Advanced Data-Driven Marketing Intelligence #255002

Upon successful completion of this course, participants will be able to:

• Architect a marketing data strategy, integrating data from CRM, web, social, and paid media into a single source of truth.

• Design and implement advanced tracking plans to capture critical customer journey data across multiple touchpoints.

• Apply statistical analysis and predictive modeling techniques (e.g., regression, clustering, propensity modeling) to solve marketing problems.

• Conduct sophisticated analyses such as Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA) to quantify ROI and optimize budget allocation.

• Build interactive dashboards and data visualizations that communicate complex insights with clarity and impact to stakeholders.

• Develop a culture of data-driven testing and experimentation (A/B, multivariate) to continuously improve performance.

This advanced course is designed for marketing professionals and analysts who are responsible for strategy, analytics, and performance:

• Marketing Directors, VPs, and Heads of Growth

• Senior Marketing Analysts and Data Analysts supporting marketing

• Performance Marketing Managers (SEM, Social, Programmatic)

• CRM and Lifecycle Marketing Managers

• Marketing Operations Specialists

• Product Marketers focused on data-driven GTM strategies

• Business Intelligence (BI) Professionals working with marketing data

Prerequisite: A foundational understanding of digital marketing channels and basic analytics (e.g., Google Analytics) is required.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Architecting the Marketing Data Ecosystem

• Morning Session: The Data-Driven Marketing Framework

  • From Silos to Symphony: Building an Integrated Data Strategy.

  • Introduction to Customer Data Platforms (CDPs) and Data Warehouses (e.g., BigQuery, Snowflake).

  • The Role of APIs and ETL/ELT Processes in Data Integration.

• Afternoon Session: Advanced Tracking and Data Governance

  • Designing a Holistic Tracking Plan with Google Tag Manager (GTM) and server-side tagging.

  • Ensuring Data Quality: Validation, cleansing, and governance protocols.

  • Workshop: Audit a website’s current tracking setup and design an improved plan.

Day 2: Deep-Dive Analytical Techniques

• Morning Session: Statistical Foundations for Marketers

  • Beyond Averages: Understanding Correlation, Regression, and Statistical Significance.

  • Cohort Analysis: Measuring customer behavior over time.

  • Introduction to R or Python for Marketing Analysis (Pandas, ggplot/Matplotlib).

• Afternoon Session: Predictive Analytics

  • Segmentation 2.0: RFM Analysis and Cluster Analysis for customer segmentation.

  • Building a Propensity Model: Predicting churn, conversion, and customer lifetime value (LTV).

  • Hands-On Lab: Use a dataset to perform cluster analysis and identify high-value customer segments.

Day 3: Measuring ROI & Attribution

• Morning Session: Multi-Touch Attribution (MTA)

  • Limitations of Last-Click: Introduction to MTA models (Linear, Time-Decay, Position-Based, Data-Driven).

  • Implementing and Interpreting MTA in platforms like Google Analytics.

• Afternoon Session: Marketing Mix Modeling (MMM)

  • The What and Why of MMM: Measuring offline and long-term impact.

  • Interpreting MMM Outputs to Optimize Budget Allocation Across Channels.

  • Case Study: Analyze MMM results to reallocate a multi-million dollar marketing budget for maximum impact.

Day 4: Personalization, Testing & Activation

• Morning Session: Personalization at Scale

  • Using Data to Fuel Personalization Engines and Dynamic Content.

  • Designing and Testing Personalization Strategies (web, email, ads).

• Afternoon Session: Advanced Experimentation

  • Designing A/B and Multivariate Tests for Statistical Rigor.

  • Introduction to Bayesian Testing methods.

  • Building a Culture of Experimentation: Process and documentation.

  • Workshop: Design a full A/B test for a landing page, including hypothesis, primary metric, and sample size calculation.

Day 5: Synthesis, Storytelling & Capstone

• Morning Session: Data Visualization & Storytelling

  • Building Executive-Level Dashboards in Data Studio/Looker or Tableau.

  • The Art of the Data Story: Crafting a narrative around your insights for different audiences.

• Afternoon Session: Capstone Project

  • Final Challenge: Participants are given a complex business problem and a messy dataset. They must: clean the data, perform analysis, build a dashboard, and present their strategic recommendations to the “CMO.”

  • Course Recap: Creating an Action Plan for Implementing a Data-Driven Culture.

  • Final Q&A and Certification.