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Understanding Google Tag Manager: Tags, Triggers and Variables

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Last updated on

22/2/2026

Chapter 01

Google Tag Manager: Deploying Reliable Tag Management for SEO, GEO and AI Visibility

Implementing clean tracking is not about stacking scripts throughout a website's code. The real challenge is building measurement that is governed, testable and scalable. That is exactly what Google Tag Manager enables: it centralises and controls tracking tags (analytics, conversions, pixels, consent scripts) without having to edit source code every time something changes, thanks to a single interface and robust versioning.

This approach becomes even more critical as the ecosystem grows more complex: more zero-click searches, fragmented cross-device journeys, tighter consent requirements, and the rise of visibility within generative answers. In this context, a robust tagging plan helps connect acquisition, engagement and conversion, and supports SEO and GEO decisions with reliable data.

What Google Tag Manager Is Used for, and When It Becomes Essential

A tag manager acts as a control centre for deploying, updating and removing tags (small snippets of code) on a website or app, without constantly relying on development teams. In practice, it allows you to:

deploy analytics tags (for example, for Google Analytics 4) and custom events (clicks, forms, scroll depth, and so on);
measure advertising conversions (Google Ads and third-party pixels) with precise triggering rules;
test and publish changes with version control and preview mode;
reduce the risk of duplicates and scripts scattered across the <head>, <body> and footer.

It becomes essential as soon as you need to pass data between your site and multiple platforms, or whenever tracking must evolve quickly (campaigns, redesigns, new forms, e-commerce, A/B testing, consent management).

Tags, Google Tag and Tag Management: Clarifying the Terms to Avoid Duplicates

Since 2025, "Google tag" has referred to Google's unified tag (GA4 or Google Ads ID), designed to simplify configuration and reuse of settings. A tag manager remains the orchestration layer: it decides when and under what conditions that tag (and others) will fire.

The main watch-out is double implementation. If your site loads a tag directly in the code and then loads it again via the container, you risk duplicating page views, events or conversions. The rule is simple: one implementation source per tag, documented and tested.

Google Tag Manager vs Google Analytics: Orchestrating Tracking Without Confusing Collection and Analysis

The most common confusion is between a tag manager and an analytics tool. A tag manager triggers and sends data (it "delivers"), whilst analytics receives, stores and surfaces it through reports.

Key point: a tag manager does not "measure" in a reporting sense. It orchestrates scripts. Analysis then happens within the analytics tool (events, conversions, attribution, segments, and so on).

To go deeper into this distinction and its implications (responsibilities, scope and frequent mistakes), see Google Tag Manager vs Google Analytics and Google Tag Manager and Google Analytics.

What You Will Put in Place: Installation, Governance and Measurement Quality

This guide covers core concepts (container, tags, triggers, variables), a safe installation method, key use cases (GA4, Google Ads conversions, advertising pixels, Hotjar), and best practice for using these data to inform SEO and GEO. The goal is not to add "more tracking", but to capture signals that are usable, compliant and stable over time.

Google Tag Manager Fundamentals: Container, Tags, Triggers, Variables and the Data Layer

It works on a straightforward model: a container (installed once on the site) holds tags. Each tag fires based on a trigger and uses variables to retrieve values (URL, clicked text, product ID, and so on). For dynamic sites, the data layer provides a stable, reusable signal.

The GTM Container: Scope, Environments, Access Rights and Publishing Logic

The container is the deployment unit: in practice, a common rule is "one site = one container". You work in a workspace and then publish. Every publish creates a new version, which makes rollback straightforward if something breaks.

To industrialise your setup, environments (for example staging and production) let you test without impacting real users. Access rights support governance: limiting publishing to accountable roles reduces the risk of undocumented changes.

Tags: Types, Use Cases and Deployment Best Practice

A tag is an action: sending an event to GA4, declaring an Ads conversion, loading a third-party pixel, or running custom HTML/JavaScript. Common examples include:

analytics tags (page views, events, e-commerce);
advertising tags (conversions, remarketing);
UX measurement tags (heatmaps, session recording);
consent-related tags (conditional firing based on user choices).

A structuring best practice is to separate "configuration" (base tags) from "events" (business actions). As a container grows, this separation makes audits and maintenance far easier.

