Microsoft Clarity Website Performance Data

Quick answer: what the data can and cannot tell you

Observed Clarity-origin performance associations paired with a measurement-value test. The practical decision is: Decide which templates justify session replay and test consent, loading, and sampling choices.

HTTP Archive detects technologies on an origin’s home page and one interior page, then combines that classification with origin-level field performance from the Chrome UX Report. The June 1, 2026 snapshot below is mobile-only. It can reveal a population-level association worth investigating; it cannot tell you the incremental cost of installing or removing one product on one site.

“Good CWV” is the share of assessable origins passing the Core Web Vitals assessment in the report. “Vs all origins” is an unadjusted percentage-point difference, not a treatment effect. Do not translate it into lost conversions, rankings, or revenue without site-specific evidence.

What the June 2026 snapshot says about Microsoft Clarity website performance

For Microsoft Clarity, the all-rank detected population contains 535,912 mobile origins. Its median JavaScript is 1,535 KB, 109.5% more than the all-origin report baseline. Its 44.4% good-CWV share is 8.7 percentage points below that baseline.

The rank slices are a necessary caution: good CWV is 42.5% in Top 1M and 32.8% in Top 100k, while median JavaScript is 1825 KB and 2049 KB respectively. Movement across those groups means the technology label is not a fixed performance cost. It is a marker attached to sites with different scale, requirements, stacks, and implementation practices.

For this page’s decision, the useful conclusion is not “Microsoft Clarity is fast” or “Microsoft Clarity is slow.” It is: reproduce the relevant feature on a representative template, compare the current and narrowed or delayed configuration, and retain the technology only when the business outcome justifies the measured user cost.

What most comparisons miss

Technology-present and technology-absent websites are not interchangeable populations. A large SaaS company may install chat, analytics, consent, experimentation, advertising, and personalization tools together. A small content site may install only analytics. A commerce implementation may carry product imagery and app code that a brochure site never loads.

That creates at least five confounders:

1. Traffic rank: popular origins often have different budgets, audiences, infrastructure, and product requirements.
2. Site type: commerce, SaaS, publishing, and brochure sites render different content and interactions.
3. Co-installed technologies: one detected product may proxy for a much larger marketing or commerce stack.
4. Total page size: images, fonts, video, first-party bundles, and markup can dominate weight.
5. Implementation quality: the same tool can load on every page, after consent, after interaction, or only where it creates value.

This is why the article says “associated with,” not “causes.” The public report is useful for discovery. A causal claim would require a stronger design and still need careful interpretation.

Rank sensitivity: does the pattern persist?

Traffic-rank slices do not solve confounding, but they are a useful stability check. Each cell below shows good-CWV share and median JavaScript for the detected-technology population in that rank group.

Read this table as a prompt for questions:

• Does the direction change across rank groups?
• Does a small detected-origin sample make a comparison unstable?
• Are the JavaScript and field-performance signals moving together, or might another factor dominate?
• Does the buyer’s site resemble any of these populations closely enough for the median to be useful?

The final question is the most important. Cross-site medians should shape a test plan, not replace one.

A decision framework for Microsoft Clarity website performance

Use four gates before changing the stack.

1. Name the business job

Write the precise decision the technology supports: route qualified support questions, measure purchases, observe checkout friction, manage content, or operate a storefront. “Marketing needs it” is not an acceptance criterion.

2. Establish a current baseline

Use real-user monitoring or CrUX where coverage exists, and a repeatable lab test for diagnosis. Google’s PageSpeed Insights documentation explains why field and lab data answer different questions: field data reflects recent real-user experiences, while Lighthouse helps diagnose one controlled run.

Capture at least:

• p75 LCP, INP, and CLS by important template and device class;
• JavaScript transfer and execution cost;
• third-party request count and domains;
• long tasks and interaction delay near the important action;
• the business metric the technology is supposed to improve.

3. Test the smallest viable loading policy

web.dev’s third-party JavaScript guidance recommends diagnosing costly third-party code and considering async/defer, delayed loading, removal, or other implementation changes where appropriate. The right experiment depends on the tool:

• load only on relevant templates;
• load after consent where required;
• delay until the critical content is visible;
• load after an intent signal or user interaction;
• reduce destinations, events, plugins, apps, or integrations;
• replace a heavy embed with a facade when the feature permits it.

4. Compare business value and user cost together

A faster page that breaks support or measurement is not automatically better. A feature that adds leads but creates interaction delay or privacy risk is not automatically acceptable. Define both sides before the experiment.

Worked validation scorecard

This is an illustrative planning framework, not an observed Arsum case study and not a performance guarantee.

Do not set a universal numeric pass threshold before seeing the site’s baseline, traffic variability, and business requirements. Agree the threshold before analyzing the result so the team cannot move the goalposts after the test.

The matched analysis Arsum has designed but not claimed

Arsum has prepared a BigQuery analysis design that compares technology-present and technology-absent origins within matched strata for traffic rank, site type, co-installed technology count, and total page-size band. The query is versioned in the project, but it has not been executed because the local environment does not have an authorized Google Cloud project and billing context.

Until that result is run, stored, reviewed, and sensitivity-tested, the published numbers remain descriptive. Even a matched observational result would show an adjusted association, not randomized causation. Unmeasured differences—team maturity, audience geography, template complexity, consent behavior, hosting, and product requirements—could remain.

Practitioner signals: useful questions, not proof

Public operator discussions repeatedly ask whether chat and marketing scripts need to load at startup, and how tag containers accumulate payload when no one owns inventory or deletion. Those discussions are useful because they surface governance questions. They are not representative samples and cannot establish typical impact.

For example, an r/TechSEO thread about tag-manager bloat focuses on accumulated tags and missing inventory management. An r/SEO thread about third-party tags asks whether tools such as analytics, heatmaps, and chat can be delayed when the feature is not needed immediately. A separate tracking-tag performance anecdote describes one implementation’s before/after test, but its measurements cannot be generalized because it is not a controlled multi-site study. Use all three as prompts to inspect your own container and waterfall—not as benchmark data.

What to do next

1. Inventory every script, integration, app, and embed on the target templates.
2. Assign a business owner, technical owner, purpose, last-used date, and removal date to each.
3. Measure field and lab baselines before changing the stack.
4. Test one loading or scope decision at a time.
5. Keep the change only if the technical result and the business outcome meet pre-agreed criteria.
6. Recheck after vendor, consent, theme, plugin, or tag-container changes.

Continue with the Website Technology Tax methodology and index or compare Google Analytics website performance, Plausible vs Google Analytics performance, Matomo vs Google Analytics performance, analytics tools performance tax.

Sources and limitations

• HTTP Archive Technology Report: detected-technology populations, lab page weight, and CrUX field distributions.
• CrUX methodology: eligibility and aggregation of real-user experience data.
• PageSpeed Insights methodology: the field-versus-lab distinction and Core Web Vitals assessment.
• web.dev third-party JavaScript guidance: mechanisms and implementation options for third-party code.

Snapshot: June 1, 2026; mobile; public HTTP Archive report API. Median origin p75 values in the local snapshot are reconstructed from report histograms and should be treated as approximate distribution summaries. Detection limits, CrUX eligibility, site mix, co-installation, and implementation differences all limit inference.