How to use GA4 to analyze traffic

Google Analytics 4 is an event-based analytics system that allows you to see the entire user path: from the first touch to the deposit and repeated sessions. The key to GA4 value is the correct event and conversion scheme, campaign markup discipline, and linking to BI (BigQuery).

1) Architecture GA4 in a nutshell

Event model: each action is an'event' with a set of'parameters'.

User context: 'user _ id', 'device _ id', attributes (language, currency, GEO).

Session: automatically determined by activity (parameter 'session _ start').

Repository: aggregated reports in the interface + raw events in BigQuery.

The base without which it is impossible to move on: a single time zone, currency, stable 'user _ id' after authorization.

2) Campaign markup: UTM discipline

Minimum set for all sources:

'utm _ source '(channel/site),' utm _ medium '(traffic type: cpc, aff, social, email),' utm _ campaign '(campaign/sprint name),' utm _ content '(creative/angle),' utm _ term '(keyword/audience).

Rules:

Strict notation (case, delimiters) and a directory of valid values.
No spaces/Cyrillic in UTM - use Latin and '_'.
For partners, mirror 'sub _ id' to 'utm _ content' or add a separate 'aff _ sub' parameter.

3) Events and conversions: what to track

Basic 'events' (approximate names, keep consistency):

Top of funnel: 'page _ view', 'session _ start', 'view _ landing', 'scroll _ 90'.
Registration/verification: 'sign _ up _ request', 'kyc _ started', 'kyc _ approved'.
Payment funnel: 'deposit _ initiated', 'deposit _ success' (amount/method), 'within _ requested', 'within _ success'.
Monetization in the product: 'game _ view', 'spin', 'bet _ placed', 'bonus _ claimed'.
Quality/protection: 'rg _ limit _ set' (deposit/session limit), 'self _ exclusion', 'fraud _ flag _ triggered'.

Conversions (mark as conversion):

'kyc _ approved ',' deposit _ success' (FTD and repeated), optionally 'second _ deposit'.
You can create individual conversions at amount thresholds (for example, 'deposit _ 100 _ plus').

Required parameters:

`value`, `currency`, `payment_method`, `game_provider`, `campaign_id`, `creative_id`, `aff_sub`, `geo`, `device`, `is_returning` (bool), `vip_tier`.

4) Data quality settings

User-ID: assign after login/registration; Enable User-ID in the data stream.

Consent Mode v2: correctly transmit consent statuses (audit/remarketing/analytics).

Server-side tags: throw critical events through the server (minimum - deposits).

Internal traffic filters: exclude IP office/contractors.

Currencies and TZ: one report currency, one timezone per project.

5) Standard GA4 reports that give "meat"

User acquisition vs. Traffic acquisition: Distinguish between "first touch" and "all sessions."

Pages & Screens: see "where they land" and where the script breaks.

Tech> Device/OS/Browser: find compatibility issues.

Monetization (if configured) is the sum of'deposit _ success' events across UTM sections.

6) Explorations: GA4 power

6. 1. Funnel Exploration

Collect a funnel: 'view_landing → sign_up_request → kyc_approved → deposit_initiated → deposit_success'.

Add sections: source/creative/geo/device. See the step with the largest stockpile and the time to conversion (Conversion lag).

6. 2. Path Exploration

Track unexpected paths: which screens go before 'deposit _ initiated', which events interfere (for example, leaving in FAQ/terms).

6. 3. Cohort Exploration

Cohorts by FTD date or enrollment. Metrics: Retention, ARPU surrogate (if Revenue is not in GA4, read the proxy through events).

6. 4. Segment Overlap

Crossing audiences: new vs. returning traffic, VIP vs. regular, paid vs. non-paid.

7) Attribution in GA4

Data-driven (DDM) - default. For media sharing, compare with Last click and First click in Advertising workspace.

See Conversion paths: where the path really begins, which channels close the conversion.

