At a glance
A cross-channel table that joins the product-detail pages Criteo dynamic ads send shoppers to against your web-vitals measurement, and lists every landing page that is both slow (poor Largest Contentful Paint and related vitals) and converting below your account average. Criteo’s whole purpose is to bring a warm, high-intent shopper back to a specific product page. If that page loads slowly, the retargeting cycle pays to deliver the click and then loses the shopper to a sluggish load before they can buy. This card finds the leak at the point it actually happens, the destination page, rather than blaming the campaign. It is where ad performance and site performance meet, and it only exists because Vortex IQ sees both the Criteo serving layer and your web-vitals data.
| What it counts | The count and list of distinct landing pages targeted by Criteo dynamic ads that exhibit poor web vitals (slow LCP and related Core Web Vitals) and a conversion rate materially below the account average. The value is the number of offending pages; the table details each page, its vitals reading, and its conversion gap. |
| Cost basis | CPC-dominant on the Criteo side. The wasted cost is the CPC paid to deliver a click to a page that then loses the shopper to slow load. |
| Currency | None for the headline count. The wasted-spend implication is in advertiser-account currency when the poor pages are weighted by their click cost. |
| Conversion attribution | 30D click + 7D view default on Criteo. The conversion-rate comparison uses Criteo-attributed conversions per landing page against the account average. |
| Attribution window | 30D click + 7D view default. |
| Bot / invalid traffic | Excluded from the conversion-rate side where filtering identifies it; vitals are measured on real sessions. |
| iOS 14.5+ ATT impact on the card | Low to moderate. Vitals are device-measured and unaffected by ATT; the conversion-rate side is attribution-sensitive, so weigh the conversion gap with that in mind on iOS-heavy pages. |
| Catalogue-feed dependency | Indirect. The feed decides which PDPs get traffic; this card judges how well those PDPs perform once the shopper arrives. |
| Time window | 30D (rolling 30 days). A monthly read smooths daily vitals variance and gives a stable conversion-rate comparison. |
| Alert trigger | any landing with LCP >4s AND CR <0.5x account-avg. An illustrative rule; a page is flagged when its load is slow and its conversion rate is well under half the account average, the combination that signals the page itself is the leak. |
| Roles | owner, marketing, engineering |
Calculation
Calculated automatically by joining your Criteo landing-page data with your connected web-vitals measurement. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
A US outdoor-gear DTC retailer runs Criteo dynamic retargeting that sends shoppers to product-detail pages. A recent PDP template change added a heavy hero-video block that slowed load on several pages. The join lists Criteo landing pages by vitals and conversion rate against the account average. Window is the rolling 30 days to 19 Jun 26. Account average conversion rate on Criteo PDPs is 3.0%.| Landing page | LCP | CLS | Criteo clicks | Conv. rate | vs account avg | State |
|---|---|---|---|---|---|---|
| /tents/4-season-expedition | 5.8s | 0.21 | 4,200 | 1.1% | 0.37x | FLAG |
| /packs/65l-trekking | 5.1s | 0.18 | 3,100 | 1.3% | 0.43x | FLAG |
| /boots/alpine-gtx | 2.1s | 0.04 | 2,800 | 3.2% | 1.07x | OK |
| /jackets/hardshell-pro | 4.6s | 0.16 | 1,900 | 1.4% | 0.47x | FLAG |
- Three PDPs are both slow and converting at well under half the account average. These are the leaks. Criteo delivered warm, high-intent clicks to them, and the pages lost those shoppers before they could buy. The campaign is doing its job; the destination is failing.
- The healthy boot page proves it is the page, not the audience. The /boots/alpine-gtx page loads in 2.1s and converts slightly above account average on the same kind of retargeting traffic. Same audience, same platform, good page, normal conversion. That contrast localises the fault to the slow pages.
- The flagged pages share the new heavy template. All three slow pages use the PDP template with the hero-video block; the fast page predates it. That common factor points the engineering fix at the template, not at individual products.
