What Can AI Do for Marketing?

Emory University via Coursera

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Overview

Explore the transformative potential of artificial intelligence in the marketing landscape. This course is designed to you distinguish between the opportunities for developing new AI algorithms and those for applying existing AI technologies in marketing. You will learn how to identify and capitalize on AI-driven opportunities to enhance marketing strategies, improve customer engagement, and drive business growth. Delve into the crucial role of data in AI applications, discovering how to collect, analyze, and leverage data to inform marketing decisions. Understand...

Syllabus

AI in Marketing

Get to know your instructor, Dr. David Schweidel, and begin with an introduction to AI and marketing.

Mapping the Customer Journey

This module explores how to leverage insights from the customer journey to influence consumer behavior, delving into various conceptual models like the purchase funnel and loyalty loop, and detailing each stage of the customer journey.

Managing the Customer Journey

This module emphasizes the significance of eliminating friction from the customer experience, identifies key engagement opportunities throughout the customer journey, and clarifies the differences between mapping and managing the customer experience to enhance overall satisfaction.

Assessing Impact

This module teaches how to critically evaluate AI applications by considering alternative explanations, identifying the necessary conditions for establishing causality, and mastering comprehensive evaluation techniques.

Opportunities for AI

Building on the previous module, this module examines two dimensions on which AI can support marketing. This module will first walk you through ways in which AI can be used to tailor marketing content delivered to consumers. It will then explore ways in which the content can be targeted based on contextual factors that will make messaging more effective.

Bias in the Machine

This module delves into the origins of AI bias, explores its potential consequences, and clearly defines what constitutes bias in AI algorithms to promote a deeper understanding and mitigation of these issues.