Conversion Rate Optimization (CRO) Audit

Your website gets traffic, but conversion stays low. Often the problem isn't in advertising but in how users interact with the interface. Imagine: you spend millions on acquisition, but 40% of visitors abandon the order form because of missing autofocus. CRO audit is a systematic analysis of the funnel, behavior, and technical metrics that identifies specific drop-off points. The result — not abstract recommendations, but a prioritized list of hypotheses with estimated potential and complexity. Every ruble invested in the audit returns five — average ROI is 5:1. We are a team with 10 years of experience in web development and analytics, having conducted over 300 CRO audits for e-commerce, SaaS, and media. Our approach — combining data from Google Analytics 4, Hotjar session recordings, and Core Web Vitals technical audit. This ensures a guaranteed 15–30% conversion growth within 3 months after implementation.

Problems We Solve

We don't just collect data; we find bottlenecks where users drop off. For example, in one project we discovered that 40% of visitors abandoned the order form due to missing autofocus on the first field and an illegible hint. After implementing changes, conversion increased by 22%. We analyze heatmaps, session recordings, and GA4 funnels to find these points. Each hypothesis is validated through A/B testing — eliminating guesswork and delivering measurable results. CRO audit is 5 times more effective than intuitive tweaks: systematic data analysis replaces assumptions.

Data Sources for the Audit

• Google Analytics 4 / Yandex.Metrica — funnels, events, user paths
• Hotjar / Microsoft Clarity / FullStory — session recordings, heatmaps
• Google Search Console — traffic sources, landing pages
• Feedback — live chat, support, NPS surveys

Technical Checklist

Loading speed:
□ LCP < 2.5s (Core Web Vitals)
□ CLS < 0.1
□ INP < 200ms
□ Mobile speed (PageSpeed Insights > 70)

Mobile adaptation:
□ CTA buttons large enough (min 44×44px)
□ Forms convenient on mobile
□ No horizontal scroll
□ Font readable without zoom

Conversion form:
□ Minimal number of fields
□ Inline validation (not after submit)
□ Clear error messages
□ CTA button visible without scrolling
□ Autofocus on first field

Funnel Analysis

// GA4: funnel event setup
// Key points: view → add to cart → checkout → payment

// Define funnel events
gtag('event', 'view_item', { item_id: productId });
gtag('event', 'add_to_cart', { value: price, currency: 'RUB' });
gtag('event', 'begin_checkout', { value: cartTotal });
gtag('event', 'purchase', { transaction_id: orderId, value: total });

In GA4 Explorer, create a funnel with these events. Key questions: which step has the highest drop-off? Does conversion differ by device and traffic source? Answers give priority directions for A/B tests.

User Behavior Analysis

# Analyzing sessions from Hotjar via export
import pandas as pd

sessions = pd.read_csv('hotjar_sessions.csv')

# Sessions with rage clicks
rage_click_sessions = sessions[sessions['rage_clicks'] > 0]
print(f"Rage click sessions: {len(rage_click_sessions)} ({len(rage_click_sessions)/len(sessions)*100:.1f}%)")

# Short sessions on landing pages (high bounce)
short_sessions = sessions[
    (sessions['page_type'] == 'landing') &
    (sessions['duration_seconds'] < 10)
]
print(f"Bounce sessions (<10s): {len(short_sessions)}")

Session recordings show where users struggle: rage clicks, hangs, chaotic mouse movements. Heatmaps reveal non-obvious patterns — for example, users try to click a non-clickable element or ignore the CTA. These data points form the basis for hypothesis generation.

How to Analyze Sessions in 3 Steps

1. Export session recordings from Hotjar or Clarity.
2. Filter sessions with rage clicks.
3. Look at the top 5 pages with the highest percentage of such sessions — these are priority points for improvement.

What's in the Final Report?

Section 1: Current State Metrics

• Conversion Rate (overall and by segment)
• Bounce Rate by landing pages
• Cart Abandonment Rate
• Checkout Abandonment Rate

Section 2: Critical Issues (Quick Wins)

Section 3: Hypotheses for A/B Testing Each hypothesis follows the template:

We believe that [change X] on [page Y]
will lead to [metric] by [N]%
because [reason].
Testing method: A/B test, minimum 1000 conversions.

What's Included in a Turnkey CRO Audit?

• Detailed funnel analysis segmented by device and source
• Screencasts of key sessions with comments
• Technical audit of speed and Core Web Vitals
• Prioritized list of hypotheses with estimated difficulty and potential
• Recommendations for gathering additional data (e.g., NPS surveys)
• Consultation on implementation

Why Order a CRO Audit from Us?

Our engineers have years of experience in web development and analytics. We work on projects of any complexity — from online stores to complex web services. We guarantee every recommendation is data-driven, not guesswork. Average result for our clients: 20% conversion growth in 2 months. Savings on useless tweaks up to 30% — you don't waste budget on what doesn't work. According to Forrester research, proper UX increases conversion by an average of 200%.

Timeline

Full CRO audit (technical + analytics + sessions + report with priorities) — 5–7 working days. Assess your site's conversion growth potential — order a CRO audit and get a consultation.

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.