- Overview
- Understanding Artificial Intelligence (AI)
- Use of AI in Marketing Mix Strategy
- Generative AI
- Conversational AI
- Agentic AI
- Potential risks of AI in marketing
- Lecture Slides
- Lecture Notes
- Lecture Assigned Articles
- Additional Materials
- AI in Marketing
- Total Videos: 8 (with an average of 12 minutes time)
- They supplement the lecture materials.
- Prompt Engineering
- Google Launches Agentic Shopping Tools Ahead of Holidays
- Learning Takeaways
- AI Transforms Marketing through Three Intelligences: AI applications in the marketing mix (Product, Price, Place, Promotion) are categorized into three types: Mechanical AI (for standardization and automation), Thinking AI (for personalization and data-driven decisions like dynamic pricing), and Feeling AI (for relationalization and emotional engagement, such as sentiment analysis).
- The Shift to Agentic AI Revolutionizes the Funnel: Beyond Generative AI (which creates content), Agentic AI systems possess autonomy and goals. These “agents” will fundamentally change the marketing funnel by autonomously optimizing campaigns (Awareness), negotiating as “Shopper Agents” (Consideration), and proactively preventing churn (Loyalty).
- Human Domain Expertise is Non-Negotiable: While AI excels at scale and processing data, it cannot understand business strategy, cultural nuances, or make ethical judgments. The most powerful systems require a Human-in-the-Loop because domain expertise transforms raw AI algorithms into impactful, non-fragile solutions.
- A Reliance on AI Risks Digital Homogeneity: If multiple brands use the same AI tools and algorithms, it creates a risk of “algorithmic monoculture” and “outcome homogenization”. This leads to “terminal uniqueness” and a lack of differentiation. Human creativity (incorporating ‘body,’ ‘soul,’ and ‘mind’ elements) is essential to provide unique insights and emotional connection, overcoming consumers’ algorithm aversion and information fatigue.