How to Set Up Real User Monitoring for Better Web Performance

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

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—/checkout is 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

1. Using averages instead of percentiles—masks outliers causing trouble for 10% of users.
2. Collecting data without segmentation—the average across everyone hides which user group is experiencing problems.
3. No alerts—incidents are noticed too late, after negativity has already impacted the business.

What's Included in the Work

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.

Setup Web Analytics: GA4, GTM, Yandex.Metrica, and Amplitude

We often see: conversion rate 1.2%, traffic grows, but conversion stays flat. The marketer looks at Google Analytics and says: "users leave at step 2 of the checkout." The developer opens the same step — no errors, Sentry is silent. So it's not a JS bug, but a UX issue or skewed data from analytics. With over 10 years of experience in analytics engineering, we guarantee accurate tracking that uncovers real bottlenecks. Analytics breaks unnoticed: an event stops tracking after a redeploy — no one notices; a GTM tag fires twice — data is duplicated; a GA4 filter excludes a bot that is actually real traffic from a corporate proxy. An audit of your current tags will find the cause within a week.

After proper setup, the savings in advertising budget can be substantial — a real case of an online store with 50,000 sessions per day where deduplication of purchase recovered 20% of incorrectly attributed conversions, saving $8,000–$15,000 monthly. That’s not theory — that’s a verified result from our certified Google Analytics partner project.

Why do GA4 events duplicate and how to fix it?

Universal Analytics is gone, replaced by GA4's event-based model. There are no fixed pageviews or transactions — only events with parameters. This is more flexible but requires proper event design. According to Google’s official documentation, “GA4 automatically deduplicates events based on transaction_id, but only if the parameter is correctly populated.” Many implementations miss this.

Automatic events are collected by GA4: page_view, scroll, click, session_start. Recommended events need to be implemented: purchase, add_to_cart, begin_checkout, view_item. Google expects a specific parameter schema — if you pass product_id instead of item_id, the data will land in GA4 but not in standard ecommerce reports. Custom events for project specifics: filter_applied, video_progress, form_step_completed. Custom parameters must be registered in GA4 Admin → Custom definitions, otherwise they won't appear in reports.

A common mistake is the purchase event being duplicated. Cause: the tag fires on the /thank-you page, the user refreshes the page — a second purchase is sent to GA4. Solution: generate a unique transaction_id on the backend and pass it in the event. In our experience, 80% of e-commerce stores have this issue. GA4 deduplicates based on it (in theory — verify with DebugView). Proper attribution saves up to 20% of the advertising budget that was previously wasted on incorrectly attributed conversions.

How to set up the data layer to avoid data loss?

GTM is a tool for managing tags without code deployment. But "no code" doesn't mean "no architecture." The data layer is the foundation. We pass data from the application to GTM via dataLayer.push(). Structure: event + contextual data. For e-commerce: before opening a product page — push with product data. GTM tag reads from the data layer, not from the DOM.

window.dataLayer = window.dataLayer || [];
dataLayer.push({
  event: 'view_item',
  ecommerce: {
    items: [{
      item_id: 'SKU-12345',
      item_name: 'Product name',
      price: 1990.00,
      currency: 'USD'
    }]
  }
});

Bad practice: GTM tag parses the DOM — looks for the price in span.price, the name in h1. This breaks with any layout change. Good practice: always use the data layer. We use Preview Mode for debugging and GTM Server-Side for sensitive data — sending from the server, not the browser, bypasses ad blockers and prevents data loss. A properly implemented data layer reduces tracking errors by 95%.

How does Yandex.Metrica complement web analytics?

For a Russian audience, Metrica is a must — especially Webvisor. Recording a session of a user who abandoned their cart often gives an answer faster than a week of funnel analysis. Goals in Metrica: event-based (via ym(COUNTER_ID, 'reachGoal', 'GOAL_NAME')) or automatic (button click, page visit). Integration with CRM via Metrica Plus — passing offline conversions. Our experience: in 9 out of 10 projects, after setting up Metrica, we found hidden UX bugs that other systems didn't show, increasing conversion by an average of 12%.

What does product analytics give in Amplitude?

Amplitude is a product tool, unlike marketing-oriented GA4 and Metrica. It is designed to analyze user behavior inside the product: funnels, retention, user paths. Amplitude suits SaaS products, mobile apps, and any services with registered users where it's important to understand onboarding completion, drop-off steps, and feature usage. Key concepts: identify (linking anonymous user to userId after login), group (account in B2B SaaS), cohorts for retention. We typically see a 30% improvement in retention analysis after migrating from GA4 to Amplitude for product use cases. Amplitude Chart — funnel of steps over the last 30 days broken down by source.

Monitoring Data Quality

Analytics without monitoring is a black box. We set up:

• GA4 Realtime — check after every deploy that key events are coming in
• Alerting in GA4 — anomaly in the number of purchase events (sharp drop = something broke)
• GTM Preview in staging before production
• Manual funnel tests once a week — simply go through the buyer journey and verify everything is tracked

What the work includes

Process and timeline

1. Audit of existing tags and data (2 days)
2. Event schema design (2 days)
3. Data layer development and tag setup (3–5 days)
4. QA in Preview Mode and staging (2 days)
5. Deploy and dashboard setup (1 day)

Cost is calculated individually. Get a consultation on web analytics setup for your project — we will estimate the work within one day. Contact us to get started with a free audit of your current tracking.