Why is CrUX data different from my RUM data?

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Web Development

The Chrome User Experience Report (CrUX) provides user experience metrics for how real-world Chrome users experience popular destinations on the web. This data is automatically collected by Chrome from users who have opted in, and is made available based on the CrUX eligibility criteria.

CrUX data is therefore available for millions of websites. Many site owners have not had access to field data before, and CrUX has enabled many sites to see the value of this for the first time. As a public dataset, CrUX can also be used for competitive analysis and benchmarking of user experience metrics.

Real User Monitoring (RUM) is similar to CrUX, but instead of Chrome automatically collecting user-experience metrics, code is included on the websites to do this collection and feed it back to a RUM provider or analytics solution for further analysis.

With both solutions measuring user experience metrics, it is natural to assume that they should be equivalent. It can be confusing when we see differences. This guide will explain why that can happen, and offers suggestions for what to do when the numbers do not align.

Benefits of supplementing CrUX with a RUM solution #

CrUX is a great tool for a consistent view across sites and—as the official dataset for the Core Web Vitals program—sites will likely want to keep an eye on what it is showing. The aim of CrUX is to provide a statistically relevant overview of millions of websites for cross comparison.

However, for a deeper dive in investigating why data is showing the numbers it is, investing in a full RUM solution to supplement CrUX can give you access to more detailed information than can be made available in a publicly queryable dataset. This can help you explain and improve your metrics in a number of ways.

Deeper analysis to investigate issues #

CrUX can often be used to point out if you have a problem on your site, but not necessarily exactly where on your site the issue lies nor why. RUM solutions—whether home-grown through the likes of the web-vitals library or some of the many commercial products—can help bridge that gap.

Using a RUM solution gives you access to much more fine-grained data for all your pages, and on all browsers. It also allows you to slice and dice this data in ways CrUX does not, enabling you to drill down and investigate problem areas of the site. Are they affected by a particular segment of users? Or users who take certain actions? Exactly when did the problem start? These are questions that are much easier to answer with the additional data that a RUM tool can provide.

Correlating with other business metrics #

RUM also allows you to compare your web performance metrics directly with any business metrics, showing the value in investing in performance, and what other performance work to prioritize. We have numerous case studies with businesses doing this correlation, such as Farfetch or The Economic Times.

Collecting other performance data #

A RUM solution allows collection of other custom metrics, tied directly to your specific business. One of the more well-known examples is Twitter’s “Time to first Tweet” metric. These site-specific measures can then be correlated with Core Web Vital improvements and business metrics.

Differences between two sets of field data #

A man with a watch knows what time it is. A man with two watches is never sure.

Segal’s Law

Whenever you have two sources of data, it can often be confusing and frustrating as to why they differ. In much the same way as it’s important to understand the difference between lab and field metrics, there can also be differences between two sources of field data. While the data would be the same in an ideal world, there are many reasons why they can differ.

Lab data versus field data #

The first thing to check is whether you are looking at lab (synthetic) metrics or field (RUM) metrics. While it’s natural to assume RUM products only look at field data, many offer a lab component as well.

Lab data is incredibly useful precisely because of the fixed conditions it measures under. It can be used to monitor unexpected changes or regressions in a production environment without the noise of a changing field population. However, lab data may not be representative of the real user experience, so field metrics can show quite different results.

Populations #

The datasets used by CrUX and RUM solutions may be different due to differences in which page visits are being measured depending on which browsers, users, sites, and devices are being compared.

Included browsers #

The Chrome User Experience Report, as its name suggests, is Chrome-only. While there are many Chromium-based browsers (Edge, Opera, and Brave to name a few) that also support the same metrics as Chrome given the shared core codebase, only Chrome users feed data into CrUX. This restriction also means Chrome users on iOS are not included, since it uses the underlying Webkit browser engine. Android WebViews also do not count as “Chrome”, so data from these users is not included—though Chrome Custom Tabs are included.

While Chrome is one of the world’s most popular browsers—and therefore would likely give a broad representation of your site’s performance in most cases—measuring just that browser is by no means a measure of all your users. This may explain one main difference between RUM and CrUX. This is particularly true for performance techniques that rely on APIs or image formats only available in Chrome, for example.

