Google Analytics 4: Measure Acquisition, Engagement and Conversions

Google Analytics 4: How to Use GA4 to Drive SEO and GEO (Without Starting From Scratch)

If you already use Google Analytics, this article focuses specifically on Google Analytics 4 (commonly abbreviated to GA4) with a clear goal: to help you leverage its distinctive features to manage organic performance (SEO) and visibility in generative environments (GEO), without rehashing fundamentals already covered elsewhere.

What This Article Adds Beyond the Google Analytics Introduction (and Why It Matters)

The move to GA4 is now a done deal: Universal Analytics has not been accessible since 1^st July 2024 (interface and API unavailable, with data removed according to communications relayed in specialist guides). The question is no longer "should we migrate?", but rather: how do you make the most of an event-based model, Explorations and the BigQuery export to connect acquisition → engagement → value for organic traffic?

In other words, the challenge becomes operational: build a content-led measurement plan (landing pages, intents, micro-conversions), make interpretation reliable (consent, attribution, tracking hygiene), then industrialise analysis where needed.

What GA4 Changes in Practice for Measuring Organic Performance and User Journeys

GA4 does not simply "replace" Universal Analytics: it imposes a different way of reading performance altogether. Three shifts have a direct impact on how you manage SEO and GEO:

• Everything is an event: rather than reporting built primarily around sessions and pageviews, you analyse time-stamped interactions (e.g. scroll, click, download, form submission) along with their parameters.
• Engagement metrics become central: engaged sessions, engagement rate, engagement time, and a bounce rate understood as the inverse of engagement.
• Diagnosis often happens in Explorations: paths, funnels, cohorts and ad hoc segmentations that are better suited to "content → progression → conversion" questions.

Understanding GA4: The Event-Based Model and Its Impact on Your Analysis

GA4 Properties, Data Streams and Tagging: The Foundations You Need to Know

GA4 is built around an account → property → data stream structure. The key difference from Universal Analytics is that views disappear in favour of data streams. There are typically three stream types: web, iOS and Android. In practice, for a B2B website, a single web stream is often sufficient—provided governance is clear (who changes what, how changes are validated, and how everything is documented).

Technically, collection begins when you deploy a tag (via gtag.js or Google Tag Manager). The web stream provides a measurement ID in the format G-XXXXXXXXXX, used to send events to the property.

GA4 Events, Parameters, Users and Sessions: Practical Definitions for SEO and GEO

In GA4, an interaction you want to measure is an event defined by:

• an event name;
• parameters that provide context (URL, content type, variant, placement, etc.).

This is particularly useful for SEO and GEO, because you can connect:

• the landing page (content) and the likely intent (URL pattern, topic);
• engagement signals (scroll, engagement time, strategically important internal clicks);
• micro-conversions (downloads, clicks to a pricing page, form starts, booking a call).

Two practical notes help avoid misleading interpretations:

• Sessions still exist, but they no longer structure every analysis. They remain useful for certain reports, particularly those covering acquisition.
• Some events are automatic or come from enhanced measurement. For example, the scroll event (enhanced measurement) fires once per page when a user passes 90% of the page height: it is an indicator, not proof of attentive reading.

Conversions and Key Events: Think "Value" Rather Than "Pageviews"

GA4 encourages a value-led approach: identify a handful of events that genuinely correlate with business outcomes, then mark them as key events (historically referred to as conversions across many resources). For B2B sites, where a sale is rarely immediate, the discipline involves separating:

• primary conversions (e.g. demo request, booking a meeting);
• micro-conversions (e.g. downloads, email clicks, viewing a pricing page), which help measure intent and prioritise content decisions.

To choose the right KPIs, apply one simple principle: every metric should drive a decision (optimise a page, improve a CTA, remove friction, strengthen internal linking) within a clearly defined time horizon.

GA4 vs Universal Analytics: The Differences That Still Matter in 2026

Event-Based Measurement vs Session-Led Logic: What Changes for KPIs

The most structural difference is straightforward: Universal Analytics prioritised sessions and pageviews (with events bolted on), whereas GA4 starts from events and rebuilds session-style views when useful.