- The wasted cost is concentrated on the highest-traffic slow page. The 4-season tent page took 4,200 retargeting clicks at a 1.1% conversion rate; a page converting at account average would have produced far more sales from the same spend. Prioritise the fix by clicks-times-conversion-gap, not by which page is slowest in isolation.
- The fix lives in engineering, not in the ad account. Defer or lazy-load the hero video, compress and correctly size hero images, reserve layout space to cut the CLS shift, and re-measure. As LCP improves toward the good band, conversion rate on retargeting traffic should climb back toward the account average.
- Re-measure after the fix before touching budget. Do not pull spend off these products; fix the page and let the recovered conversion rate restore the ROAS. Pausing the campaign would forfeit warm demand that the page fix recaptures.
- Page slow + conversion well below average = genuine destination leak, fix the page.
- Page slow + conversion near average = the speed is tolerable for that product; deprioritise.
- Page fast + conversion below average = not a vitals problem; look at price, stock, or offer.
- Several flagged pages share a template = template-level fix, highest leverage.
- Conversion recovers after a vitals fix = confirmed the page was the cause.
Sibling cards merchants should reference together
| Card | Why it matters next to Landing Pages with Poor Web Vitals | What the combination tells you |
|---|---|---|
| Landing Page Performance | The per-landing-page performance breakdown. | This card adds the web-vitals dimension that explains why a landing page underperforms. |
| Landing Page Conv. Rate | The conversion-rate side of the join. | A low landing-page conversion rate paired with poor vitals confirms speed is the likely cause. |
| Landing Page CPC | The cost paid to reach each page. | High CPC into a slow, low-converting page is the most expensive version of this leak. |
| Landing Page Revenue | The revenue each page produces. | Sizes the opportunity: fixing a high-traffic slow page recovers the most revenue. |
| Conversion Rate Trend | Account-level conversion direction. | A dip aligned to a PDP template change points at vitals regression as the cause. |
| ROAS | The efficiency headline. | Slow landing pages silently depress ROAS; fixing them is a site-side ROAS lever. |
Reconciling against Criteo
Where to look in Criteo’s own dashboard:Criteo Management Centre → Reporting → Performance Report for clicks and conversions by landing page or product, and a web-vitals source (your real-user monitoring, Google PageSpeed Insights, or a CrUX-based report) for the LCP and CLS readings on those same URLs.Criteo cannot produce this card on its own. Criteo sees the clicks it delivered and the conversions it attributed per landing page, but it has no idea how fast those pages load, that lives in your web-vitals measurement. The cross-channel join of Criteo landing-page performance against web-vitals data is what Vortex IQ assembles. To reconcile manually, export Criteo’s per-landing-page clicks and conversion rate, pull the LCP and CLS for the same URLs from your vitals source, and the pages that are both slow and below your account-average conversion rate are the ones this card flags. Why our number may legitimately differ from a manual check:
| Reason | Direction | Why |
|---|---|---|
| Field vs lab vitals | Either direction | Real-user (field) vitals and lab-tool readings can diverge; a page may pass in a lab test yet read poorly for real shoppers on slower devices and networks. |
| Device and network mix | Ours reflects real traffic | Vitals vary by device and connection; a page that is fine on desktop can be poor on the mobile traffic Criteo actually sends. |
| Attribution on the conversion side | Conversion gap can shift | The conversion-rate comparison uses Criteo-attributed conversions, which iOS ATT can understate on iOS-heavy pages. |
| URL normalisation | Ours may group variants | Query-string and variant URLs for the same product are grouped to the canonical landing page; a manual check that keeps them separate will count differently. |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
website.lcp / website.cls | The web-vitals signal is the speed side of the join; the same poor LCP and CLS that hurt SEO and direct traffic also leak Criteo-paid clicks. | Site-wide vitals can look acceptable while specific PDPs that Criteo targets are poor, which is why a landing-page-level join matters. |
shopify.total_revenue / bigcommerce.total_revenue | Sizes the recoverable revenue from fixing the flagged pages. | The revenue upside depends on each page’s traffic volume and product margin, not on how slow it is alone. |