HBR IdeaCast What We Still Need to Learn about AI in Marketing — and Beyond
Aug 24, 2021
Eva Ascarza, an Associate Professor at Harvard Business School, focuses on customer analytics and the pitfalls of AI in marketing. She reveals that many companies misfire in leveraging AI due to neglecting to ask the right questions and improperly balancing the cost of mistakes. The conversation highlights the need for better alignment between AI insights and marketing strategies, underscoring the importance of clear communication between marketing and data science teams. Ultimately, Ascarza emphasizes the potential of AI to enhance decision-making when integrated thoughtfully into business processes.
AI Snips
Chapters
Transcript
Episode notes
Inaccurate Forecasting
- Data scientists improved a sales forecasting system with AI, increasing overall accuracy.
- However, it became worse at forecasting high-margin products, ultimately losing money.
AI in Marketing
- AI in marketing uses customer data to generate insights and inform decisions, ranging from simple forecasts to complex pricing models.
- This data-driven approach has evolved from data-driven marketing and data mining to machine learning and now encompasses the broader concept of AI.
Misalignment
- A common problem is misalignment between AI predictions and marketing decisions.
- AI teams often focus on predicting customer behavior, while marketers need to influence it, leading to wasted resources and missed opportunities.