The direct consequence for your SEO KPIs:

• Bounce rate no longer plays the same role. In GA4, it is read as the inverse of engagement rate (non-engaged sessions). This encourages managing performance through engagement rate and engagement time instead.
• Analysis of "organic traffic quality" becomes more precise when you instrument events that demonstrate intent (click to a product page, click to pricing, form submission), rather than relying on pageviews alone.

Standard GA4 Reports vs GA4 Explorations: When to Switch to Advanced Analysis

Standard reports follow the lifecycle structure (acquisition, engagement, monetisation, retention). They are sufficient for regular monitoring—particularly if you maintain a clear dashboard.

Move into Explorations when you have a precise optimisation question, for example:

• "Do organic visitors who land on the blog click through to product pages?"
• "Which sequence of events most often precedes a demo request?"
• "Where does the journey break down on mobile?"

Explorations (paths, funnels, cohorts) are designed for this kind of ad hoc diagnosis and help you avoid managing by gut feel.

BigQuery Export: What It Enables (and What It Does Not Fix)

The BigQuery export is one of the major differences commonly associated with GA4. The benefit is clear: access more granular data, maintain history, query in depth, combine with other sources and automate quality checks.

However, BigQuery does not solve upstream problems: a vague tagging plan, poorly managed consent, inconsistent UTMs or duplicate tags will simply produce inconsistent data at scale. Before you industrialise, you need to make collection reliable first.

Universal Analytics Migration: Moving to GA4 Without Losing SEO Clarity

Migration Checklist: Properties, Streams, Access and Governance

If your migration is already complete, this checklist is primarily useful for confirming that your setup remains manageable and usable over the long term:

• One GA4 property per website (except for justified multi-brand or multi-context cases).
• A correctly configured web stream, with the measurement ID used in the right container and environment.
• Permissions: who can create or modify events, publish GTM changes, and edit the report library.
• Documentation (tagging plan, naming conventions, event dictionary, parameters, objectives).

Rebuilding a Content-Led Measurement Plan: Pages, Intent and Micro-Conversions

To avoid an unfocused catch-all view, start from content and intent. A straightforward method:

• Group your pages by URL patterns (e.g. /blog/, /solutions/, /resources/…).
• Define one to three objectives (primary conversions) and a few micro-conversions per content type.
• Instrument interactions that demonstrate progression (clicks to pricing, clicks to a case study, downloads, submitted forms).

This approach is especially valuable as search behaviours evolve (zero-click searches, AI overviews). For broader context, you can refer to our SEO statistics, which document, among other things, the significant share of searches that result in no click and how usage patterns are shifting.

Preventing Number Mismatches: Attribution, Thresholds, Consent and Comparisons

Differences between your GA4 figures and other sources (CRM, back office, server logs, Search Console) are not automatically an anomaly. They are often explained by:

• consent (partial data, modelling);
• attribution (windows, models, touchpoints);
• implementation changes (broken GTM triggers, duplicate tags, untagged pages);
• non-like-for-like comparisons (seasonality, campaigns, UX changes).

To make sound decisions, compare equivalent time periods and validate data collection quality (real-time view, DebugView, event consistency) before interpreting any trend line.

Setting Up Events in GA4: A Reliable Method for SEO and Lead Generation

Choosing Between Automatic, Recommended and Custom Events

A robust approach is to move from standard to bespoke:

1. Automatically collected events (e.g. first_visit, session_start, user_engagement).
2. Enhanced measurement (toggled on at the web stream level): file downloads, video engagement, internal search, outbound clicks, and so on.
3. Recommended events where they match your use case (e.g. login with the method parameter).
4. Custom events for your specific business actions (CTAs, form steps, strategically important internal clicks).

Important: if you already track an event via GTM, avoid leaving an equivalent automatic measurement enabled, as this can create duplicates or competing definitions.

