1
At a glance the headline numbers for E-commerce impact
E-commerce providers and their performance.
12
Providers ranked
After min-sites filter
40,405
Sites in sample
Combined across all items
0.06
Best CLS (p75)
Shop Pay
The State of Web Vitals · Q2 2026 · 189,915 sites · desktop field datacorewebvitals.io/state-of-cwv
2
The ranking sorted by CLS at p75, fastest first
| # | Provider | CLS p75 | Passing | Sites | |
|---|---|---|---|---|---|
| 1 | Shop Pay | 0.06 | 85.2% | 7,922 | |
| 2 | Shopify | 0.06 | 85.1% | 7,965 | |
| 3 | OpenCart | 0.07 | 84.8% | 231 | |
| 4 | Shopware | 0.08 | 81.1% | 5,514 | |
| 5 | PrestaShop | 0.09 | 78.0% | 1,221 | |
| 6 | Klarna | 0.10 | 77.6% | 491 | |
| 7 | PayPal | 0.10 | 76.3% | 2,306 | |
| 8 | WooCommerce | 0.10 | 75.9% | 12,482 | |
| 9 | Stripe | 0.12 | 70.4% | 1,844 | |
| 10 | BigCommerce | 0.13 | 65.9% | 124 | |
| 11 | Afterpay/Affirm | 0.18 | 61.2% | 80 | |
| 12 | Ecwid | 0.24 | 55.6% | 225 |
The State of Web Vitals · Q2 2026 · 189,915 sites · desktop field datacorewebvitals.io/state-of-cwv
3
Passing CLS per provider which group passes the CLS most often
ProviderSitesPassing CLSp75
Snipcart 0%
23
0.03
Salesforce commerce 0%
4
0.04
Shop Pay 4.2%
7,922
0.06
Shopify 4.2%
7,965
0.06
OpenCart 0.1%
231
0.07
Shopware 2.9%
5,514
0.08
PrestaShop 0.6%
1,221
0.09
Klarna 0.3%
491
0.10
PayPal 1.2%
2,306
0.10
WooCommerce 6.6%
12,482
0.10
Stripe 1%
1,844
0.12
BigCommerce 0.1%
124
0.13
Afterpay/Affirm 0%
80
0.18
Volusion 0%
17
0.18
Ecwid 0.1%
225
0.24
Good
Needs Improvement
Poor
Sorted best-passing first · median colored by its own rating · pass = good CLS (0.1 at p75) · one value per site
85% of Shop Pay sites pass CLS. Ecwid trails 29 points behind, leaving 44% of its sites failing. computed
The State of Web Vitals · Q2 2026 · 189,915 sites · desktop field datacorewebvitals.io/state-of-cwv
4
All five vitals at once the whole category without toggling - cell is the pass rate, small number the p75
LCP
INP
CLS
FCP
TTFB
Shopware 2.9%
911.8s
9963ms
810.08
871.4s
641.0s
Stripe 1%
762.5s
9880ms
700.12
721.9s
541.3s
PrestaShop 0.6%
872.1s
9957ms
780.09
771.7s
361.4s
Shopify 4.2%
951.7s
9978ms
850.06
951.1s
95481ms
Afterpay/Affirm 0%
772.3s
93103ms
610.18
801.5s
77695ms
Snipcart 0%
751.6s
10084ms
1000.03
751.3s
501.8s
Volusion 0%
782.3s
10047ms
560.18
781.7s
63993ms
Shop Pay 4.2%
951.7s
9978ms
850.06
951.1s
95480ms
WooCommerce 6.6%
593.2s
10061ms
760.10
492.7s
152.4s
PayPal 1.2%
862.0s
10066ms
760.10
831.6s
561.2s
Ecwid 0.1%
613.2s
95105ms
560.24
761.7s
441.6s
Klarna 0.3%
881.9s
9887ms
780.10
861.4s
75804ms
BigCommerce 0.1%
941.9s
10077ms
660.13
921.4s
87676ms
OpenCart 0.1%
911.9s
10050ms
850.07
811.6s
541.1s
Salesforce commerce 0%
752.4s
100112ms
1000.04
751.6s
75728ms
60%95%+ passing
Cell: pass rate, small number = p75 · faded rows: under 100 sites
One row per provider, one column per vital - the cell is the share of sites passing, the small number the p75. No toggling needed to see where the category actually differs.
