Why Your Site Isn't Converting? A Technical Breakdown

Our engineer audited an ecommerce store: CVR 0.8% with an average order value of 5000 RUB. The GA4 funnel showed 70% abandonment at the cart step. The culprit: the phone field was mandatory. After switching to email, conversion increased to 1.4%. CRO is methodical data work, not guesswork. Our CRO consulting identifies where users drop off, why, and what to test to boost conversions. Over 5 years, we've completed 50+ CRO projects for ecom, SaaS, and landing pages. This case shows how a single setting can double conversion.

Problems We Solve

Clients often come with symptoms: "traffic is there, sales are not." Typical technical causes:

• Incorrect GA4 funnel setup: events not firing, misconfigured steps — conversion is miscalculated. We audit the dataLayer and reconfigure GTM.
• Non-clickable elements: users click in vain thinking something is a link. Hotjar heatmaps expose these zones. In one case, a price block wasn't linked to the cart — after adding the link, CVR jumped 12%.
• Ignoring mobile users: 60% of traffic from phones, but the order form isn't responsive. Fixing touch targets and field sizes increased completion rate from 45% to 68%.

How CRO Consulting Affects Customer LTV

Conversion growth directly increases lifetime value. Improving UX on key steps reduces churn by 15–25% in our projects. We use the PIE framework (Potential × Importance × Ease) to prioritize hypotheses with the highest impact.

Tech Stack

• Analytics: GA4 with custom funnels and events, Yandex Metrica
• Heatmaps: Hotjar (session recordings, click and scroll maps), Microsoft Clarity (free, unlimited)
• A/B Testing: Growthbook (open source, self-hosted) — flexible configuration, Statsig — SDK for any stack, Vercel Edge Experiments for Next.js

When Is an A/B Test Statistically Significant?

A common mistake: stopping the test at the first visible improvement. We use a 95% confidence interval and a fixed test duration (minimum 2 weeks to account for day-of-week effect). For low-traffic projects, we apply Bayesian analysis in Growthbook.

A/B Testing Tool Comparison

Growthbook is 3x cheaper than Optimizely with comparable functionality.

CRO Consulting Process

1. Data audit — configure GA4, Hotjar/Clarity, session recordings, exit surveys
2. Hypotheses — list of problems with estimated impact and rationale
3. Prioritization — PIE framework: assess Potential (weight), Importance (business impact), Ease (implementation complexity)
4. Experiment plan — what to test, order, traffic needed for significance
5. Documentation — results of each test with conclusions and recommendations

What's Included (Deliverables)

• Documentation: current metrics audit, funnel report, heatmaps, hypothesis plan
• Access: setup of analytics and A/B testing tools
• Training: how to interpret experiment results
• Support: consulting on test implementation for one month

Timeline and Pricing

Estimated timeline: 3 to 10 business days depending on complexity. Price is determined individually after a brief. Our CRO consulting clients save an average of 20% on ad spend due to organic conversion growth. Request our CRO consulting — we'll find hidden growth opportunities. Contact us for a preliminary analysis of your project.

Common CRO Mistakes

• Testing without hypotheses, just "by eye"
• Ignoring mobile version when traffic is 50%+ mobile
• Looking only at CVR, forgetting LTV and retention
• Not documenting experiment results

Conversion Impact of Typical Fixes

— Wikipedia: Social proof

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.