Structuring Event Parameters: Naming Conventions and Long-Term Consistency

The value you get from GA4 depends heavily on parameter quality. A few rules that stand the test of time:

• Stable names (avoid frequently renaming a key event—otherwise trends become unreadable).
• Decision-led parameters (e.g. cta_location, content_type, form_step, asset_name) rather than unworkable levels of detail.
• Register custom dimensions: if you send a parameter, you often need to register it as a custom dimension (event or user scope) to use it in reports. Some GA4 screens cite a delay of up to 24 hours before the dimension becomes available.

On the subject of limits, a frequently cited constraint is that an event can carry up to 25 custom parameters, which encourages prioritisation and restraint.

Declaring a Conversion: Rules, Windows and Common Pitfalls

Technically, marking a conversion (or key event) is straightforward, but it requires a clear framework:

• Business rule: what genuinely proves intent (a submission, a click, a thank-you page view, etc.)?
• Uniqueness: avoid counting the same action twice (e.g. a click plus a thank-you page without deduplication).
• Validation: check in Real-time and DebugView before drawing conclusions, then allow reports to stabilise.

B2B Examples: Form, Meeting Booking, Download, Email/Phone Click

• Form: use form_submit (or a more reliable custom event) with parameters form_id, lead_type and content_group.
• Meeting booking: fire book_meeting on success (confirmation), not on the initial click.
• Download: use file_download if it suits your file formats; otherwise use a custom event with asset_name and asset_category.
• Email/phone click: use dedicated events (e.g. click_email, click_phone) with page_location and cta_location to identify which pages are generating intent.

GA4 Reports: Analysing Organic Acquisition and Traffic Quality

Correctly Reading Channels, Source/Medium and Campaigns (UTMs)

For SEO, the first priority is isolating the organic channel and avoiding attribution confusion. Strict UTM discipline is essential: without it, some traffic ends up labelled as "direct" or dispersed across vague referral sources.

If you also run paid campaigns, maintain consistent conventions so you can compare performance cleanly against your SEA statistics and acquisition trends, without conflating content effects with campaign effects.

Landing Pages and Content Performance: Finding What Starts a Journey vs What Closes It

To avoid overvaluing end-of-journey pages, separate:

• landing pages: pages that initiate a session (often SEO content);
• progression pages: pages that move visitors towards the offer (case studies, solutions, pricing);
• conversion pages: confirmation pages, meeting booking pages, and so on.

A page may generate few direct conversions yet play a major role in assisted conversions. GA4 brings this multi-touchpoint perspective more to the fore, helping you decide which content to strengthen (internal linking, CTAs, updates, consolidation).

Connecting Insights to Search Console: Queries, Pages and Intent

GA4 describes what happens after the click, while Search Console describes performance within Google (impressions, clicks, queries, positions). Linking the two is often the quickest route from "query → page → engagement → conversion".

To frame this logic, the article on Search Console and Google Analytics details the complementarity between the two tools and the interpretation pitfalls that arise when you mix exposure metrics (SERPs) with behavioural metrics (on-site).

GA4 Explorations: Diagnosing Journeys and Prioritising Optimisations

Path Exploration: Identifying Sequences That Precede a Conversion

Path exploration is particularly effective for answering a very practical SEO question: "What do organic visitors do after landing on a specific page?"

You can start from an event (session start, pageview, a strategic internal click) and observe the branches that follow. The goal is not to "see the whole site", but to identify recurring patterns: loops, unexpected exit points, and paths that most frequently precede a key event.

Funnels: Measuring Friction and Mapping Stages (MQL, SQL)

Funnels become useful as soon as you have an expected journey—even a simple one:

• SEO content landing → solution page visit → pricing view → form submission.

In B2B, you can translate marketing stages (MQL, SQL) into observable events, then measure step-to-step rates. The objective is to identify where drop-off occurs (mobile vs desktop, content type, country or region, source) and test a targeted improvement.

