Overview of AI and its role in digital marketing - Evolution of marketing: Traditional vs. AI-driven approaches - Key AI technologies in marketing - Customer journey mapping with AI - AI-powered marketing automation tools - Benefits and challenges of AI in marketing Lab Experiments: 1.Use a simple flowchart tool to map a customer journey and identify where AI could personalize email, ads, and recommendations at each stage. 2.Set up a mock email campaign with automated follow-ups using Mailchimp to explore how AI segments customers and triggers actions.

Customer segmentation using clustering algorithms: K-means, RFM - Predictive analytics for customer behavior - Personalization engines and recommendation systems - AI-driven dynamic pricing strategies - Sentiment analysis for brand perception Lab Experiments: 1.Use Python to apply K-Means clustering on customer purchase data and group them into segments for targeted marketing. 2.Build a simple collaborative filtering model to suggest products based on user behavior and simulate personalized recommendations.

AI for content generation: GPT-3, Jasper AI - Automated video and image creation tools: DALL-E, Runway ML - AI-powered social media listening tools - Influencer identification using network analysis - Optimal posting time prediction - Deepfake technology in marketing: Opportunities and risks - AI for A/B testing and ad optimization Lab Experiments: 1.Use ChatGPT or Copy.ai to create ad copy and social posts for a product; compare AI outputs with human-written versions. 2.Analyze Twitter/X data with TextBlob to track brand sentiment and visualize positive/negative trends over time.

Programmatic advertising with AI - Bid optimization using reinforcement learning - Customer lifetime value prediction - Churn prediction models - AI for fraud detection in digital ads - Case study: How Uber optimizes ads with AI - Attribution modelling with machine learning - Voice search optimization Lab Experiments: 1.Simulate a programmatic ad auction where AI adjusts bids in real time based on predicted user engagement and ROI. 2.Build a logistic regression model to predict which customers will churn using historical data and identify key risk factors.

Bias in AI marketing algorithms - Privacy concerns (GDPR, CCPA compliance) - Explainable AI for transparent marketing - Blockchain for ad fraud prevention - Metaverse and AI-driven virtual marketing Lab Experiments: 1.Analyze an AI ad-targeting model for demographic bias and apply fairness metrics to ensure equitable audience reach. 2.Simulate a data collection scenario and identify GDPR violations, then propose compliant AI-driven marketing workflows.