Artificial Intelligence in Marketing Course

Lesson 1 • What AI Means for Marketers

Defines AI, machine learning, and deep learning in plain marketing terms. Establishes shared vocabulary used throughout the course.

Lesson 2 • AI Capabilities Relevant to Marketing

Surveys core AI capabilities—prediction, classification, generation, and recommendation. Links each capability to a concrete marketing use case.

Lesson 3 • Ethical and Responsible AI Use

Introduces fairness, transparency, and privacy principles that govern AI marketing. Prepares students to evaluate tools against responsible-use standards.

Lesson 4 • The Modern Marketing Data Landscape

Maps the types and sources of marketing data AI systems consume. Connects data quality to AI output reliability.

Lesson 1 • Voice of the Customer with AI

Uses sentiment analysis and topic modeling to extract insight from reviews, surveys, and social data. Findings feed directly into messaging and product strategy.

Lesson 2 • Predictive Customer Scoring

Teaches lead scoring, churn prediction, and lifetime value modeling using supervised learning. Scores are linked directly to campaign prioritization decisions.

Lesson 3 • Customer Data Platforms and AI

Explains how customer data platforms ingest and unify data across channels. Positions the unified profile as the foundation for AI-driven personalization.

Lesson 4 • Privacy-Compliant Data Practices

Addresses consent management, data minimization, and anonymization within AI workflows. Ensures audience intelligence practices meet consumer-protection standards.

Lesson 1 • Content Quality and Brand Governance

Establishes review processes, style guides, and approval workflows for AI-generated content. Ensures consistency and accuracy before publication.

Lesson 2 • AI-Generated Visual and Video Content

Covers AI tools for creating brand-consistent images, graphics, and short-form video. Addresses brand safety and legal considerations for synthetic media.

Lesson 3 • Generative AI Content Fundamentals

Explains how large language models and image generators produce content. Establishes realistic expectations for quality, accuracy, and creative range.

Lesson 4 • Scaling Copy Across Channels

Applies generative AI to produce email, social, ad, and web copy at volume. Covers adaptation of a single message across formats and audience segments.

Lesson 1 • Recommendation Engines in Practice

Covers collaborative filtering, content-based, and hybrid recommendation models. Students configure and evaluate recommendation logic for e-commerce and content sites.

Lesson 2 • Dynamic Email and Web Personalization

Implements real-time content swapping in email and on-site experiences using behavioral signals. Connects personalization triggers to customer journey stages.

Lesson 3 • Conversational AI and Chatbots

Designs AI-powered chat experiences that qualify leads, support customers, and drive conversions. Covers intent recognition, dialogue flow, and escalation logic.

Lesson 4 • Measuring Personalization Impact

Establishes KPIs and testing frameworks to quantify the revenue and engagement lift from personalization. Links measurement back to strategy refinement.

Lesson 1 • AI-Driven Creative Testing

Uses dynamic creative optimization and multivariate testing to identify top-performing ad variants. Automates creative iteration based on performance data.

Lesson 2 • Programmatic Advertising and AI

Explains real-time bidding, demand-side platforms, and AI-driven inventory selection. Connects programmatic mechanics to campaign efficiency and reach goals.

Lesson 3 • Smart Bidding and Budget Optimization

Covers automated bidding strategies that optimize for conversions, value, and return on ad spend. Students configure bid strategies aligned to campaign objectives.

Lesson 4 • Audience Targeting and Lookalikes

Applies AI-generated audience segments and lookalike models to expand reach while maintaining relevance. Covers suppression lists and exclusion logic.

Lesson 1 • Marketing Automation Architecture

Maps the components of a modern automation stack and how AI layers onto existing platforms. Establishes integration patterns between CRM, CDP, and automation tools.

Lesson 2 • Email Automation with AI

Applies AI to optimize send time, subject line selection, and content personalization within automated email programs. Covers lifecycle email strategy.

Lesson 3 • Lead Nurturing and Scoring Automation

Automates lead progression through the funnel using behavioral triggers and predictive scores. Connects marketing automation to sales handoff criteria.

Lesson 4 • Automation Performance and Optimization

Establishes monitoring, reporting, and continuous improvement processes for automated workflows. Identifies failure points and optimization levers.

Lesson 1 • Insight Communication and Decision Support

Translates complex AI analytics outputs into clear executive narratives and actionable recommendations. Covers dashboard design and stakeholder reporting.

Lesson 2 • Attribution Modeling with AI

Compares rule-based and data-driven attribution models and their impact on budget decisions. Students select and implement attribution approaches for their channel mix.

Lesson 3 • Predictive Forecasting for Marketers

Applies time-series and regression models to forecast demand, pipeline, and campaign performance. Covers confidence intervals and scenario planning.

Lesson 4 • Marketing Analytics Foundations with AI

Reviews core analytics concepts and shows how AI augments traditional reporting. Establishes the metrics hierarchy from activity to business outcome.

Lesson 1 • AI Marketing Roadmap Development

Sequences AI initiatives into a phased roadmap with milestones, dependencies, and success metrics. Balances quick wins against long-term capability building.

Lesson 2 • Change Management for AI Adoption

Manages organizational resistance, skill gaps, and cultural shifts required for AI marketing adoption. Covers training plans, communication strategies, and success tracking.

Lesson 3 • Building the AI Marketing Business Case

Constructs ROI models and investment justifications for AI marketing initiatives. Covers cost-benefit analysis, risk quantification, and executive presentation.

Lesson 4 • Assessing AI Marketing Maturity

Evaluates an organization's current AI capabilities across data, technology, talent, and process dimensions. Identifies gaps and prioritization criteria for investment.