Segments and Comparisons: Separating SEO, Campaigns and Strategic Audiences

Segments help you avoid drawing global conclusions from aggregate data. The same content may perform well on average but poorly for a critical segment (mobile users, a specific region, a highly engaged audience). In GA4, segment by:

• channel (organic vs others);
• page type (cluster, pillar, local);
• geography (particularly useful for GEO);
• engagement level (engaged sessions, scroll depth, micro-conversions).

GA4 Audiences: Building Actionable Segments for SEO, GEO and Retargeting

Intent-Based Audiences: Content Viewed, Depth and Engagement

A useful audience is not "all blog visitors", but a group defined by intent signals. Effective B2B examples include:

• users who viewed two or more pieces of content from the same cluster and triggered an internal click to a solution page;
• users who exceeded an engagement threshold and viewed a pricing page;
• users who returned multiple times (recurrence) within a thematic area.

B2B Maturity Audiences: Weak Signals vs Strong Signals

GA4 also lets you work with "strong" signals (demo requests, meeting bookings) alongside "weak" signals (reading, clicking, downloading). The key is not to confuse activity volume with genuine progress towards conversion.

According to GA4 guidance, the platform may also suggest audiences and includes predictive capabilities (purchase probability, churn prediction, revenue forecasting) in certain contexts and at sufficient data volumes. In B2B, treat these with caution: their usefulness depends heavily on tracking quality and signal volume.

Limits and Good Practice: Size, Delays, Privacy and Interpretation

Three themes come up regularly in advanced use:

• Delays: some custom dimensions and reports do not populate instantly.
• Privacy and thresholds: data may be modelled or withheld depending on consent settings and configuration, which affects segments.
• Interpretation: an audience must be tied to an action (content to improve, a journey to simplify, a retargeting campaign), otherwise it becomes a purely analytical artefact.

GEO Angle: Measuring the Impact of Generative AI Answers on Performance

Define What You Are Measuring: Visits, Engagement, Conversions and Attributable Traffic

GEO (visibility within generative answers, AI assistants and search interfaces) requires you to clarify your measurement approach. You are not simply measuring visits, but:

• attributable share (where it exists) through tagging and referrer data;
• quality through engagement and key events;
• value through micro-conversions and primary conversions.

The market context makes this increasingly important: sources such as Semrush (2025) report a significant share of searches ending without a click, which makes a simple "SEO equals sessions" view incomplete. To contextualise these trends, see our GEO statistics.

Structuring UTM Tagging to Separate Traffic From Generative Environments

To measure what comes from generative environments, you need a strict, documented UTM convention. The aim is to prevent these visits from being classified as "direct" or ending up in ambiguous referral buckets.

Good practice includes:

• standardising in lowercase, without accents and without unnecessary variations;
• tagging links inside PDFs, emails, signatures and partner content as well;
• maintaining the same naming convention over time so you can compare before and after.

Creating GEO Reports and Segments in GA4: Comparing GEO vs SEO on Consistent KPIs

To compare GEO and SEO meaningfully, use consistent KPIs: engaged sessions, engagement time, progression to a micro-conversion, and primary conversions. Then segment by source/medium/campaign (UTMs) to isolate generative environment traffic.

A common trap is comparing session volumes alone: in GEO, a source may generate fewer visits but stronger intent (or the reverse). Comparisons should focus on quality and value, not just quantity.

BigQuery and GA4: Industrialising Analysis Without Overcomplicating the Marketing Team

Practical Use Cases: Raw Data, Quality Checks, Multi-Source Analysis

The BigQuery export becomes relevant when you need:

• quality checks (spotting broken events, missing parameters, duplicates);
• custom queries (e.g. rebuilding journeys, analysing granular cohorts);
• multi-source joins (for example, linking acquisition and conversion signals if your data governance allows it).

The GA4 Data Model: What to Understand to Avoid Misleading Queries

Two ideas prevent many common errors:

• An event is not a pageview: page_view is just one event among many, and it may fire on URL changes without a full page reload (common in single-page applications).
• Parameters must be interpreted with scope in mind: confusing a user-scoped value with an event-scoped value can produce inconsistent aggregations.

