📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI in Digital Marketing, from foundations to a certified capstone project.
Measurement
• Last-click, multi-touch and their systematic biases
• Geo experiments and holdout tests to measure genuine incrementality
• Marketing mix modelling in a privacy-constrained measurement environment
Audience
• RFM and behavioural segmentation that a marketer can act on
• Propensity and lookalike modelling, and audience overlap pitfalls
• Customer lifetime value estimation and its use in bidding
Content
• Copy and creative generation with brand-voice constraints
• Review workflow: factual accuracy, claims substantiation and disclosure
• Creative testing at volume without drowning in inconclusive variants
Optimisation
• Automated bidding: what the platform optimises versus what you want
• On-site personalisation and recommendation, and the cold-start problem
• Lifecycle and churn intervention timed to actually change behaviour
Compliance
• Consent management, GDPR and DPDP obligations for marketing data
• Cookie deprecation, first-party data strategy and clean rooms
• Advertising standards for AI-generated content and synthetic likeness
Earn government-registered certification in AI in Digital Marketing
e-Certificate and e-Marksheet issued on successful completion.
