End-to-End Analytics Setup for Your Website

You run ads, spend budget, but sales aren't growing? You don't know which channel is profitable and which is draining money. We fix that — we set up end-to-end analytics that connects every ruble of ad spend to actual revenue. Within 2-3 weeks, you'll see the full picture: from click to deal. Without end-to-end analytics, you're managing blindly — we change that.

According to Wikipedia, end-to-end analytics is a method of measuring ad effectiveness that links costs to specific sales. In this article, we'll break down how end-to-end analytics works, what components are needed, and how we implement it. You'll learn how to combine data from ad platforms, CRM, and your website to track ROI per channel. Example: a B2B client reduced cost per lead by 35% after implementing end-to-end analytics by reallocating budget, saving about 250,000 ₽ per month.

Components of end-to-end analytics

• Ad spend tracker — pulls cost data from Yandex.Direct, Google Ads, VKontakte, Facebook via their APIs.
• Call tracking — replaces phone numbers with unique ones per source to track calls.
• CRM — stores deals with lead source attribution.
• BI/Dashboard — consolidates everything into a single table: spend → visits → leads → sales → revenue.

How to choose between Roistat and a custom implementation?

Roistat — a Russian end-to-end analytics platform. It's embedded via script, integrates with Yandex.Direct, Google Ads, amoCRM, Bitrix24. Suitable for most small/mid-size businesses. Custom analytics is needed when ready-made solutions don't cover your specifics or become too expensive at high volumes.

We often see: companies with high traffic (100,000+ visits per month) benefit from custom analytics because Roistat becomes costly. For smaller projects, Roistat is 2-3 times faster and cheaper.

Why end-to-end analytics pays for itself in 1-2 months?

End-to-end analytics shows which channels generate real profit. For example, you might discover that context ads give ROAS of 8, while targeted ads only 2. By reallocating budget, you increase total revenue without extra spend. We guarantee a payback within 1-2 months based on experience with 50+ projects.

Key end-to-end analytics metrics

How is the technical implementation done?

Architecture of custom implementation

Ad systems (API)
    → ETL service (Python/Go)
        → Data storage (ClickHouse / PostgreSQL / BigQuery)
            → BI tool (Redash / Metabase / Superset)
                → Dashboard

Website (UTM + cookies)
    → Backend
        → Data storage

CRM
    → Data storage

Fetching data from ad systems

# Yandex.Direct: get spend for a period
import requests

headers = {'Authorization': f'Bearer {YANDEX_TOKEN}', 'Client-Login': CLIENT_LOGIN}
report_body = {
    'params': {
        'SelectionCriteria': {
            'DateFrom': '2024-01-01', 'DateTo': '2024-01-31'
        },
        'FieldNames': ['Date', 'CampaignName', 'Impressions', 'Clicks', 'Cost'],
        'ReportName': 'Cost Report',
        'ReportType': 'CAMPAIGN_PERFORMANCE_REPORT',
        'DateRangeType': 'CUSTOM_DATE',
        'Format': 'TSV',
        'IncludeVAT': 'YES'
    }
}
response = requests.post(
    'https://api.direct.yandex.com/json/v5/reports',
    headers=headers,
    json=report_body
)

Linking leads to ad source

-- Table to store sessions with UTM
CREATE TABLE sessions (
    session_id   UUID PRIMARY KEY,
    user_id      BIGINT REFERENCES users(id),
    utm_source   VARCHAR(100),
    utm_medium   VARCHAR(100),
    utm_campaign VARCHAR(200),
    referrer     TEXT,
    created_at   TIMESTAMP
);

-- Leads linked to session
CREATE TABLE leads (
    id         BIGINT PRIMARY KEY,
    session_id UUID REFERENCES sessions(session_id),
    phone      VARCHAR(20),
    type       VARCHAR(50),  -- 'form' | 'call' | 'chat'
    created_at TIMESTAMP
);

-- Orders linked to lead
CREATE TABLE orders (
    id       BIGINT PRIMARY KEY,
    lead_id  BIGINT REFERENCES leads(id),
    total    INTEGER,
    status   VARCHAR(20),
    created_at TIMESTAMP
);

Main end-to-end analytics report

SELECT
    s.utm_source,
    s.utm_campaign,
    COUNT(DISTINCT s.session_id)    as visits,
    COUNT(DISTINCT l.id)            as leads,
    COUNT(DISTINCT o.id)            as orders,
    SUM(o.total) / 100.0            as revenue,
    SUM(rc.cost) / 100.0            as ad_spend,
    ROUND(SUM(o.total) / NULLIF(SUM(rc.cost), 0), 2) as roas
FROM sessions s
LEFT JOIN leads l ON l.session_id = s.session_id
LEFT JOIN orders o ON o.lead_id = l.id AND o.status = 'completed'
LEFT JOIN ad_costs rc ON rc.utm_source = s.utm_source
    AND rc.utm_campaign = s.utm_campaign
    AND DATE_TRUNC('day', rc.date) = DATE_TRUNC('day', s.created_at)
WHERE s.created_at >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY s.utm_source, s.utm_campaign
ORDER BY revenue DESC NULLS LAST;

Call tracking in custom implementation

For call tracking, we integrate with Calltouch or CoMagic. They replace the phone number on the site based on traffic source. On call, they pass UTM parameters via webhook. Alternatively, custom call tracking via SIP provider: a pool of virtual numbers, each visitor gets a unique number from the pool.

Dashboard in Metabase

Metabase connects directly to PostgreSQL and builds dashboards without writing code. For non-technical marketers, it's the optimal choice.

What our work includes?

• Audit of current ad accounts and CRM
• Integration of ad system APIs
• Call tracking setup (if needed)
• Development of ETL process for data collection
• Creation of dashboard with key metrics
• Documentation and training for your team
• Support for 30 days after launch

Process of work

1. Analysis — study your ad structure, CRM, site.
2. Design — choose architecture (platform or custom).
3. Implementation — set up data collection, ETL, dashboard.
4. Testing — verify data accuracy, adjust.
5. Deployment — go live, hand over documentation.

Approximate timelines

• Ready-made solution: 1-2 weeks
• Custom analytics: 4-6 weeks
• Hybrid approach: 2-4 weeks

Contact us for a consultation — we'll evaluate your project and propose the optimal solution. Order end-to-end analytics setup and gain full control over your ad budget.

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.