In practice, before writing any "definitive" queries, validate your assumptions in GA4 (DebugView, Real-time, Explorations) to avoid industrialising a definition error.

Governance Best Practice: Documentation, Versioning and Reproducibility

If you industrialise your analysis, document systematically:

• your event list (and status: auto, enhanced measurement, custom);
• parameters and their meaning;
• changes over time (versioning);
• attribution and comparison rules (windows, segments, exclusions).

Without this, reporting becomes fragile the moment a tag changes or a new stream is added.

Compliance, Consent and Data Quality: Keeping Your Numbers Usable

GDPR, Retention and Privacy Settings: Key Considerations in France and the EU

Across France and the EU, audience measurement must align with compliance requirements. GA4 promotes a privacy-first approach (including not storing IP addresses and offering retention and collection controls). This does not remove your obligations: purposes, retention periods, proof of consent, access governance and deletion requests all still apply.

For a structured overview of the considerations and settings involved, refer to the article on GDPR, which covers analytics-specific points to watch.

Consent Mode, Modelling and Thresholds: How to Read Incomplete Data

When some users refuse consent, your figures become partial. GA4 may use modelling to fill certain gaps, and some reports can apply thresholds (particularly for sensitive dimensions). The SEO and GEO implication: do not treat a small dip as ground truth without first checking whether consent settings, your consent management platform, or your configuration have changed.

Tracking Hygiene: Internal Traffic, Self-Referrals, Cross-Domain and Duplicates

The most costly data-quality issues are often the simplest:

• internal traffic not excluded (office networks, VPNs);
• duplicates (a tag installed twice, or enhanced measurement and GTM both tracking the same event);
• self-referrals (poor cross-domain configuration, payment gateways, unconfigured subdomains);
• polluted referrals (spam or secondary domains).

Before making any SEO decision, verify that your key events are still firing (a website update can break a trigger) and that attribution has not been degraded by a tagging issue.

Centralising GA4, Search Console and Performance Tracking With Incremys (via API)

What the Integration Delivers: Unified Visibility, 360° SEO Management and ROI-Led Reporting

For many teams, the challenge is not a lack of data but its fragmentation. Incremys can connect to GA4 and Search Console via API to centralise acquisition signals (queries, pages, rankings) alongside behavioural and conversion signals (events, engagement), helping you relate SEO and GEO performance to ROI estimates without relying on manual exports.

When to Use the Performance Reporting Module to Automate Monitoring and Dashboards

As soon as you are tracking groups of pages (clusters, pillar pages, local pages) and want recurring reporting, automation becomes genuinely valuable. The Performance Reporting module is designed precisely for this: structuring ongoing monitoring, stabilising comparable indicators over time, and reducing friction between analysis and action.

Google Analytics 4: Frequently Asked Questions

What is Google Analytics 4?

Google Analytics 4 is the current version of Google Analytics. It is built on an event-based data model: every interaction you measure (pageview, scroll, click, download, purchase, form submission) is recorded as an event, enriched with parameters, and then surfaced through reports and Explorations.

What are the main differences between GA4 and Universal Analytics?

• Events vs sessions: GA4 starts from events; Universal Analytics was more centred on sessions and pageviews.
• Structure: data streams (web, iOS, Android) replace the views-based model.
• Analysis: GA4 emphasises Explorations (paths, funnels, cohorts) and engagement-led metrics.
• Scaling analysis: the BigQuery export is a major lever for advanced analysis and quality control.

How do you succeed with a Universal Analytics migration without losing SEO visibility?

In 2026, the priority is mainly preserving clarity after migrating: document your measurement plan, rebuild decision-led KPIs (engagement, micro-conversions, conversions), stabilise UTM conventions, and check tracking hygiene (internal traffic, duplicates, cross-domain) before interpreting any SEO trend.

How do you set up events in GA4?