Triggers: Conditions, Priorities and Firing Rules

A trigger defines when a tag fires: page load, click, form submission, custom event, scroll, timer, and so on. Strong triggers rely on stable criteria (ID, attribute, data-layer event) rather than brittle ones (text that may change, DOM structure that gets refactored).

For a "Request a demo" button, use a dedicated ID (e.g. demo-btn) or a data-layer event rather than broad "all element clicks" rules.

Variables: Built-In, Custom and Standardising Parameters

Variables add precision. They can be used to:

filter triggers (e.g. fire only if the URL contains /pricing);
enrich events (e.g. send button_text, form_name, plan);
standardise parameters (e.g. set a fixed currency via a constant variable).

Start by enabling useful built-in variables (Page URL, Click ID, Click Classes, Form ID, etc.), then add custom variables when you need business mapping (lookup tables) or values coming from the data layer.

The Data Layer: Clean, Reusable Data for Robust Events

The data layer is a structured layer that your site pushes as objects (e.g. event, form_name, plan). It stabilises measurement because tracking is less dependent on design and the DOM. For a dedicated deep dive, see the data layer.

Illustrative example of a site-side push:

dataLayer.push({ event: "demo_request", form_name: "demo", plan: "pro"});

You then create data-layer variables for form_name and plan, a Custom Event trigger for demo_request, and a GA4 tag plus an Ads tag, both firing on that same event.

Event Schema: Conventions, Naming and Governance

To avoid an unreadable container, it helps to define an event schema (often called a taxonomy) before adding dozens of triggers. The idea is to define a short list of business events (e.g. lead_submit, demo_request, pricing_view) and a stable parameter dictionary (e.g. form_name, content_type, cta_location).

Two pragmatic governance rules:

an event should describe an action, not a page (avoid duplicating page views as events);
a parameter must keep the same meaning everywhere (avoid plan meaning "offer" on one page and "subscription" on another).

Installing Google Tag Manager Properly: Scripts, Validation and Checks

Good installation comes down to two principles: place snippets in the correct locations and validate before publishing. For a dedicated walkthrough, see how to install it step by step.

Create an Account and Container: Web, App and Initial Options

Create an account (organisation level), then a container (site or app level). For websites, choose "Web". Use clear naming (domain + environment, e.g. example.com - prod). If you use multiple environments, define a publishing flow (staging first, then production).

Add the GTM Snippets: Placement and Common Mistakes to Avoid

After creation, the interface provides two snippets. The first belongs in the <head> (as high as possible), the second just after the opening <body> tag (the fallback noscript version). For full details, see the article on the implementation script.

Common mistakes to avoid:

using the wrong container ID (wrong environment or wrong site);
forgetting the <body> snippet;
leaving old tracking hard-coded whilst also reinstalling it via the container (duplicates);
publishing without testing (lost conversions, inconsistent events).

Validate the Installation: Preview Mode, Diagnosis and First Publish

Before adding complex tags, confirm the container loads and that Preview mode works correctly. This dedicated guide explains how to test your setup and diagnose firing issues.

A sensible starting point: implement a base tag (such as GA4 configuration), verify it appears in real-time reports and DebugView within analytics, then add business events.

Building a Tagging Plan for SEO, GEO and Conversion

A tagging plan (or tracking plan) defines what you measure, why you measure it and how you will use it. Without this framework, containers bloat, triggers overlap and data reliability suffers.

Define Tracking Objectives: Key Pages, Events, Micro-Actions and Conversions

Start by linking measurement to outcomes:

macro conversions: demo requests, quotes, purchases, booked meetings;
micro-actions: clicks on key CTAs, opening a form, viewing a pricing page, downloads, scrolling to strategic sections;
quality signals: engagement, visit depth, paths between entry pages and high-intent pages.

This prioritisation avoids "track everything" and focuses collection on signals that genuinely help SEO, GEO and conversion performance.

Naming Conventions and Documentation: Keeping a Container Readable Over Time

Adopt consistent naming:

tags: GA4 - event - lead_submit, Ads - conversion - lead_submit;
triggers: CE - demo_request (custom event), CLK - demo_btn;
variables: DLV - plan, CONST - currency.

Document each published version (what changed, why, and where). Traceability makes audits easier and speeds up troubleshooting when measurement breaks.

