Synthetic Monitoring Setup: Detecting Hidden Website Problems

Ordinary ping monitoring shows HTTP 200 but doesn't notice that the login form is broken or a page loads in 10 seconds from a remote region. We've encountered this: a client was losing conversions while users complained to support. Synthetic Monitoring solves this problem — it simulates real actions: logging into an account, filling a cart, placing an order. Tests run from different geolocations every minute. This uncovers hidden failures before they impact your business. We configure synthetic monitoring turnkey: from scenario audit to dashboards and alerts. Our team has over 5 years of monitoring experience and 50+ projects.

How Synthetic Monitoring Reveals Hidden Problems

A scenario is recorded once, then run on a schedule. For example, a login test fills fields and checks for the presence of a dashboard. If a step fails (page not loaded, button not found), the system generates an alert. Thresholds are configurable: 2 consecutive failures in 5 minutes — critical, send to Slack and PagerDuty. This allows faster response than waiting for user reports.

On one project, we discovered that a server in Asia responded with TTFB of 8 seconds due to incorrect CDN configuration. Synthetic Monitoring with a Tokyo location showed this within 5 minutes after deployment. Thanks to the Slack alert, the issue was fixed before users noticed. As a result, error detection time dropped from hours to minutes.

What Ordinary Checks Miss — Synthetic Monitoring Configuration

• API timeout — ping passes, but backend returns 503.
• Broken CSS/JS — page renders incorrectly in a specific browser.
• Slow authentication — login time exceeds 5 seconds in a remote region.

These issues are invisible with standard monitoring. Synthetic Monitoring emulates a browser and measures real user experience. We use three approaches: Datadog Synthetics (cloud), Checkly (code-first), and Grafana k6 (load testing). Let's compare them.

Comparison of Tools

Datadog is convenient for quick no-code setup. Checkly is for developers who want to version control tests in git. k6 is for load testing. The choice depends on your stack and tasks.

Why Choose a Code-First Approach for Tests?

A code-first approach (Checkly, k6) integrates with your CI/CD: deploying tests with the same command as site code. No separate environment needed. Checkly provides ready integrations with Slack and PagerDuty.

// __checks__/login.spec.ts
import { test, expect } from '@playwright/test';
test('Login flow works', async ({ page }) => {
  await page.goto('https://mysite.com/login');
  await page.fill('[name="email"]', process.env.MONITOR_EMAIL!);
  await page.fill('[name="password"]', process.env.MONITOR_PASSWORD!);
  await page.click('[type="submit"]');
  await expect(page).toHaveURL(/\/dashboard/);
});

This is more efficient than custom solutions: time savings on setup up to 40% compared to traditional UI tests. Playwright ensures robust selectors and cross-browser support. According to Playwright documentation, these tests run more stable than Selenium.

What's Included in Synthetic Monitoring Setup?

Result: a dashboard with metrics for availability, response time, and scenario execution. We guarantee tests will function for 30 days after launch.

Timelines and Cost

Project assessment is free. Typical timeline: 2 to 5 business days depending on scenario complexity. Cost is calculated individually and includes warranty. Contact us for a free assessment of your project. Request a consultation to find out which tool fits your needs.

Measuring Core Web Vitals via Synthetic Monitoring

Synthetic Monitoring allows measuring LCP, CLS, INP from multiple points. This is especially important for e-commerce where every second of load time costs conversions. Synthetic monitoring also helps identify issues with CDN, DNS, and routing. According to Google Web Dev, a good LCP should be under 2.5 seconds.

Get a consultation on Synthetic Monitoring setup for your project. We will select the optimal tool and help you avoid hidden failures.

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.