Session Recording Analysis for UX Issue Detection

You launch ads, traffic flows, but conversion stagnates. Quantitative analytics shows an exit rate of 70% on the checkout page. Without session recordings, it's impossible to understand user behavior. Recent case: an online clothing store with a 2.1% conversion rate and 12,000 daily visitors. Analyzing 50 sessions in Microsoft Clarity revealed that 35% of users couldn't find the "Add to Cart" button — it was hidden behind a banner. After moving the button, conversion rose to 3.8% within a week, and the fix cost paid off in three days.

We see such cases every day: clicks on decorative icons, waiting for nonexistent autofill, confusion with input fields. Session recordings are the only way to see the problem through the visitor's eyes and identify specific friction points. Heatmaps show the average temperature across the site; recordings show every step with context. A similar conversion boost is possible for your site.

What UX Problems Do Session Recordings Reveal?

Fixing these problems can generate additional revenue: for example, a 15% reduction in bounce rate could add 150,000 rubles per month for a store with an average order of 3,000 rubles and 1,000 orders per day. But first, you need to find them.

Session Recording Tools: Comparison and Selection

We use Microsoft Clarity, Hotjar, FullStory, and LogRocket — the choice depends on your budget and goals. Clarity is free and suitable for quick diagnostics, Hotjar offers advanced filters, FullStory is for enterprise with API access, and LogRocket for technical errors.

Clarity is the best choice for a quick start. Hotjar is for deep analysis with segmentation. FullStory is for integrating with your data via API. LogRocket is if frontend errors are a priority. A consultation will help you choose the optimal tool for your budget and needs.

How to Install Microsoft Clarity

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Custom tags for filtering:

clarity("set", "user_plan", user.plan)
clarity("set", "page_type", "product_page")
clarity("set", "ab_variant", experimentVariant)

// Track user plan (guest, registered, premium)
clarity("set", "customer_tier", user.tier)
// Page type for segmentation
clarity("set", "page_category", "checkout")
// A/B test variant
clarity("set", "experiment", "test_v2")

For more details, see the official Clarity documentation.

How to Set Up Filters to Identify Problems?

Instead of watching all recordings, use signals:

• Dead clicks: clicks on non-clickable elements
• Rage clicks: 3+ clicks in 1 second in the same spot
• Excessive scrolling: user scrolled >90% of the page
• Quick back: returned to previous page in <8 seconds
• JavaScript errors: sessions with console errors

Why Are Session Recordings More Effective Than Heatmaps?

Heatmaps show where users clicked on average, but not why. A session recording gives full context: did the user click because an icon looked like a button, or due to a script error? Only recordings let you see the sequence of actions and emotions (through movement speed).

Integration with Product Analytics

// Link session recording to GA4 event
document.getElementById('checkout-form').addEventListener('submit', function() {
  clarity("event", "checkout_submitted")
})

// Mark session on error
window.addEventListener('error', function(e) {
  clarity("set", "has_js_error", "true")
  clarity("event", "javascript_error", { message: e.message })
})

// Link Clarity session with GA4 client_id
clarity.identify(userId, sessionId, undefined, {
  ga_client_id: gaClientId
})

What's Included in the Work?

1. Analysis of current metrics and identification of pages with high bounce rates
2. Setup of recording tools with custom tags
3. Collection of 100 sessions using signal filters
4. Documentation of each anomalous behavior with timestamps
5. Clustering of problems by frequency and conversion impact
6. Preparation of a report with prioritized hypotheses and effort estimates

GDPR and Privacy

// Mask personal data in recordings
// Clarity automatically masks fields with type="password"
// Manual masking:
// .credit-card-number { display: none } in CSS — won't be recorded
// Programmatically
clarity("consent", false)  // disable recording for non-consenting users
clarity("consent", true)   // enable after consent

For more details on masking, see the official Clarity documentation.

Timeline and Guarantees

Tool installation, custom tag setup, analysis of 100 sessions with a report — 2–3 business days. We guarantee you will receive at least 5 realistic hypotheses for UX improvement. If we don't find any issues within a week, we'll refund your money. We have analyzed over 200 websites across various niches — from online stores to SaaS platforms. Our engineers hold certifications in Google Analytics and Hotjar. Order a session analysis of your site — contact us for a consultation. Our engineers are ready to help you increase conversion this week.

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.