Shopify leads on CLS: 85% of its sites pass. Ecwid trails at 56%. computed
The State of Web Vitals · Q2 2026 · 189,915 sites · desktop field datacorewebvitals.io/state-of-cwv
Related signals
Personalization (6) →
Marketing pixels (10) →
RUM (14) →
Scheduling (3) →
Chrome field data from 189,915 sites, representing millions of real page loads · How we measured
Chrome field data from 189,915 sites, representing millions of real page loads. How we measured.
Live queries (3) — admin only
Query #1:
30.7 ms
SELECT 1 AS ok FROM site_providers LIMIT 0;
Query #2:
408.0 ms
SELECT CAST(p.key AS VARCHAR) AS e, COUNT(*) AS sites,
COUNT(*) FILTER (WHERE s.crux."all".lcp IS NOT NULL) AS all_lcp_n,
quantile_disc(s.crux."all".lcp, 0.5) AS all_lcp_median,
quantile_disc(s.crux."all".lcp, 0.75) AS all_lcp_p75,
COUNT(*) FILTER (WHERE s.crux."all".lcp <= 2500) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".lcp IS NOT NULL), 0) AS all_lcp_good,
COUNT(*) FILTER (WHERE s.crux."all".lcp > 2500 AND s.crux."all".lcp <= 4000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".lcp IS NOT NULL), 0) AS all_lcp_ni,
COUNT(*) FILTER (WHERE s.crux."all".lcp > 4000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".lcp IS NOT NULL), 0) AS all_lcp_poor,
COUNT(*) FILTER (WHERE s.crux."all".inp IS NOT NULL) AS all_inp_n,
quantile_disc(s.crux."all".inp, 0.5) AS all_inp_median,
quantile_disc(s.crux."all".inp, 0.75) AS all_inp_p75,
COUNT(*) FILTER (WHERE s.crux."all".inp <= 200) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".inp IS NOT NULL), 0) AS all_inp_good,
COUNT(*) FILTER (WHERE s.crux."all".inp > 200 AND s.crux."all".inp <= 500) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".inp IS NOT NULL), 0) AS all_inp_ni,
COUNT(*) FILTER (WHERE s.crux."all".inp > 500) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".inp IS NOT NULL), 0) AS all_inp_poor,
COUNT(*) FILTER (WHERE s.crux."all".cls IS NOT NULL) AS all_cls_n,
quantile_disc(s.crux."all".cls, 0.5) AS all_cls_median,
quantile_disc(s.crux."all".cls, 0.75) AS all_cls_p75,
COUNT(*) FILTER (WHERE s.crux."all".cls <= 0.1) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".cls IS NOT NULL), 0) AS all_cls_good,
COUNT(*) FILTER (WHERE s.crux."all".cls > 0.1 AND s.crux."all".cls <= 0.25) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".cls IS NOT NULL), 0) AS all_cls_ni,
COUNT(*) FILTER (WHERE s.crux."all".cls > 0.25) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".cls IS NOT NULL), 0) AS all_cls_poor,
COUNT(*) FILTER (WHERE s.crux."all".fcp IS NOT NULL) AS all_fcp_n,
quantile_disc(s.crux."all".fcp, 0.5) AS all_fcp_median,
quantile_disc(s.crux."all".fcp, 0.75) AS all_fcp_p75,
COUNT(*) FILTER (WHERE s.crux."all".fcp <= 1800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".fcp IS NOT NULL), 0) AS all_fcp_good,
COUNT(*) FILTER (WHERE s.crux."all".fcp > 1800 AND s.crux."all".fcp <= 3000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".fcp IS NOT NULL), 0) AS all_fcp_ni,
COUNT(*) FILTER (WHERE s.crux."all".fcp > 3000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".fcp IS NOT NULL), 0) AS all_fcp_poor,
COUNT(*) FILTER (WHERE s.crux."all".ttfb IS NOT NULL) AS all_ttfb_n,
quantile_disc(s.crux."all".ttfb, 0.5) AS all_ttfb_median,
quantile_disc(s.crux."all".ttfb, 0.75) AS all_ttfb_p75,
COUNT(*) FILTER (WHERE s.crux."all".ttfb <= 800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".ttfb IS NOT NULL), 0) AS all_ttfb_good,
COUNT(*) FILTER (WHERE s.crux."all".ttfb > 800 AND s.crux."all".ttfb <= 1800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".ttfb IS NOT NULL), 0) AS all_ttfb_ni,