Data Reliability: De-duplication, Event Consistency and Reducing Signal Loss

Three checks dramatically improve quality:

de-duplication: one event = one stable trigger = one tag, with no parallel hard-coded implementation;
parameter consistency: the same field names (e.g. form_name, page_location), formats and non-empty values;
robustness: prioritise the data layer over DOM-based triggers, especially during frequent redesigns.

To reduce signal loss, remember that external factors can affect collection (consent, blockers, browsers, front-end errors). The objective is to minimise artificial variation caused by unstable implementation.

Core Use Cases with Google Tag Manager: GA4, Ads, Third-Party Pixels and Hotjar

The use cases below cover most performance needs: measuring audience and engagement, attributing advertising conversions, and understanding user experience.

Deploying GA4 via GTM: Base Configuration and Robust Collection

For measurement, GA4 is the foundation (see also Google Analytics). A container lets you configure the base tag cleanly and add custom events that reflect business actions, not just page views.

The goal is to send clean, stable events enriched with useful parameters (content category, form type, selected offer, and so on). That makes analysis actionable when deciding editorial and optimisation priorities.

Tracking Clicks, Forms, Scroll and Interactions: Useful Custom Events

A practical B2B example: track the click on "Request a demo" and the actual form submission separately. Clicks help identify form friction (many clicks, few submissions). Submissions become your primary conversion. You can then segment by entry page, channel (organic vs campaign) or content type.

Measuring Google Ads Conversions: Configuration, De-duplication and Attribution

Ads conversion tracking demands strict discipline because errors can become expensive quickly (algorithmic optimisation on bad data, misleading attribution). For context on how SEA metrics and budgets evolve, see the benchmarks in SEA statistics.

Best practice:

fire conversions on robust signals (ideally a data-layer success event);
avoid conversions based only on a page view (false positives);
check the conversion is not already installed elsewhere (de-duplication);
test the full journey before publishing.

Third-Party Advertising Pixels: Firing Control and Performance Impact

Third-party pixels (social networks, B2B platforms, and so on) are typically implemented via tag templates or custom HTML. The key question is not only "does it work", but "does it fire at the right moment" and "does it respect consent".

On performance: the container loads asynchronously, but accumulating scripts can still degrade the user experience. Keep your container lean, regularly audited, and disable any unused tags.

Installing Hotjar: Targeting, Triggering and Protecting User Experience

Heatmaps and session recordings can be valuable for diagnosing friction points (pricing pages, forms, navigation). However, they carry a potential cost to performance and compliance. Restrict firing:

to a traffic sample where possible;
to key pages (pricing, checkout, forms);
after consent, if required by your chosen purpose.

Also exclude internal environments (staging) and team traffic to avoid polluting your analysis.

Advanced Data-Layer Scenarios: E-commerce, Basket, Funnel Steps and Product Data

In e-commerce (or multi-step funnels), the data layer becomes central: product ID, value, currency, funnel step, customer type, and so on. Rather than inferring data from the DOM, you receive it explicitly, improving attribution accuracy and performance analysis by segment.

This structure also makes change easier: if the data layer remains stable during a front-end redesign, most triggers and tags remain intact.

Measurement for SEO Steering: Linking Behaviour, Content and Acquisition

To steer SEO effectively, you need two complementary data families: visibility in Google (impressions, clicks, queries) and on-site behaviour (engagement, conversions). The first comes from Search Console; the second comes from analytics. Well-configured tracking helps connect both without mixing scopes.

The Role of Google Tag Manager in an SEO Strategy

It provides a governed implementation layer: tracking key events (engagement, internal clicks, forms), measuring organic conversions, and enabling iteration without requiring a development cycle for every adjustment. These data then inform your SEO decisions.

For market context and benchmarks, see the figures gathered in SEO statistics.

Linking Content to Usage: Engagement, Navigation, Clicks and Paths

SEO is not only about rankings. You need to understand what visitors do after landing: which pages hold attention, which sections attract clicks, and where people drop off. Custom events (CTA clicks, scrolling to key blocks, FAQ interactions) provide strong signals to improve content structure and clarity.

Prioritising Optimisations: Entry Pages, Friction Points and Contribution to Conversion

A straightforward method is to cross-reference SEO entry pages with business outcomes. You can identify:

pages that attract traffic but convert poorly (intent mismatch, unclear value proposition, UX friction);
pages that convert well but lack visibility (SEO opportunity);
paths where engagement drops (internal linking, content quality or speed issues).

