The 2025 platform comparison
The chart reproduces the Web Almanac's good overall Core Web Vitals rates for five widely detected ecommerce platforms. Shopify is at 76% on both mobile and desktop in this sample. WooCommerce is at 35% on mobile and 33% on desktop. Wix eCommerce, Squarespace Commerce, and PrestaShop sit between them.
These percentages are shares of eligible origins classified under each platform and device. They are not the percentage of shoppers who had a good visit, and they are not conversion rates. A store's theme, apps, imagery, hosting, and custom code can move its result substantially.
| Detected platform | Mobile | Desktop |
|---|---|---|
| WooCommerce | 35% | 33% |
| Shopify | 76% | 76% |
| Wix eCommerce | 66% | 70% |
| Squarespace | 69% | 69% |
| PrestaShop | 50% | 54% |
Scale: 0–100%
Percent of detected origins with good overall Core Web Vitals in each platform's eligible mobile or desktop sample. ‘Squarespace’ abbreviates the source label ‘Squarespace Commerce’. Samples differ by platform and device.
Why the platform is not the whole explanation
The report detects ecommerce technology with Wappalyzer. It can miss a store in a subdirectory, a headless implementation, or a technology whose fingerprint is not visible on the crawled pages. The report also notes that detection changes and crawl geography can affect the comparison. These are observed groups of sites, not a controlled experiment assigning otherwise identical merchants to platforms.
The mobile samples are also different sizes: the chapter lists 995,782 WooCommerce origins and 567,932 Shopify origins. Size does not make one group's percentage wrong, but it reminds us that the groups contain different stores and likely different mixes of traffic, implementation choices, and business needs. Do not turn the chart into a promised outcome from migration.
Sources: 2025 Web Almanac: platform detection and Core Web Vitals tables
What to inspect on your own store
Start with mobile product, category, and checkout pages that receive meaningful traffic. Read their real-user LCP, INP, and CLS separately where page-level data exists. An origin-level pass can hide a weak checkout; a failed origin does not tell you which template needs work. Pair the field result with a browser trace and the site's conversion funnel.
For a hypothetical triage, suppose mobile product pages have an LCP of 3.4 seconds across 18,000 visits in a 28-day window, while checkout pages have an INP of 280 milliseconds across 1,200 checkout starts. Record those as two separate page groups with their own field-data coverage. The product-page LCP affects more visits; the checkout INP sits closer to purchase. Trace both templates, estimate effort, and choose the first fix with the team. Neither figure proves a conversion loss or predicts the return from a fix.
If LCP is weak, inspect the main image and its loading path. If INP is weak, check product filters, variant selectors, and third-party scripts. If CLS is weak, check image dimensions and late-loading promotions. Prioritize fixes by affected traffic and measured friction, then verify the same device and page group after changes.
Sources: web.dev: Core Web Vitals definitions and thresholds
How to use a benchmark responsibly
Put this external chart beside your site's own 28-day field data, clearly labeled by page or origin and device. Record the current theme, major apps, and measurement window. A comparison helps you ask whether your store is unusually slow and which metric explains the gap; it should not substitute for a diagnosis.
If a platform move is already under consideration, evaluate total cost, required commerce features, data migration, and operational fit alongside performance. Test representative pages on the proposed stack before treating the published platform average as evidence for a migration decision.