COUNT(*) FILTER (WHERE s.crux."all".ttfb > 1800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."all".ttfb IS NOT NULL), 0) AS all_ttfb_poor,
COUNT(*) FILTER (WHERE s.crux."phone".lcp IS NOT NULL) AS phone_lcp_n,
quantile_disc(s.crux."phone".lcp, 0.5) AS phone_lcp_median,
quantile_disc(s.crux."phone".lcp, 0.75) AS phone_lcp_p75,
COUNT(*) FILTER (WHERE s.crux."phone".lcp <= 2500) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".lcp IS NOT NULL), 0) AS phone_lcp_good,
COUNT(*) FILTER (WHERE s.crux."phone".lcp > 2500 AND s.crux."phone".lcp <= 4000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".lcp IS NOT NULL), 0) AS phone_lcp_ni,
COUNT(*) FILTER (WHERE s.crux."phone".lcp > 4000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".lcp IS NOT NULL), 0) AS phone_lcp_poor,
COUNT(*) FILTER (WHERE s.crux."phone".inp IS NOT NULL) AS phone_inp_n,
quantile_disc(s.crux."phone".inp, 0.5) AS phone_inp_median,
quantile_disc(s.crux."phone".inp, 0.75) AS phone_inp_p75,
COUNT(*) FILTER (WHERE s.crux."phone".inp <= 200) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".inp IS NOT NULL), 0) AS phone_inp_good,
COUNT(*) FILTER (WHERE s.crux."phone".inp > 200 AND s.crux."phone".inp <= 500) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".inp IS NOT NULL), 0) AS phone_inp_ni,
COUNT(*) FILTER (WHERE s.crux."phone".inp > 500) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".inp IS NOT NULL), 0) AS phone_inp_poor,
COUNT(*) FILTER (WHERE s.crux."phone".cls IS NOT NULL) AS phone_cls_n,
quantile_disc(s.crux."phone".cls, 0.5) AS phone_cls_median,
quantile_disc(s.crux."phone".cls, 0.75) AS phone_cls_p75,
COUNT(*) FILTER (WHERE s.crux."phone".cls <= 0.1) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".cls IS NOT NULL), 0) AS phone_cls_good,
COUNT(*) FILTER (WHERE s.crux."phone".cls > 0.1 AND s.crux."phone".cls <= 0.25) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".cls IS NOT NULL), 0) AS phone_cls_ni,
COUNT(*) FILTER (WHERE s.crux."phone".cls > 0.25) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".cls IS NOT NULL), 0) AS phone_cls_poor,
COUNT(*) FILTER (WHERE s.crux."phone".fcp IS NOT NULL) AS phone_fcp_n,
quantile_disc(s.crux."phone".fcp, 0.5) AS phone_fcp_median,
quantile_disc(s.crux."phone".fcp, 0.75) AS phone_fcp_p75,
COUNT(*) FILTER (WHERE s.crux."phone".fcp <= 1800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".fcp IS NOT NULL), 0) AS phone_fcp_good,
COUNT(*) FILTER (WHERE s.crux."phone".fcp > 1800 AND s.crux."phone".fcp <= 3000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".fcp IS NOT NULL), 0) AS phone_fcp_ni,
COUNT(*) FILTER (WHERE s.crux."phone".fcp > 3000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".fcp IS NOT NULL), 0) AS phone_fcp_poor,
COUNT(*) FILTER (WHERE s.crux."phone".ttfb IS NOT NULL) AS phone_ttfb_n,
quantile_disc(s.crux."phone".ttfb, 0.5) AS phone_ttfb_median,
quantile_disc(s.crux."phone".ttfb, 0.75) AS phone_ttfb_p75,
COUNT(*) FILTER (WHERE s.crux."phone".ttfb <= 800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".ttfb IS NOT NULL), 0) AS phone_ttfb_good,
COUNT(*) FILTER (WHERE s.crux."phone".ttfb > 800 AND s.crux."phone".ttfb <= 1800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".ttfb IS NOT NULL), 0) AS phone_ttfb_ni,
COUNT(*) FILTER (WHERE s.crux."phone".ttfb > 1800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."phone".ttfb IS NOT NULL), 0) AS phone_ttfb_poor,
COUNT(*) FILTER (WHERE s.crux."desktop".lcp IS NOT NULL) AS desktop_lcp_n,
quantile_disc(s.crux."desktop".lcp, 0.5) AS desktop_lcp_median,
quantile_disc(s.crux."desktop".lcp, 0.75) AS desktop_lcp_p75,