This becomes increasingly important as zero-click searches grow, making it crucial to optimise both visibility and conversion on the sessions you do earn.

Search Console and Tracking: Combining Uses Without Mixing Data Sources

Google Search Console measures performance in search results (impressions, clicks, CTR, rankings). Analytics measures what happens on your site. A tag manager primarily supports the on-site side (triggering tracking) and does not replace Search Console.

To see how they work together in a clean setup, read Google Search Console and Google Tag Manager.

Why the Numbers Differ Between Search Console and Analytics

Differences are normal because definitions and scope differ: Search Console counts clicks from Google, whereas analytics counts sessions and events on the site, with impacts from consent, ad blockers, redirects, processing delays and attribution models. For a detailed explanation, see the difference between Search Console and Google Analytics.

Structured Data: Part of Your SEO Strategy, Not a Substitute for Tracking

Structured data (schema) helps search engines understand content and can enhance presentation in results (rich snippets). It does not replace tracking, and tracking does not replace structured data. In some cases, you may inject JSON-LD via a custom HTML tag, but this should remain controlled and fully tested. For more, see structured data.

GEO: How Google Tag Manager Fits into an AI Visibility Strategy

GEO focuses on optimising visibility in generative engines and assistants (synthesised answers, enriched extracts, recommendations). Measurement becomes essential here: without behavioural and conversion signals, it is difficult to connect AI visibility to tangible business impact.

What You Can (and Cannot) Measure About Visibility in Generative Answers

You can reliably measure what happens after users land on your site: landing pages, engagement, events, conversions and retention. By contrast, measuring precise exposure within a generative answer (who saw what, how often, in what context) is often limited because these interfaces do not always provide standardised reporting.

As a result, the most robust approach is to treat AI visibility as an acquisition lever and analyse landing behaviour, visit quality and contribution to outcomes (leads, pipeline, sales) over coherent time windows.

Instrumenting LLM-Driven Journeys: Landing Pages, Behaviours and Conversions

To instrument traffic coming from assistants or generative engines, you need two building blocks:

identifiable landing pages (guides, comparisons, solution pages, proof pages);
events that describe the journey (reading, navigation to high-intent pages, interactions, conversion).

At minimum, track: the entry page, a visit to a high-intent page (pricing or contact), clicks on major CTAs, then the outcome (submitted form or booked meeting). This gives you a clear "acquisition → usage → result" view, even if the upstream "AI" source remains partly opaque.

Defining GEO Events: Intent Signals, Visit Quality and Business Value

To make GEO analysis actionable, organise events into three layers:

visit quality: reading a key section, scroll depth on a guide, table-of-contents clicks, meaningful time on page rather than a raw duration figure;
intent: viewing pricing, clicking towards product pages, downloading decision-oriented content, interacting with objection-handling FAQs;
business value: demo request, quote, account creation, purchase, booked meeting.

This structure reduces noise (events without business meaning) and makes analysis by topic or content type possible, which is especially useful when publishing at high frequency.

Setting Up a GEO Taxonomy via the Data Layer: Content, Entities and Topics

The data layer lets you add editorial and semantic attributes to tracking without relying on fragile HTML structure. Useful parameters include:

content_type (guide, comparison, solution page, case study);
topic or theme;
entity (product, service, problem);
funnel_stage (discovery, consideration, decision).

In practice, you push these values on page load (or view change for SPAs), then reuse them in key events. This enables performance comparisons "by entity" or "by topic", rather than relying on instinct alone.

Analysing GEO Impact: Pipeline Contribution, Retention and Editorial Performance

An effective SEO content strategy relies on usage data: identifying which topics attract qualified traffic, which formats sustain attention, and which pages contribute to pipeline. GEO adds the need to create structured, explicit, answer-led content whilst proving its usefulness through measurable behaviour.

For additional context and benchmarks, see GEO statistics.

Cookies, Consent and Compliance: Securing Data Collection

Compliance is not an abstract legal concern: it determines when tags may fire and whether measurement remains consistent over time. A clean container includes consent rules, a cookie inventory and controlled publishing.

GDPR: Consent, Conditional Firing and Measurement Impacts

Under GDPR, some tags must not fire before consent is given. This directly affects event and conversion volumes and makes time-based comparisons more complex. Your tagging plan should distinguish:

strictly necessary tags;
measurement tags (analytics);
marketing tags (advertising, remarketing).

