PGDM Core Subject
AI Driven Predictive Marketing
Course Objective
Course Objectives
- Understand the role of AI and predictive analytics in modern marketing.
- Apply machine learning techniques to forecast consumer behavior.
- Design AI-driven marketing strategies for personalization and engagement.
- Analyze ethical and privacy issues related to AI in marketing.
- Utilize AI tools for data analysis and decision-making.
Course Outcomes (CO)
| CO No. | Course Outcome | PO Mapping |
|---|---|---|
| CO1 | Explain the fundamentals of AI in marketing and its business applications. | PO1, PO2 |
| CO2 | Apply predictive analytics to model consumer behavior. | PO2, PO4 |
| CO3 | Design and evaluate AI-driven customer engagement strategies. | PO1, PO5 |
| CO4 | Assess ethical implications and regulatory considerations of AI use in marketing. | PO4, PO6 |
| CO5 | Use AI-based analytical tools to support marketing decision-making. | PO2, PO7 |
AI-Driven Predictive Marketing Syllabus T30
| Session No. | Topics |
|---|---|
| 1 | Introduction to AI in Marketing |
| 2 | Fundamentals of Predictive Analytics in Marketing |
| 3 | Customer Data Collection and Segmentation Using AI |
| 4 | Machine Learning Models for Consumer Behavior Prediction |
| 5 | AI Applications in Market Research and Consumer Insights |
| 6 | Personalized Marketing Using AI-Driven Recommendations |
| 7 | Customer Lifetime Value (CLV) Prediction Models |
| 8 | Sentiment Analysis and Social Listening Tools |
| 9 | Predictive Lead Scoring for Sales Optimization |
| 10 | AI in Dynamic Pricing and Demand Forecasting |
| 11 | Automating Customer Journey Mapping with AI |
| 12 | Using AI for Customer Churn Prediction |
| 13 | Real-Time Data Analytics in Marketing Campaigns |
| 14 | AI-Driven Content Creation and Optimization |
| 15 | Ethical Considerations and Data Privacy in AI Marketing |
| 16 | Implementing AI Chatbots for Customer Engagement |
| 17 | AI-Powered Marketing Dashboards and KPI Monitoring |
| 18 | Case Studies of AI in Indian Marketing Strategies |
| 19 | Future of AI in Consumer Decision-Making |
| 20 | Integrating AI with Traditional Marketing Approaches |
References:
- “AI for Marketing and Product Innovation” – A.K. Pradeep, Andrew Appel, & Stan Sthanunathan
- “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die” – Eric Siegel
- “Marketing Analytics: Strategic Models and Metrics” – Stephan Sorger