COUNT(*) FILTER (WHERE s.crux."desktop".lcp <= 2500) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".lcp IS NOT NULL), 0) AS desktop_lcp_good,
COUNT(*) FILTER (WHERE s.crux."desktop".lcp > 2500 AND s.crux."desktop".lcp <= 4000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".lcp IS NOT NULL), 0) AS desktop_lcp_ni,
COUNT(*) FILTER (WHERE s.crux."desktop".lcp > 4000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".lcp IS NOT NULL), 0) AS desktop_lcp_poor,
COUNT(*) FILTER (WHERE s.crux."desktop".inp IS NOT NULL) AS desktop_inp_n,
quantile_disc(s.crux."desktop".inp, 0.5) AS desktop_inp_median,
quantile_disc(s.crux."desktop".inp, 0.75) AS desktop_inp_p75,
COUNT(*) FILTER (WHERE s.crux."desktop".inp <= 200) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".inp IS NOT NULL), 0) AS desktop_inp_good,
COUNT(*) FILTER (WHERE s.crux."desktop".inp > 200 AND s.crux."desktop".inp <= 500) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".inp IS NOT NULL), 0) AS desktop_inp_ni,
COUNT(*) FILTER (WHERE s.crux."desktop".inp > 500) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".inp IS NOT NULL), 0) AS desktop_inp_poor,
COUNT(*) FILTER (WHERE s.crux."desktop".cls IS NOT NULL) AS desktop_cls_n,
quantile_disc(s.crux."desktop".cls, 0.5) AS desktop_cls_median,
quantile_disc(s.crux."desktop".cls, 0.75) AS desktop_cls_p75,
COUNT(*) FILTER (WHERE s.crux."desktop".cls <= 0.1) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".cls IS NOT NULL), 0) AS desktop_cls_good,
COUNT(*) FILTER (WHERE s.crux."desktop".cls > 0.1 AND s.crux."desktop".cls <= 0.25) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".cls IS NOT NULL), 0) AS desktop_cls_ni,
COUNT(*) FILTER (WHERE s.crux."desktop".cls > 0.25) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".cls IS NOT NULL), 0) AS desktop_cls_poor,
COUNT(*) FILTER (WHERE s.crux."desktop".fcp IS NOT NULL) AS desktop_fcp_n,
quantile_disc(s.crux."desktop".fcp, 0.5) AS desktop_fcp_median,
quantile_disc(s.crux."desktop".fcp, 0.75) AS desktop_fcp_p75,
COUNT(*) FILTER (WHERE s.crux."desktop".fcp <= 1800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".fcp IS NOT NULL), 0) AS desktop_fcp_good,
COUNT(*) FILTER (WHERE s.crux."desktop".fcp > 1800 AND s.crux."desktop".fcp <= 3000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".fcp IS NOT NULL), 0) AS desktop_fcp_ni,
COUNT(*) FILTER (WHERE s.crux."desktop".fcp > 3000) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".fcp IS NOT NULL), 0) AS desktop_fcp_poor,
COUNT(*) FILTER (WHERE s.crux."desktop".ttfb IS NOT NULL) AS desktop_ttfb_n,
quantile_disc(s.crux."desktop".ttfb, 0.5) AS desktop_ttfb_median,
quantile_disc(s.crux."desktop".ttfb, 0.75) AS desktop_ttfb_p75,
COUNT(*) FILTER (WHERE s.crux."desktop".ttfb <= 800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".ttfb IS NOT NULL), 0) AS desktop_ttfb_good,
COUNT(*) FILTER (WHERE s.crux."desktop".ttfb > 800 AND s.crux."desktop".ttfb <= 1800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".ttfb IS NOT NULL), 0) AS desktop_ttfb_ni,
COUNT(*) FILTER (WHERE s.crux."desktop".ttfb > 1800) * 1.0 / NULLIF(COUNT(*) FILTER (WHERE s.crux."desktop".ttfb IS NOT NULL), 0) AS desktop_ttfb_poor
FROM site_providers p
JOIN sites s ON s.origin = p.origin
WHERE p.category = 'ecommerce'
AND p.key IS NOT NULL AND CAST(p.key AS VARCHAR) <> ''
GROUP BY p.key;
Query #3:
29.7 ms
SELECT COUNT(*) AS n FROM sites;
JSON file lookups (5)
20260625/meta.json
0.03 ms
20260625/types.json
0.44 ms
20260625/loaf-scripts.json
0.28 ms
20260625/menu.json
0.10 ms
20260625/providers.json
3.47 ms