You then apply conditional triggers based on user choices.

Mapping Cookies: Purposes, Retention and Documentation

A cookie map (see cookies) helps link "a tag" to "a purpose". At minimum, document: cookie name, purpose, retention period, recipient platform, firing conditions and legal basis (within your compliance framework).

Reducing Risk: Access Control, Validation and Publishing Rules

Reduce operational risk through:

restricted publishing rights;
a systematic review before production release;
a clear description for every published version;
regular audits of active tags (remove anything no longer in use).

Testing, Debugging and Maintaining a Clean Google Tag Manager Container

Reliable measurement depends less on the initial install and more on your ability to test and maintain. Every new tag is a potential risk if it is not properly controlled.

Preview and Diagnosis: Validating Tags, Triggers and Variables

Preview mode is your best ally: reproduce the action (click, form submit), then check:

whether the tag appears under "Tags Fired";
whether the trigger conditions matched as intended;
whether variables (URL, IDs, data-layer fields) are non-empty and correctly formatted.

This "timeline → tags → variables" workflow speeds up diagnosis and reduces risky releases.

Version Management: Audits, Traceability and Rollback

Every publish creates a version. Use it as a logbook: name each version, describe additions and removals, and list impacted pages or journeys. If measurement breaks (for example a conversion drop), compare deployment dates and roll back to a stable version whilst you investigate.

Performance and Reliability: Limiting Scripts, Preventing Regressions and Monitoring Errors

Overloaded containers can harm performance and complicate debugging. Maintain strict hygiene: remove obsolete tags, avoid overly broad triggers, and prefer conditional execution wherever possible.

Test Plan: Pre-Release Checks and Post-Deployment Controls

Before release: Preview test, full journey test, parameter validation, duplicate checks. After release: real-time checks in analytics, conversion verification, and volume review over several days to catch any anomalies.

Redesigns, Migrations and Removal: Avoiding Measurement Breaks

Website redesigns rarely "break SEO" by magic: they more often break measurement (DOM triggers, JavaScript forms, changed journeys). Without reliable data, you are effectively flying blind.

Pre-Redesign Checklist: Dependencies (GA4, Ads, Pixels) and Business Impact

inventory all active tags (analytics, conversions, third-party pixels, consent);
identify fragile triggers (those based on CSS classes or DOM structure);
define a data-layer contract for critical events (leads, purchases, funnel steps);
prepare a test environment and plan post-launch validation.

Removing Google Tag Manager Properly: Script Removal, Tag Clean-Up and Verification

To remove it cleanly, do not simply delete the snippet: first identify everything that depends on the container (GA4, Ads, pixels, consent scripts). Then:

migrate essential tags to an alternative implementation (or hard-code them if you choose that route);
remove the snippet from the <head> and the noscript from <body>;
confirm essential events and conversions still come through.

Post-Deployment Controls: Conversion Continuity, Events and Data Quality

After removal or migration, monitor event volumes, conversion rates, channel consistency and any gaps in data collection. Compare with a previous period whilst accounting for consent and seasonality effects.

Alternatives to Google Tag Manager: When to Consider Another Tag Management System

Another tag management system may make sense for governance, security requirements or architecture constraints (including large-scale server-side needs). The key consideration is to weigh migration cost against the risk of measurement disruption.

Selection Criteria: Governance, Security, Server-Side, Compliance and Maintenance

governance: access rights, auditability, workflows, environments;
security: script control, validation, reduced injection risk;
server-side: performance needs and control over data flows;
compliance: consent handling, documentation, conditional firing;
maintenance: readability, technical debt, team dependency.

When to Stick with GTM: Deployment Simplicity and Ecosystem

Staying with it often makes sense when you need fast implementation, a broad template catalogue, built-in testing and accessible versioning. In most cases, performance depends less on the choice of tool and more on governance quality (tagging plan, data layer, testing and audits).

With Incremys: Turning Measurement into SEO and GEO Priorities Tied to ROI

Once tracking is reliable, the next challenge is making it useful: turning events and conversions into content decisions. Incremys supports this by centralising SEO and GEO analysis and integrating Google Analytics and Google Search Console via API within a 360° SEO SaaS approach.

