Programme Parameters
Graduate / Intermediate
3 Weeks (30 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Digital Marketing shows how machine learning is transforming how brands find, understand and engage customers. You learn to apply AI across the marketing funnel: segmenting and targeting audiences, personalising content and recommendations, predicting conversion and churn, and optimising ad spend and campaigns. The course also covers generative AI for content creation and the analytics that prove marketing impact — while keeping sight of privacy and responsible use. You finish able to apply AI to a real digital-marketing problem and measure its effect. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to digital marketing — personalisation, audience targeting, predictive analytics, content generation and campaign optimisation.
📋 Course Objectives
1. Segment and target audiences with AI.
2. Personalise content and recommendations.
3. Predict conversion, churn and lifetime value.
4. Optimise campaigns and ad spend.
5. Use generative AI for marketing content.
👥 Who Should Enroll?
• Digital marketers and growth professionals
• Marketing analysts and data scientists
• Content and campaign teams
• Students of marketing analytics
🚀 Key Learning Outcomes
• The ability to apply AI across marketing.
• A campaign-analytics or personalisation project.
• A measurable, ROI-focused approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Attribution and Incrementality
• 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
Segmentation and Propensity
• 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
Generative AI in Campaign Production
• 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
Bidding, Personalisation and Lifecycle
• 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
Privacy, Consent and Trust
• 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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Google Analytics |
| Covered Tool / Platform | Google Ads |
| Covered Tool / Platform | SEMrush |
| Covered Tool / Platform | HubSpot |
| Covered Tool / Platform | Canva |
| Covered Tool / Platform | Hootsuite |
| Covered Tool / Platform | Mailchimp |
Frequently Asked Questions
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