The lack of iOS data can also lead to bias. For example, as iOS users are typically on more performant devices or visiting from more countries with better network infrastructure, including them can lead to high overall performance metrics. On the other hand, excluding them—as CrUX does—can lead to data that is skewed to the lower end of site visitors (example case study). Android users typically cover a wider range of devices, device capabilities, and markets.

RUM solutions can get data for non-Chrome browsers, and in particular from Chromium-based browsers that often have the same metrics (such as Core Web Vitals) built in. Non-Chromium-based browsers are also measured by RUM solutions, but may have a more limited set of metrics. For example, Largest Contentful Paint (LCP) and Cumulative Layout Shift (CLS) are currently only available in Chromium-based browsers and some other metrics can be measured quite differently (see later).

Opted-in users #

As well as being limited to Chrome users, CrUX is further restricted by only measuring a subset of Chrome users who have opted in to share CrUX data when the browser was installed.

RUM providers also only look at a subset of users, usually due to cookie banner prompts—asking users to opt into RUM data collection—or tracking blockers. This can adversely affect some initial page loads if the confirmation is not given until the second or subsequent page, when some of the site assets have already been cached from previous pages. If this happens frequently, the metrics may appear more favorable in RUM than they actually are if slower initial page loads are excluded in a sufficient number of cases.

Included sites #

CrUX is only intended to report on public websites, so there are other eligibility criteria that may result in data not being logged in CrUX. Most notable of these criteria is that the website must be publicly discoverable and sufficiently popular to ensure a minimal sample size from which to draw meaningful conclusions. In most cases, this will result in no data being available in CrUX. This is less of a confusing difference compared to the data being available but different, but explains why that happens.

However, if specific pages of a site are marked as indexable but others are not, then you may only see a subset of URLs in CrUX. If the origin is publicly discoverable, then all page views within that origin will be included in the origin-level data, but URL-level data may not be available.

Devices #

CrUX segments data by mobile, desktop, and tablet – though many tools concentrate on the first two and may not surface tablet data, or may include it within mobile or desktop. The performance characteristics on mobile versus desktop can be quite different—both in terms of the content delivered, and in the capabilities of the devices viewing them.

RUM data will allow segmenting traffic similarly, but often shows consolidated data by default. RUM may only allow segmenting by device type (for example, mobile) or browser (for example, Chrome) easily, but not both to only see mobile Chrome traffic. When comparing to CrUX data, ensure you are comparing like-for-like by filtering by device type and the Chrome browser.

Sampling #

RUM solutions typically allow adjusting the sampling rate of opted-in visitors where data is collected. This can be used to reduce the volume of data needed to be analyzed, and to reduce costs of commercial RUM services. If that sample size is too small and not representative of the wider population, then the resulting metrics will also be similarly skewed. Discuss with your RUM provider the appropriate sampling size for your site.

Aggregation of data #

Field data by its very nature will include many, many data points of the same metrics compared to lab data, which will give a single value. If this data is aggregated differently for reporting, it can lead to another reason for differences between CrUX and RUM.

Timespan #

CrUX data is based on a 28 day sliding window of traffic, and it is not possible to change this timeframe—though the BigQuery data is stored for each month, allowing you to see previous months.

RUM data typically allows much greater granularity to allow seeing the impact of changes much sooner. When choosing smaller periods, though, RUM data can be unduly impacted by fluctuations in website traffic and visitors. When comparing RUM data to CrUX data, always ensure you are looking at performance over 28 days. Once you are satisfied the data is similar, you can look at other time frames to drill into the RUM data.

Aggregation of statistics #

CrUX metrics are measured at the 75th percentile—that is, looking at the value that 75% of page views achieved. There will be extremes in field data and removing the worst 25% experiences, it is intended to give a value that the majority of visitors can reasonably be expected to achieve.

RUM products often give a wider number of options of how to aggregate the metrics, including 75th percentile, median and other percentiles. If comparing RUM values to CrUX data, it is necessary to ensure you are looking at 75th percentile data to compare like-for-like.

The histogram data in CrUX includes all available data—not just 75th percentile—and shows the number of page views in each rating, but the aggregate score will be based on 75th percentile. This CrUX data is surfaced in tools like PageSpeed Insights:

PageSpeed Insights screenshot showing histograms of LCP rating page loads

Differences in metrics #

There are many metrics used to measure web performance, so when comparing two different sets of data, it’s important to understand what metrics are being measured, and how those metrics are being used.