Centralising Performance Data to Prioritise High-Potential Content

The Performance Reporting module helps you monitor results by integrating analytics data via API, making it easier to connect content, behaviour and outcomes. Operationally, this helps you prioritise optimisation where impact is measurable: conversion, pipeline contribution, and engagement on strategic pages.

Tracking SEO, SEA and GEO Signals to Guide Decisions and Measure Impact

To ground decisions in benchmarks and trends, Incremys resources include SEO statistics, SEA statistics and GEO statistics. The objective remains the same: measure, prioritise and iterate continuously.

Google Tag Manager FAQ

Google Tag Manager: Definition and What It Is Used for on a Website

It is a tool that lets you add, edit and remove tracking tags (analytics, conversions, pixels, consent scripts) via an interface, without modifying your site's source code for every change. It centralises management, simplifies testing and reduces errors caused by scattered scripts.

How GTM Works: Container, Tags, Triggers, Variables and the Data Layer

The container is installed once on the site. It holds tags (what runs). Each tag fires via a trigger (when it runs) and uses variables (which values are used or sent). The data layer structures stable information that the site sends to the container to trigger robust events.

What Is the Difference Between Google Tag Manager and Google Analytics?

A tag manager orchestrates firing and data sending. Analytics collects, stores and surfaces those data in reports. Use a tag manager to deploy tracking, and analytics to analyse behaviour and performance.

Avoiding Tracking Duplicates: Audits, Trigger Rules and Best Practice

Choose a single implementation method per tool (either hard-coded or via the container), document it clearly, and audit regularly to ensure scripts have not been re-added elsewhere (theme, plugin, CMS, external integrations). Then review volumes (page views, events) to detect any abnormal spikes.

Installing and Testing GTM: Script Placement, Preview Mode and Post-Publish Checks

Place the main snippet in the <head> (as high as possible). Place the noscript snippet immediately after the opening <body> tag. Before publishing, use Preview to confirm the tag fires on the right event and that variables contain the expected values. After publishing, confirm data in real-time reports and DebugView within analytics, then monitor event and conversion trends over the following days.

The Data Layer: What It Is for and When to Implement It

The data layer is used to send business information (form name, selected plan, transaction value, funnel step) in a stable, structured way. It is strongly recommended for dynamic sites, JavaScript forms, SPAs and e-commerce, because it is more resilient to design changes than DOM-based tracking.

Configuring GA4 and Ads with GTM: Custom Events and Duplicate-Free Conversions

Deploy the base GA4 tag first, then create dedicated events (e.g. lead_submit, demo_click) triggered by stable signals (IDs, data-layer events). Add useful parameters (e.g. form_name, button_text) for segmentation. For Ads, fire conversions on a real success signal (ideally via the data layer), verify there is no parallel installation (hard-coded script, plugin or another container), then test the full journey.

Hotjar and Third-Party Pixels: Minimising Performance Impact and Respecting Consent

Limit firing to key pages, use a traffic sample where possible, and avoid running scripts sitewide without a clear need. Audit the impact (loaded scripts) regularly and respect consent where required by purpose.

GDPR and Consent: Managing Cookies and Conditional Firing with GTM

Map cookies by purpose, implement conditional firing based on consent, and document your choices. Regularly review active tags to prevent accumulation and reduce compliance risk.

Search Console vs Analytics: Why Data Differ and How to Use Them for SEO

Use Search Console to steer visibility and queries, and analytics to steer engagement and on-site conversion. Do not mix metrics: they do not measure the same scope. Because definitions differ (clicks vs sessions and events) and analytics collection depends on factors such as consent, blockers, redirects and attribution models, differences do not necessarily indicate a problem, but they do require source-appropriate interpretation.

GTM and GEO: Measuring LLM-Driven Journeys and Business Value

Its primary role is instrumenting what happens on your site after acquisition: engagement events, intent signals, conversions, and editorial parameters sent via the data layer (topic, content type, entities). These allow you to evaluate visit quality and the business contribution of content, which is central when optimising visibility in generative answers.

Removing GTM Cleanly Without Breaking Measurement

First inventory all dependent tags (analytics, conversions, pixels, consent), migrate what must remain, then remove the snippets from the <head> and <body>. Finally, verify the continuity of essential events and conversions after deployment.

To explore SEO, GEO and digital marketing with a data-led approach, browse the Incremys Blog.