Setting Up Conversions in GA4: Events, Attribution, and Best Practices

Most projects that come to us already have GA4 markup, but the data in reports does not match reality. Conversions are doubled, micro-conversions are not tracked, and attribution spreads the budget thin. The reason is incorrect event design and lack of custom parameters. We have set up analytics for 50+ projects over 5 years and know how to avoid these errors. After implementing properly designed events, data accuracy reaches 95%, and cost per lead drops by 20% due to correct attribution.

How GA4 Differs from Universal Analytics and Why It Matters

In UA, a goal was a page visit or a specific condition. GA4 treats conversions as events. Each event can be marked as a conversion and immediately participates in attribution. However, there is a nuance: GA4 counts multiple conversions of the same type in a session. If you need to count only the first registration, you'll need an additional parameter or filtering.

Why Data-Driven Attribution Is More Accurate Than Last Click and When to Use It

The default attribution model in GA4 is Data-Driven (when 1000+ conversions per month). It distributes credit among touchpoints rather than giving it all to the last click. Tests show DDA improves attribution accuracy by 30% compared to Last Click. If data is insufficient, GA4 automatically switches to Last Click. We recommend switching to DDA when you have enough volume — it provides a more realistic picture.

How to Properly Design Conversions

We start with a funnel audit: identify which user actions are truly significant — form submission, phone number click, order placement. Then for each action, create a separate event with a unique name. For example, generate_lead for a lead, purchase for a purchase. Use snake_case without spaces. A typical mistake is mixing multiple actions into one event, causing duplication and blurred attribution.

Basic Markup of Conversion Events

Conversions are sent via gtag.js or GTM. Example — form submission:

gtag('event', 'generate_lead', {
  event_category: 'lead',
  event_label: 'contact_form',
  value: 1,
  currency: 'RUB',
  form_name: 'main_contact',
  page_section: 'footer',
});

Purchase:

gtag('event', 'purchase', {
  transaction_id: 'ORDER-789',
  value: 14500,
  currency: 'RUB',
  items: [{ item_id: 'SKU-001', name: 'Pro Plan', price: 14500, quantity: 1 }],
});

It is important to pass the value in the value parameter and the currency.

Custom Conversions via GTM and Data Layer

If the site uses GTM, setup is easier. Create a trigger, e.g., for form submission or thank-you page visit. Then a tag of type "Google Analytics: GA4 Event". Test via Preview and DebugView. This way, you can mark any conversion in minutes. To pass dynamic data (user ID, order amount), use the dataLayer. Always clear ecommerce: null before the next event to prevent data overlap.

window.dataLayer.push({
  event: 'purchase',
  ecommerce: {
    transaction_id: orderId,
    value: totalAmount,
    currency: 'RUB',
    items: cartItems,
  },
  user_id: currentUser?.id,
});
window.dataLayer.push({ ecommerce: null });

Setup in GA4 Interface and Attribution

Events that have started coming in need to be marked as conversions: Admin → Events → Mark as conversion. Register event parameters in Custom Definitions. The limit is 50 custom dimensions per property. For attribution, GA4 uses Data-Driven Attribution by default (if enough data). The model can be changed in Attribution Settings. To import into Google Ads, link the accounts and select the desired conversions. After that, conversions are available for Target CPA strategies. Note: when changing the attribution model, past data is recalculated automatically, which may cause temporary discrepancies.

Debugging and Verification

Use DebugView in GA4 for real-time data. In the browser console, check dataLayer and gtag. The Tag Assistant extension helps see all hits.

What Our Work Includes

• Audit of current GA4 markup and error identification
• Event design aligned with business funnel
• GTM or gtag.js setup for sending conversions
• Registration of custom parameters and dimensions
• Attribution setup and Google Ads import
• Token (client_id, session_id) transfer to CRM if needed
• Training of the client's team on report usage
• Documentation of all configured events

We guarantee data accuracy: after delivery, you will receive reports where numbers match real user actions. Request an audit of your current markup — we will assess and propose a revision plan. Get a consultation on conversion setup.

Timelines

Markup of 3–5 key conversions in code + GTM tags — 1 day. Setup of custom parameters and Data Layer — 4–6 hours. Attribution setup and linking to Google Ads — 2–3 hours.

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.