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Google Analytics in Digital marketing

Google Analytics in Digital marketing

$300.00
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30-day money-back guarantee — no questions asked
How you learn:
Self-paced • Lifetime updates
Toolkit Included:
Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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What does the Google Analytics in Digital marketing course cover?

Google Analytics in Digital marketing is covered here in 9 modules: Account and Property Architecture Design, Event and Conversion Modeling Strategy, Data Collection and Tagging Infrastructure and 6 more. The outline lists 63 specific topics, opening with select between GA4 and Universal Analytics properties based on client compliance timelines and data continuity requirements.

How do you approach Google Analytics in Digital marketing step by step?

The work is sequenced in 9 stages. It starts with Account and Property Architecture Design, moves through Event and Conversion Modeling Strategy and Data Collection and Tagging Infrastructure, and ends at Performance Monitoring and QA Processes. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Google Analytics in Digital marketing course?

Module 1 is Account and Property Architecture Design. It works through select between GA4 and Universal Analytics properties based on client compliance timelines and data continuity requirements., structure hierarchical account access using Google Analytics Admin roles to align with organizational departments and agency partnerships., implement cross-domain tracking configuration with proper consent mode and referrer preservation for multi-website brands. and 4 more.

How is the Google Analytics in Digital marketing course delivered?

The Google Analytics in Digital marketing course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Google Analytics in Digital marketing course cost?

The Google Analytics in Digital marketing course is $302 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: Google Analytics in Google Analytics Dataset, Google Analytics Toolkit, Google AdWords in Google Analytics Dataset, Google Tag Manager in Google Analytics Dataset.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the equivalent of a nine-workshop technical onboarding program for analytics consultants, covering the full implementation lifecycle from account architecture to compliance, with the depth required to support enterprise tagging governance, cross-platform integration, and audit-ready documentation.

Module 1: Account and Property Architecture Design

  • Select between GA4 and Universal Analytics properties based on client compliance timelines and data continuity requirements.
  • Structure hierarchical account access using Google Analytics Admin roles to align with organizational departments and agency partnerships.
  • Implement cross-domain tracking configuration with proper consent mode and referrer preservation for multi-website brands.
  • Define data stream separation for web, iOS, and Android apps to ensure accurate platform-specific measurement.
  • Configure internal traffic filters using IP addresses and GA4’s internal traffic settings to exclude employee activity.
  • Establish naming conventions for accounts, properties, and data streams to support auditability and client handover.
  • Evaluate the impact of subdomain vs. directory structures on session attribution and user journey analysis.

Module 2: Event and Conversion Modeling Strategy

  • Map business KPIs to GA4 events, distinguishing between engagement, conversion, and e-commerce events.
  • Define conversion events based on client business outcomes, considering downstream funnel impact and reporting thresholds.
  • Implement enhanced measurement for scroll depth, outbound clicks, and video engagement with selective overrides.
  • Design custom events for form submissions with validation to prevent duplicate or bot-triggered events.
  • Configure event parameters to capture context such as form ID, page section, or campaign source for segmentation.
  • Use modeling techniques for conversion gaps due to consent restrictions, balancing accuracy and data completeness.
  • Assess the trade-offs between automatic event tracking and custom instrumentation for maintainability and control.

Module 3: Data Collection and Tagging Infrastructure

  • Deploy GA4 via Google Tag Manager with version control and workspace naming standards for team collaboration.
  • Configure GTM triggers based on DOM elements, URL patterns, and user interactions to capture dynamic content.
  • Implement consent mode v2 with CMP integration to align data collection with regional privacy regulations.
  • Validate data layer pushes for e-commerce transactions to ensure accurate revenue and product data capture.
  • Use GTM debug mode and GA4 DebugView to troubleshoot event firing and parameter population in staging environments.
  • Manage tag firing priorities and sequencing to prevent race conditions in multi-vendor tracking setups.
  • Document data layer schema requirements for developers to ensure consistent implementation across site updates.