Work step by step: enable automatic collection and enhanced measurement first, then add custom events via GTM for business-specific interactions (CTAs, forms, strategically important internal clicks). Validate in Real-time and DebugView, then register the required parameters as custom dimensions so you can use them in reports.

What is the difference between events, key events and conversions?

An event is a measured interaction (e.g. a click, scroll or form submission). A key event (often treated as a conversion in everyday usage) is an event you prioritise because it represents business value—whether a primary conversion or a micro-conversion. The difference lies in the value you assign to it, not in its technical nature.

How do you choose the right analytics KPIs for SEO and GEO?

Choose KPIs that connect acquisition to value: organic sessions, engagement rate and time, progression to micro-conversions, primary conversions and assisted conversions. To structure this approach, the article on KPIs helps you build a decision-making dashboard rather than simply tracking vanity metrics.

Why do my GA4 figures differ from Search Console?

Search Console measures performance within Google (impressions, clicks, queries), whilst GA4 measures on-site behaviour after the click. Differences also arise from consent, attribution, redirects, tagging and definition mismatches. The guide on Search Console and Google Analytics explains the common causes and how to compare the two sources properly.

How do you analyse organic acquisition in GA4 (source/medium, channels, landing pages)?

Start by isolating the organic channel, then analyse landing pages and their contribution to progression (internal clicks, micro-conversions). Ensure your UTMs are consistent so that untagged links or campaigns do not distort your interpretation (traffic appearing as "direct" or ambiguous referrals).

How do you create useful GA4 Explorations (paths, funnels) to optimise content?

Begin with a specific question (e.g. "Which content leads to demo requests?"). Then build a path exploration to identify common sequences, or a funnel to measure friction between steps. Filter by segment (organic, mobile, geography) to avoid being misled by averages.

What are GA4 audiences used for, and how do you define them for a B2B website?

Audiences group users according to behaviour and intent signals. In B2B, prioritise audiences based on viewing content within a cluster, depth of engagement, and progression towards micro-conversions (pricing pages, downloads, case study clicks), rather than overly broad segments.

How do you track the impact of generative AI answers (GEO) in GA4?

Define a dedicated UTM convention to identify these sources where possible, then compare GEO vs SEO using consistent KPIs (engagement, micro-conversions, conversions). Use dedicated segments and reports, and monitor how consent settings and thresholds affect data availability.

What is the BigQuery export from GA4 used for?

The BigQuery export provides access to more detailed data, supports custom analysis, automates quality checks and enables multi-source data joins. It does not replace a solid measurement plan: without stable event and parameter conventions, industrialised analysis remains fragile.

Is GA4 GDPR compliant, and which settings are essential?

GA4 includes privacy-oriented features (collection controls, data retention settings, deletion capabilities, and non-storage of IP addresses as stated in its documentation). Compliance nonetheless depends on your implementation (consent, purposes, documentation, governance). For further detail, see the article on GDPR.

How do you reduce attribution errors (direct, referral, self-referrals)?

Standardise your UTMs, clean unwanted referrers, configure cross-domain tracking correctly, exclude internal traffic, and avoid duplicate tags. If something looks off, check the implementation first (published GTM container, triggers, consent configuration) before interpreting any apparent SEO drop.

How do you build a reliable dashboard for SEO and GEO tracking?

A reliable dashboard highlights three to five decision-level KPIs (level one) alongside diagnostic indicators (level two). It should connect acquisition (organic, tagged GEO traffic), engagement (engaged sessions, time on site) and value (micro-conversions, conversions), all with stable, consistent definitions.

When should you consider an alternative analytics solution, and how do you compare properly?

An alternative may be worth considering when constraints around hosting, governance, data minimisation or consent make implementation difficult, or when you need a different level of control. To frame the comparison without confusion, the article on Matomo vs Google Analytics sets out structured criteria covering data ownership, consent, tracking flexibility and limitations.

To keep building your data-driven approach to performance management and content optimisation, explore the Incremys Blog.