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AI for Marketing Course

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AI for Marketing Course

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AI for Marketing Course

Name: AI for Marketing Course
Price: 2994 PHP

AI is reshaping every corner of marketing, and marketers who know how to use it are pulling ahead fast. This course gives you the practical AI skills to improve targeting, automate content, and make smarter budget decisions. From customer insights to SEO to paid advertising, you'll learn how to apply AI across the full marketing function.

Dedika for businesses

What you will learn:

You'll learn how AI works and how to apply it to real marketing challenges across channels and functions. The course covers AI-powered customer segmentation, predictive profiling, and sentiment analysis to help you build sharper audience strategies. You'll master generative AI for content creation, copywriting, and visual asset production at scale. Paid advertising, SEO, CRM personalization, and marketing analytics are all covered with hands-on AI applications. You'll also learn how to measure AI's impact on revenue, build an AI marketing roadmap, and lead responsible AI adoption inside your organization.

How you study in a practical way AI for Marketing Course

How you practice AI for Marketing Course

For companies who want to train their team

With Dedika for businesses, the course includes exercises and examples tailored to your own business and the way your company needs.

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Course content

8 Chapters • 38 LessonsDuration between 4 and 360 hours (you decide)

Chapter 1See details

AI and Marketing Fundamentals

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 4 • What AI Actually Is
Defines AI, machine learning, and deep learning in plain language. Grounds all subsequent AI marketing discussions in accurate conceptual understanding.

Chapter 2See details

AI-Powered Customer Insights

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 4 • Sentiment Analysis and Social Listening
Applies natural language processing to extract brand perception and customer emotion from text data. Feeds real-time sentiment signals into campaign and messaging decisions.

Chapter 3See details

Generative AI for Content Creation

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 5 • Editorial Quality Control for AI Content
Establishes review processes to catch factual errors, brand misalignment, and AI hallucinations. Ensures AI-generated content meets publication standards before distribution.

Chapter 4See details

AI in Paid Advertising and Media Buying

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 5 • AI-Driven Budget Allocation
Applies AI tools to distribute budget across channels and campaigns based on predicted return. Shifts budget management from manual rules to data-driven optimization.

Chapter 5See details

AI for Marketing Analytics and Attribution

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 5 • AI-Powered Dashboards and Reporting
Designs automated reporting systems that surface insights without manual data pulling. Reduces reporting time and increases decision-making speed for marketing teams.

Chapter 6See details

AI-Driven Personalization and CRM

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 5 • Website and App Personalization
Delivers individualized on-site experiences using AI-driven content and product recommendation engines. Increases conversion rates by matching site experience to visitor intent.

Chapter 7See details

AI for SEO and Organic Growth

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 5 • AI-Assisted Keyword Research
Applies AI tools to uncover high-opportunity keywords, search intent clusters, and content gaps. Produces a prioritized keyword strategy faster than manual research methods.

Chapter 8See details

AI Marketing Strategy and Governance

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.
Lesson 5 • Data Privacy and Compliance in AI Marketing
Applies privacy-by-design principles to AI data collection, processing, and personalization workflows. Ensures AI marketing programs meet data protection obligations across markets.

Certification

Your valid completion certificate

This course is for you:

Digital marketing manager: ready to upgrade strategy with AI-driven decision-making.
Brand strategist: seeking smarter ways to understand and reach target audiences.
Freelance marketer: looking to offer AI-powered services and stand out competitively.
Career changer: transitioning into marketing with a desire to enter AI-first.
Small business owner: wanting to compete more effectively through intelligent marketing tools.
Marketing team lead: responsible for modernizing how their department operates and performs.

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