AKTU Artificial Intelligence for Marketing (BMBAMK03) Syllabus PDF – MBA (Business Analytics) 3rd Semester

Course Objectives

1. Understand the fundamentals of Artificial Intelligence (AI) and its relevance in modern marketing.
2. Analyze how AI enhances customer insights, targeting, and personalization.
3. Apply AI tools and algorithms in marketing communications, campaign management, and decision-making.
4. Explore advanced applications of AI in pricing strategies, promotions, and customer retention.
5. Gain practical exposure to AI platforms, tools, and industry case studies for marketing innovation.

Unit 1: Foundations of AI in Marketing (8 hrs.)

Introduction to AI and its evolution in marketing, Machine Learning vs. Deep Learning in business Types of AI: Narrow, General, and Super AI, AI and Big Data synergy in marketing, Consumer behavior modeling using AI, Overview of AI-powered marketing ecosystems, AI readiness for marketers: mindset and skills, Introduction to AI use cases in marketing channels

Unit 2: AI for Customer Insights and Segmentation (8 hrs.)

Customer data sources and structuring for AI, AI-driven segmentation models (e.g., clustering), Behavioral segmentation using ML, Sentiment analysis using NLP, Voice of the Customer (VoC) using AI tools Personalization through predictive profiling, Persona generation using unsupervised learning, Case studies of segmentation with AI (Amazon, Netflix), AI in Customer Relationship Management (CRM)

Unit 3: AI in Marketing Communications and Campaigns (8 hrs.)

AI in content generation (text, video, images), Natural Language Processing for ad copy & email, Generative AI tools for creative marketing, AI-powered chatbots and conversational marketing, A/B testing and campaign performance optimization, Predictive lead scoring and funnel movement, Marketing automation platforms (e.g., HubSpot, etc), AI in social media analytics and influencer selection, Case studies of campaign success using AI

Unit 4: AI in Pricing, Promotion, and Personalization (8 hrs.)

Dynamic pricing algorithms and AI-based pricing models, Promotion optimization using AI simulations Recommendation engines (content-based, collaborative filtering), AI in cross-selling and upselling strategies Personalization engines in e-commerce, Customer churn prediction and retention modeling, Loyalty programs enhanced with AI, Retail analytics with AI: shelf placement, in-store behavior, Real-time personalization and contextual marketing

Unit 5: AI Tools, Platforms, and Case Applications in Marketing (8 hrs.)

Overview of tools: Google AI, Azure ML, AWS AI, IBM Watson, Python/R basics for marketers, Power BI/Tableau for AI-driven marketing dashboards, Using ChatGPT & Midjourney for marketing creatives Deploying recommender systems with ML libraries, Interpreting AI model output for business actions Future trends: AI in Metaverse and immersive marketing, Industry case studies (e.g., Coca-Cola, Sephora, Spotify)

Course Outcomes

Suggested Readings

Text Books

1. “Artificial Intelligence for Marketing” by Jim Sterne Publisher: Wiley, 2017
2. “Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data” by Omer Artun & Dominique Levin Publisher: Wiley, 2015
3. “Marketing Artificial Intelligence” by Paul Roetzer & Mike Kaput Publisher: BenBella Books, 2022 Additional Resources:
4. McKinsey & Company Reports on AI in Marketing
5. Harvard Business Review Articles on Marketing Automation and AI Ethics
6. Google AI Blog and OpenAI Research for real-time trends and case studies
7. AI Marketing Tools: HubSpot AI, Salesforce Einstein, Adobe Sensei
8. Online Platforms for Practice: Google Colab, Kaggle, Azure ML Studio