Metrics measured #

CrUX data is the official dataset of the Core Web Vitals initiative and primarily measures these three metrics (LCP, FID, and CLS), with a few additional metrics to complement these.

RUM tools usually include these Core Web Vitals, but often include many other metrics too. Some RUM providers also measure the user experience using their own combination of all these metrics perhaps to give a happiness index or such. When comparing RUM data to CrUX, ensure you are comparing like-for-like.

Tools that assess Core Web Vitals pass/fail status should consider a page passing if it meets the recommended targets at the 75th percentile for all Core Web Vitals. If FID is not present for pages with no interactions, then only LCP and CLS need to pass.

Metric differences across browsers #

CrUX only measures in Chrome browsers, and you can refer to the Web Vitals Changelog to see how these change with each version of Chrome.

RUM solutions, however, will measure from a wider variety of browsers. Chromium-based browsers (Edge, Opera, and so on) will likely be similar to Chrome, unless Chrome is implementing new changes as noted in the Changelog.

For non-Chromium browsers, the differences can be more pronounced. First Contentful Paint (FCP), for example, is available in Safari and Firefox, but is measured in a different way. This can lead to significant variances in the times reported. As stated previously, if you are wanting to compare RUM to CrUX, it is best to filter on just Chrome users to allow for a like-for-like comparison.

Metrics timing #

Core Web Vitals metrics are provided by web browser APIs, but that does not mean there is not a potential for differences of values reported using them. Exactly when the metric measurement is taken—at page load or throughout the full page life cycle—can lead to differences. RUM tools may not always measure metrics in the same way—even if using the same names—and the same browser APIs to get the data, which can be confusing.

Largest Contentful Paint (LCP) is a page-load metric. A number of LCP elements can be reported by the Web API if larger elements are loaded later after the initial render. The final LCP element is when the page finishes loading or the user interacts with the page. Therefore differences can arise if the LCP element is reported earlier than those two events.

Additionally, in field data, the LCP element can be different depending on how the page is loaded. For a default page load showing the top of the page content, the LCP element will depend primarily on the screen size. However, if the page is opened with an anchor link further down the document, or similarly opened with a deep-link into a Single Page App (SPA)—more on that later—then the LCP element can be different.

Do not assume that the LCP timings provided in either CrUX nor RUM are based on the same element as lab tools. While CrUX will give you the overall LCP value per page or origin, RUM can segment this further to identify individual LCP problem sessions.

Cumulative Layout Shift (CLS) is measured throughout the life of the page, so the initial page-load CLS may not be representative of pages that cause greater shifts later after the page has loaded and the user has interacted with it. Taking the CLS value only after the page load—as many RUM products do—will therefore give a different result than taking the CLS value after the user finishes with the page.

First Input Delay (FID) requires an input to be measured, so it cannot be measured at page load. But—as its name implies—FID measures the first input only. The newer Interaction to Next Paint (INP) responsiveness metric measures all interactions throughout the life of the page, in a similar fashion to CLS, and so the reported value of INP may be very different if measured after the user has made a number of interactions on the page.

CrUX will follow the Core Web Vitals documentation and measure these through the full lifetime of the page. Many RUM providers choose instead to measure these metrics either after page load, or at some other time (for example, when a key call-to-action is clicked) for various reasons.

Getting an understanding from your RUM provider as to when Core Web Vitals are measured is important when seeing unexplained variances between the two data sources.

Single-page applications #

Single-page applications (SPA) work by updating the content on the current page, rather than performing traditional page navigation at the browser level. This means the browser does not see these as page navigations, despite users experiencing them as such. The Core Web Vitals APIs provided by the browser will not take these into consideration, and therefore CrUX does not currently support these page navigations. Work is currently underway to resolve this issue.

Some RUM providers do attempt to detect “soft navigations” in SPAs, but if they’re also attributing Core Web Vitals metrics to those “soft navigations” it will lead to differences with CrUX since the underlying APIs do not support this.

CrUX and Web API differences #

As well as the differences in which page views are measured, and what is measured, there are a few other, more complicated, scenarios to be aware of that can lead to differences in CrUX and RUM data. Some of these are due to limitations of the Web APIs used to measure the metrics, and some are where the results returned by the API need to be treated differently for certain scenarios. The Core Web Vitals documentation lists these differences for LCP, CLS, and FID but the main differences are noted below.

