Session Replay Setup (LogRocket/FullStory)

Session Replay Setup (LogRocket/FullStory) for Your Website

Your client reports a bug that you can't reproduce. Logs and screenshots aren't enough. You spend hours debugging, and the problem might be a rare scenario. Session Replay solves this: you literally look over the user's shoulder, seeing every click, scroll, and network request. In 10 minutes you find what would have taken days.

We've implemented Session Replay on 50+ projects—from e-commerce stores to SaaS platforms. We know where even experienced teams stumble: PII leaks, performance drops, non-obvious masking settings. Below are proven approaches that save time and frustration.

Why Integrate Session Replay with Sentry?

The LogRocket + Sentry combo gives superpowers: for every error, we get a video of the user's session. This reduces debugging time by 5–10x compared to traditional logging. For integration, we use the LogRocket.getSessionURL call, which returns a direct link to the recording, and save it in the error context: Sentry.configureScope(scope => scope.setExtra('sessionURL', sessionURL)). Now any bug can be investigated with full context of user actions.

How to Organize Data Masking?

Before production, you must hide passwords, payment data, and personal fields. In LogRocket, use the inputSanitizer parameter: true masks all text fields. Flexible configuration via requestSanitizer allows hiding authorization headers. To hide specific elements, add the CSS class lr-hide. In FullStory, there are also API methods for masking—by selectors or attributes. In practice, masking all PII fields takes about 30 minutes.

Tool Comparison

LogRocket vs FullStory

An open-source alternative with self-hosting is OpenReplay. It's less convenient but gives full data control.

OpenReplay vs LogRocket

LogRocket is better for debugging complex bugs, while FullStory is for UX analytics. In our experience, teams using LogRocket find the root cause on average 60% faster.

Technical Implementation

Installing LogRocket

import LogRocket from 'logrocket';
LogRocket.init('your-app/project-id');

// Identify user
LogRocket.identify(user.id, {
  name: user.name,
  email: user.email,
  plan: user.subscriptionPlan
});

For React, add logrocket-react to track components:

import setupLogRocketReact from 'logrocket-react';
setupLogRocketReact(LogRocket);

Masking Sensitive Data

Before production, mask passwords, payment data, personal fields:

LogRocket.init('app/id', {
  dom: {
    inputSanitizer: true, // hides all inputs
    textSanitizer: false
  },
  network: {
    requestSanitizer: request => {
      if (request.headers['Authorization']) {
        request.headers['Authorization'] = '[redacted]';
      }
      return request;
    }
  }
});

Or use the CSS class lr-hide on specific elements.

Linking to Sentry Errors

LogRocket.getSessionURL(sessionURL => {
  Sentry.configureScope(scope => {
    scope.setExtra('sessionURL', sessionURL);
  });
});

Now every Sentry error includes a direct link to the session recording where the bug occurred.

How We Set Up Session Replay

Turnkey Setup Process

We execute work in several stages: current architecture analysis → integration design → implementation (installation, masking, custom filters) → staging testing → production deployment. As a result, you get a ready tool with configured dashboards and alerts.

What's Included in Our Work

• Installation and configuration of LogRocket/FullStory
• PII masking (passwords, email, payments)
• Integration with Sentry/Datadog/Bugsnag
• Configuration of session filters (by browsers, events, users)
• Creation of dashboards for UX analytics
• Documentation on usage and access
• Team training (1 hour online)

Timeline and Cost

Timelines depend on integration complexity: from 1 to 5 days. Cost is calculated individually—we'll assess your project for free. Team time savings can reach 40 hours per month when using Sentry integration. Contact us for a consultation.

Why Trust Us

We have been configuring analytics tools for over 5 years. We've implemented 50+ Session Replay projects for e-commerce stores, SaaS platforms, and corporate portals. We guarantee correct operation and full compliance with privacy standards. Order Session Replay setup from us.

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.