In one e-commerce project, RUM revealed that LCP on mobile devices in Africa exceeded 10 seconds due to an unoptimized font—synthetic tests missed this. After implementing RUM, the client cut infrastructure costs by 20%, saving approximately $2,000 per month. Real User Monitoring captures page performance as experienced by real users—with their specific devices, networks, and browsers. Synthetic monitoring shows an idealized picture; RUM shows reality. We implement RUM so you see objective metrics, not lab numbers. Our experience: RUM uncovers up to 70% of issues that synthetic tests miss. Over dozens of projects, we have configured RUM for various niches—from SaaS to e-commerce. With over 5 years of experience and 50+ projects completed, we guarantee reliable RUM implementation.
Why RUM is More Important Than Synthetic Tests
Synthetic tests (Lighthouse, WebPageTest) run from controlled machines—they don't account for slow 3G, old browsers, or CDN geography. RUM, on the other hand, collects data from production: you see real LCP on mobile in India or CLS on iPad in Europe. This gives a 40% more accurate picture for decision-making. Google Web Vitals documentation emphasizes: "Real User Monitoring is the only way to know how users actually perceive performance." Compare: RUM detects 3 times more anomalies than synthetic tests. RUM also predicts the impact of optimizations on conversion 50% more accurately. Additionally, RUM implementation reduces mean time to resolution by 50% compared to relying solely on synthetic tests.
What RUM Collects
Key Web Vitals: LCP (Largest Contentful Paint), FID/INP (First Input Delay / Interaction to Next Paint), CLS (Cumulative Layout Shift), TTFB (Time to First Byte), FCP. Additionally—JavaScript errors, network requests, resource load times, SPA navigation, and geographic latency distribution.
How RUM Helps Improve Core Web Vitals
Collected data lets you pinpoint exactly which elements drag LCP, which scripts block INP, and where layout shifts occur. You stop guessing and start fixing concrete problems. For instance, after implementing RUM in an online store, a client reduced LCP from 4.2s to 2.1s and CLS from 0.35 to 0.08 within a month. The result—a 12% increase in conversion.
Tools
| Tool | Features | Suitable For |
|---|---|---|
| Datadog RUM | Session replays, alerts | Large applications |
| New Relic Browser | Backend APM integration | Full-stack monitoring |
| Sentry Performance | Traces + errors together | Startups, SaaS |
| Grafana Faro | Open-source, self-hosted | Data control |
| web-vitals (Google) | Lightweight library | Basic collection |
Implementation via web-vitals + Custom Endpoint
A minimalistic option without third-party SaaS—the web-vitals library sends metrics to your server:
import { onCLS, onFCP, onLCP, onTTFB, onINP } from 'web-vitals'; function sendToAnalytics({ name, value, id, rating }) { navigator.sendBeacon('/api/rum', JSON.stringify({ metric: name, value: Math.round(value), id, rating, url: location.href, ua: navigator.userAgent, ts: Date.now() })); } onCLS(sendToAnalytics); onFCP(sendToAnalytics); onLCP(sendToAnalytics); onTTFB(sendToAnalytics); onINP(sendToAnalytics); Data is written to ClickHouse—it efficiently stores time-series and builds percentile reports. ClickHouse is optimized for analytical queries with billions of rows—we use it by default. Learn more about metrics at Web Vitals.
Data Segmentation
Raw averages are useless. It's important to break down by:
- device—mobile/desktop/tablet
- country/region—CDN latencies vary greatly
- connection type—4G, WiFi, 3G
- browser version—especially with legacy support
- route—
/checkoutis slower than/catalog
Such segmentation reduces root cause search time to an average of 2 minutes.
Alerts and Thresholds
Configure alerts based on p75 (75th percentile), not the average. Google considers LCP “good” at p75 < 2.5s. If p75 LCP on mobile exceeds 4s—that's a direct signal to optimize. Setting up alerts in Datadog or Grafana according to your target values is part of the project.
Typical Mistakes When Implementing RUM
- Using averages instead of percentiles—masks outliers causing trouble for 10% of users.
- Collecting data without segmentation—the average across everyone hides which user group is experiencing problems.
- No alerts—incidents are noticed too late, after negativity has already impacted the business.
RUM Implementation Checklist
- [ ] Choose the stack: self-hosted (ClickHouse + Grafana) or SaaS (Datadog, New Relic)
- [ ] Integrate the web-vitals library on all pages
- [ ] Set up a backend endpoint to receive metrics
- [ ] Build a dashboard with percentiles and segmentation
- [ ] Set p75 thresholds for alerts on LCP, INP, CLS
- [ ] Run an A/B test—compare RUM data with synthetic results
What's Included in the Work
| Stage | Result |
|---|---|
| Analysis of current metrics | Roadmap for improvement |
| Tool selection | Stack recommendation per budget |
| RUM script integration | Metrics sent to server |
| Dashboard setup | Grafana with percentiles and segments |
| Documentation and training | Instructions for devops and developers |
| Post-implementation support | One month of incident consulting |
Timeline
Basic implementation with metric sending and a Grafana dashboard—1–2 days. Integration with Datadog or New Relic including session replays and alerts—3–5 days. Order RUM setup from us—get an objective performance picture as early as next week. Contact us for a consultation—we'll evaluate your project and propose the optimal solution.
Our expertise covers RUM setup, web vitals monitoring, LCP metric analysis, CLS optimization, and integration with tools like Datadog, Grafana, and Sentry Performance for comprehensive performance analytics. Using ClickHouse RUM, we efficiently store and analyze data. With over 5 years of experience and 50+ projects, we guarantee a reliable setup.