queries. Recommended action: focused 4-week optimisation cycle to lift to 75-85 percent before considering the site ranking-stable. My checkout passes everything but my homepage fails. Should I worry? Yes. Homepage failures are more commercially damaging than checkout failures because: (1) homepage handles brand search and direct visit traffic; (2) checkout traffic is captive (already converting); (3) Google weights homepage CWV heavily for site-level signal. Fix the homepage first; checkout’s healthy state should be protected from regression but doesn’t need optimisation work. What threshold should I use as a launch gate? 80 percent of audited URLs passing all-three, with no top-traffic URL (>10 percent share) failing. Brands launching below this typically face customer complaints + ranking degradation within 30-60 days. Brands at 90+ percent launch confidently; brands at 70-79 percent have risk but can manage with active monitoring; brands below 70 percent should not launch. Why does the same URL show different pass status in GSC vs Vortex IQ? Lab-vs-field divergence. GSC shows field data (real users); Vortex IQ shows lab data (synthetic emulation). Real users may experience better OR worse CWV than emulation depending on device + network mix. For ranking decisions, trust GSC; for engineering iteration, trust this card. Can I get to 100 percent passing? Theoretically yes; commercially diminishing returns. Brands at 85-95 percent typically operate productively. Pushing the last 5 percent often requires architectural-level work (server-side rendering, edge functions, custom CDN) that exceeds the marginal benefit. My pass rate is 75 percent. Is that good enough? Just barely. 75 percent is the threshold where Google’s CWV ranking signal flips from penalty to neutral. Operating at exactly 75 percent is risky: a single bad deploy can drop you below threshold and back into penalty. Build a 5-10 percent cushion: target 80-85 percent operating state. Can Vortex IQ tell me how to flip a specific URL from failing to passing? Indirectly. The card identifies which CWV(s) are dragging each failing URL; the per-CWV cards (crux_lcp_p75, etc.) and per-URL ranking cards explain the fix. The Vortex Mind Pre-Launch Readiness report assembles these into a per-URL action plan.
Why does Google care so much about CWV?
User experience research. Google’s data shows CWV failures correlate with bounce rate, low engagement, and reduced search satisfaction. CWV is a proxy for user experience quality, weighted into ranking because Google wants to show users to fast, stable, responsive pages. Failing CWV signals “this page provides a degraded experience”; ranking signal reflects that.