Module 4: E-commerce and Monetization Tracking

  • Implement GA4 e-commerce events (view_item, add_to_cart, purchase) with correct parameter nesting and value formatting.
  • Map product-level data such as SKU, category, brand, and coupon code to enable product performance reporting.
  • Configure transaction ID capture to prevent duplicate revenue counting in refund or retry scenarios.
  • Integrate offline conversion data using measurement protocol for call center or in-store purchases.
  • Set up value parameters in micro-conversions to estimate lifetime value for non-transactional goals.
  • Validate purchase event timing against ad click timestamps to assess last-click attribution validity.
  • Handle multi-currency transactions by standardizing to a base currency at the data layer or reporting layer.

Module 5: Audience and User Segmentation

  • Build audiences based on user properties such as region, device type, or first_open date for remarketing.
  • Define engagement-based audiences using event conditions like session duration or screens per session.
  • Export GA4 audiences to Google Ads with appropriate membership duration and update frequency settings.
  • Create funnel-based audiences for users who abandoned carts or completed onboarding flows.
  • Apply thresholds and sampling controls when building large audiences to maintain data accuracy.
  • Use predictive audiences for churn or purchase likelihood, evaluating model performance against business outcomes.
  • Restrict audience sharing across properties to comply with data governance policies and client agreements.

Module 6: Attribution and Pathing Analysis

  • Compare data-driven attribution models to last-click in GA4 to quantify assist value of upper-funnel channels.
  • Adjust attribution windows for different conversion types based on typical customer decision cycles.
  • Use path exploration reports to identify common drop-off sequences in user journeys.
  • Filter out internal campaign tagging errors that distort channel source/medium classification.
  • Reconcile GA4-assisted conversions with platform-specific ad metrics to detect tracking discrepancies.
  • Configure custom attribution models for offline-heavy industries where digital touchpoints are underrepresented.
  • Document attribution model assumptions when reporting to stakeholders to prevent misinterpretation.

Module 7: Integration with Advertising and CRM Platforms

  • Link GA4 to Google Ads for conversion import, audience sync, and cross-account campaign reporting.
  • Configure BigQuery export to enable CRM data joins and long-term retention beyond GA4’s interface limits.
  • Map GA4 user IDs to CRM customer records using hashed email matching in offline conversion imports.
  • Set up server-side tracking with Google Tag Manager to reduce client-side dependency and improve data reliability.
  • Integrate GA4 data with BI tools like Looker Studio using standardized data schemas for executive dashboards.
  • Manage API access for third-party tools using service accounts with least-privilege permissions.
  • Validate data consistency between GA4 and external platforms by comparing session and conversion totals.

Module 8: Privacy Compliance and Data Governance

  • Configure data retention settings in GA4 to align with corporate data minimization policies.
  • Implement IP anonymization and disable personal data collection in regions requiring GDPR compliance.
  • Document data processing agreements (DPAs) and subprocessor disclosures for client legal review.
  • Use GA4’s data collection controls to disable advertising cookies where consent is not obtained.
  • Audit user access logs monthly to detect unauthorized configuration changes or data exports.
  • Establish data deletion request workflows using GA4’s user deletion API and documentation requirements.
  • Train internal teams on data sharing restrictions, especially when using GA4 in regulated industries.

Module 9: Performance Monitoring and QA Processes

  • Develop a tracking QA checklist for website redesigns, including event validation and data layer verification.
  • Schedule automated regression tests using tools like ObservePoint or custom scripts to detect tracking breaks.
  • Monitor GA4 hit volume and error rates through GTM and Cloud Logging for anomalies.
  • Compare GA4 session counts with server logs or CDN metrics to identify undercounting issues.
  • Review data freshness and processing latency for time-sensitive reporting requirements.
  • Conduct quarterly audits of custom dimensions and metrics to deprecate unused or redundant configurations.
  • Document known data discrepancies and their root causes for transparency in stakeholder reporting.
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