AI for Marketing Course

Lesson 1 • The AI Marketing Technology Landscape

Surveys categories of AI marketing tools across channels and functions. Enables informed tool selection by understanding capability differences.

Lesson 2 • AI's Role in Modern Marketing

Maps AI capabilities to core marketing functions including targeting, content, and analytics. Shows how AI shifts marketers from reactive to predictive decision-making.

Lesson 3 • Data as the Foundation of AI

Explains why data quality and quantity determine AI output quality. Connects data literacy to every AI marketing application covered in the course.

Lesson 1 • AI-Driven Audience Segmentation

Covers clustering algorithms and behavioral segmentation methods that go beyond demographic splits. Produces sharper audience definitions for targeting and messaging.

Lesson 2 • Voice of Customer AI Analysis

Processes surveys, reviews, and support transcripts with AI to surface recurring themes. Translates unstructured feedback into prioritized product and messaging improvements.

Lesson 3 • Predictive Customer Profiling

Builds predictive profiles using purchase history, engagement patterns, and intent signals. Enables proactive outreach timed to customer readiness.

Lesson 1 • Prompt Engineering for Marketers

Teaches structured prompting techniques that produce usable marketing outputs on the first attempt. Directly improves output quality for every generative AI task in this chapter.

Lesson 2 • Content Scaling and Repurposing

Transforms single content pieces into multi-format, multi-channel assets using AI. Maximizes content ROI by reducing production time per asset.

Lesson 3 • AI-Generated Visual Content

Uses text-to-image and image-editing AI to produce on-brand visual assets at scale. Addresses quality control, usage rights, and brand guideline alignment.

Lesson 4 • AI Copywriting Across Channels

Generates and refines copy for ads, emails, landing pages, and social posts using AI. Covers channel-specific tone, length, and compliance considerations.

Lesson 1 • Dynamic Creative Optimization

Assembles and tests ad creative combinations automatically using AI to find top performers. Reduces manual A/B testing workload while increasing creative iteration speed.

Lesson 2 • Campaign Performance Analysis with AI

Uses AI analytics to diagnose underperformance and surface optimization opportunities quickly. Connects paid media data to broader marketing attribution covered in Chapter 5.

Lesson 3 • Automated Bidding Strategies

Explains how AI bidding algorithms optimize for conversions, value, or awareness goals. Teaches configuration choices that align bidding behavior with business objectives.

Lesson 4 • AI-Powered Audience Targeting

Uses AI targeting features in ad platforms to reach high-intent audiences with precision. Builds on customer insight skills from Chapter 2 to inform targeting inputs.

Lesson 1 • Predictive Analytics and Forecasting

Builds demand and revenue forecasts using AI models trained on historical marketing data. Shifts planning from intuition-based estimates to model-supported projections.

Lesson 2 • Marketing Mix Modeling with AI

Uses AI-assisted marketing mix modeling to quantify the contribution of each channel to revenue. Provides a privacy-resilient measurement approach for cookieless environments.

Lesson 3 • Marketing Measurement Foundations

Establishes key performance metrics and measurement frameworks before introducing AI enhancements. Ensures AI analytics outputs are interpreted within a sound measurement context.

Lesson 4 • AI-Enhanced Attribution Modeling

Compares rule-based and data-driven attribution models and explains how AI improves credit assignment. Enables more accurate channel investment decisions.

Lesson 1 • CRM Intelligence and Lead Scoring

Enriches CRM data with AI-generated scores for lead quality, purchase propensity, and churn risk. Enables sales and marketing alignment through shared, data-driven prioritization.

Lesson 2 • AI-Powered Email Personalization

Applies AI to optimize send time, subject lines, content blocks, and product recommendations in email. Directly increases open rates, click rates, and email-driven revenue.

Lesson 3 • Personalization Strategy and Architecture

Defines personalization tiers from rule-based to AI-driven and maps them to business maturity. Provides a strategic framework before implementing specific personalization tactics.

Lesson 4 • Lifecycle Marketing Automation with AI

Designs AI-orchestrated customer journeys that adapt messaging based on real-time behavior. Moves beyond static drip sequences to responsive, individualized lifecycle programs.

Lesson 1 • AI Search and Generative Engine Optimization

Adapts SEO strategy for AI-powered search experiences and generative answer engines. Prepares marketers for organic visibility in AI-mediated search environments.

Lesson 2 • Technical SEO with AI Assistance

Identifies and prioritizes technical SEO issues using AI-powered crawl and audit tools. Reduces time spent on manual site audits while improving fix prioritization accuracy.

Lesson 3 • AI-Optimized Content for Search

Uses AI to align content structure, depth, and semantic coverage with search ranking factors. Connects generative content skills from Chapter 3 to organic search performance.

Lesson 4 • Organic Growth Measurement with AI

Tracks SEO performance using AI analytics to attribute traffic, rankings, and conversions accurately. Feeds organic data into the broader attribution framework from Chapter 5.

Lesson 1 • Ethical AI in Marketing Practice

Addresses bias, transparency, and consumer trust issues specific to AI-driven marketing decisions. Embeds ethical review into campaign and tool deployment workflows.

Lesson 2 • Measuring AI Marketing ROI

Establishes frameworks to quantify efficiency gains, revenue impact, and cost savings from AI adoption. Enables ongoing justification of AI investment to executive stakeholders.

Lesson 3 • Building an AI Marketing Roadmap

Prioritizes AI use cases by impact and feasibility and sequences implementation across quarters. Produces a phased roadmap that aligns AI investment with business objectives.

Lesson 4 • AI Team Structure and Capability Building

Defines roles, skills, and training needed to sustain AI marketing operations internally. Guides leaders in building hybrid human-AI teams that scale with organizational growth.