Back/forward cache #

CrUX considers Back/forward cache (or bfcache) restores as page navigations even though they do not result in a traditional page load. As the Web APIs do not treat these as a page load, RUM solutions need to take extra steps for these pages to be counted if they wish to match CrUX. These are considerably faster page loads that can result in overall better performance being reported for a site, so not including them can result in worse overall page performance metrics. Refer to your RUM solution to understand if they handle bfcache restored pages.

Iframes #

For security and privacy reasons, top-level pages do not have access to content within iframes (not even same-origin iframes). This means performance metrics for content in those can only be measured by the iframe itself, and not through the Web APIs on the framing page. If the iframe content includes the LCP element, or content that impacts the CLS, FID or INP experienced by the user, this will not be available to RUM solutions (including the Google web-vitals JavaScript library).

CrUX however, being measured by the Chrome browser itself rather than the page, does not have these limitations and so does measure metrics within iframes when reporting Core Web Vitals. This more accurately reflects what the user experiences, but can be another reason for differences for sites making use of iframes.

Cross-origin resources #

LCP media served from other domains do not give render time in the PerformanceObserver API—unless the Timing-Allow-Origin header (TAO) is provided—due to browser security restrictions to reduce timing attacks. This falls back to the load time of the resource but this may be quite different from when the content was actually painted.

Again, CrUX does report the render time data for Core Web Vitals. Sites are advised to limit cross-origin content that impacts the Core Web Vitals metrics and to enable TAO where possible if they wish to measure this more accurately. Other cross-origin resources may be subject to similar restrictions.

Background tabs and prerender #

When a page is not opened in the foreground—such as opening it in the background or if it uses prerender options (currently under development for Chrome)—they will still emit metrics via Web APIs. However, these are not reported by CrUX since they give timings that are inconsistent with the user experience. RUM solutions should also consider ignoring these, or at least explaining how these page views are treated.

So what can we do about it? #

We have shown why there may be differences between CrUX and RUM data, either due to differences in the methodology each uses or due to which users and page views are included or excluded. Ideally, both sets of data will still be representative of your site performance to be useful, but this article should outline why it is very unlikely to get the exact same numbers in each.

Where differences are slight (for example reporting a LCP of 2.0 seconds versus 2.2 seconds) both datasets will be useful and can usually be considered to be roughly in sync.

When pronounced differences make you question the accuracy of the data, you should try to understand those differences. Can the RUM data be filtered to be more closely aligned with CrUX (looking only at Chrome users, for desktop or mobile, with 75th percentile values over 28 days) to reduce these differences?

If so—and you can get the data to match more closely—then you should still ask why you are seeing these differences in the overall data and what this means. Are non-Chrome users skewing your metrics in a positive or negative way? Does this give you more insights into where you have performance issues that you can prioritize?

If your non-Chrome users are getting different results then you can use this valuable insight that RUM has given you to optimize differently. For example, certain APIs are not available on certain browsers, but you can consider alternatives for unsupported browsers to also improve their experiences. Or you can give a different, but more performant, experience to users on constrained devices or networks. CrUX is limited to Chrome data, but you should consider all your site visitors’ experiences to help prioritize improvements. RUM data can fill that gap.

Once you understand the reasons for any differences, both tools can be incredibly useful to understand the user experiences of your website, and to help improve this even if the numbers are not identical. Use your RUM data to complement CrUX data and allow you to dig into what CrUX is telling you at a high level by segmenting your traffic to help you identify if it’s particular areas of your site or user base that need attention.

Looking at the trends to see your improvements are having the expected positive impacts is often more important than having each number match exactly between the two data sources. As mentioned above, RUM allows you to look at different time frames to get an advance look at what your 28 day CrUX scores will be—though looking at too short time frames can lead to noisy data, hence why CrUX uses 28 days.

Often there is no “right” or “wrong” answer in these different metrics – they are simply having a different lens on your users and how they are experiencing your site. As long as you understand why these differences happen, and what that can do to drive your decision making, that is what is more important to better serve your site visitors.

Acknowledgements #

Hero image by Steven Lelham on Unsplash

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