Fix the decision rule: for example, procurement is guided by DDM, but rates/cap - taking into account last-click risk.

8) Traffic quality audit and anti-fraud signals

The GA4 does not have a full-fledged anti-fraud, but there are useful indicators:

Engagement rate and Average engagement time are abnormally low.
The CTR/CR (click→reg) is high, but the CR (reg→kyc/deposit) is close to zero.
No interaction with the page (no 'scroll _ 90', 'view _ terms'), bursts at night/identical devices.
Geo/language does not match the payment method.

Response: mark the source/sub-ID with a flag, restrict traffic, turn on the server anti-bots and logs on the back side.

9) Export to BigQuery (mandatory for mature)

Why: event-level data for cohort ARPU/LTV, retention and advanced models.

What to store: raw 'events _', UTM/creative dictionaries, exchange rates, bet/pay tables.

Quick showcases:

Cohort revenue D1/D7/D30/D90 by source/creative.
Payback: cumulative ARPU of the vs. CPA cohort.
Anomaly detection: catching "broken" postbacks, delays and bursts of spam.

10) Responsable Marketing and Compliance

Separate section in event reports: 'rg _ limit _ set', 'self _ exclusion', age declarations.

Filters by region with strict rules, exclusion of incompatible channels.

Store and convey consent, do not mask the vertical.

11) Mini dashboard metrics (in GA4 or BI)

Acquisition: Sessions, New users, Cost (if end-to-end), eCPC, eCPM.

Activation: CR(click→reg), CR(reg→kyc), CR(kyc→FTD), Conversion lag.

Monetization: FTD, ARPU_D7/D30, 2nd-dep rate (if any), NGR proxy.

Quality: Engagement rate, Time on site, Bounce proxy, Fraud flags.

Tech: OS/Device/Browser errors, download speed.

12) Frequent mistakes and how to avoid them

1. No User-ID - the user path is falling apart.

2. Raw event names - 20 variants of 'deposit'. Keep the dictionary and diagram.

3. UTM chaos - it is impossible to compare channels. Enter a naming policy.

4. Only the GA4 interface - without BigQuery there will be no LTV and no normal cohort.

5. Ignore Consent Mode - attribute skew and data gaps.

6. There is no link to the back - the amounts/currency/timezone do not match, the ARPU "floats."

7. Decisions on small samples - wait for thresholds (clicks/registration), see trends.

13) 30-60-90 implementation plan GA4

0-30 days - Data base and hygiene

Describe the event scheme (BRD): names, parameters, conversions.

Enable User-ID, Consent Mode, internal traffic filters.

Mark UTM, agree on directories of sources/campaigns/creatives.

Configure event side server for'deposit _ success'.

Collect 2 Exploration: Funnel and Cohort.

31-60 days - Cohorts and attribution

Enable BigQuery export (daily).

Build showcases: ARPU_D7/D30, Payback, Retention; dashboard quality.

Compare DDM vs. Last/First click; fix the decision rule.

Configure event delay and CR anomaly alerts.

61-90 days - Forecast and operating system

Add 2nd-dep and VIP segments, audit RG events.

Introduce weekly retro by creative/source in conjunction with GA4 + BI.

Document the launch playbook, statistics thresholds, quality checklists.

14) Pre-scale checklist

Unified event/conversion schema and User-ID enabled
'deposit _ success' side server, correct 'value/currency'
UTM Guide and AutoCheck Labels
Consent Mode works; internal traffic excluded
BigQuery export and ARPU/Payback/Retention storefronts
Exploration funnels by main GEO/devices
Delay alerts and anti-fraud indicators

GA4 is not just a "visitor counter," but a framework for a cohort economy. With the right event schema, clean UTM markup, server-side payment capture, and export to BigQuery, you see which sources and creatives bring payback cohorts, where funnels are torn, and how to speed up Payback. Standardize data, use Explorations and cohorts - and turn analytics into an operational decision-making tool.