Organizations have been working for over ten years on digital transformation, but how exactly can this be successfully achieved? Implementing artificial intelligence, machine learning, and marketing automation requires heavy investment in data infrastructure as well as creation of the culture and know-how that empowers marketers to successfully implement data-driven decision making.
In this course, you will navigate this complex journey. As you progress through the course, you will perform a digital transformation assessment of your organization's current marketing activities. You will explore some of the successes marketers have accomplished in leading organizations and use these insights to design specific strategies to take your organization's digital transformation to the next level, in terms of both the data and the people involved. You will gain a working knowledge of artificial intelligence and machine learning from a marketing perspective and use your new skills to determine optimal ways to supercharge your marketing activities. In addition, you will identify some of the threats to digitization and determine how your organization can plan to mitigate these threats as well as strategize to keep up with rapidly changing data and technologies.
KEY COURSE TAKEAWAYS
Perform a digital transformation assessment of your organization's current marketing activities
Strategize to enhance your organization's data infrastructure and mitigate potential obstacles to digital transformation
Assess your marketing team's culture and know-how and develop a plan to evolve them to the next stage for empowered and effective data-driven decision making
Assistant Professor of Marketing, Johnson Graduate School of Management, Cornell University
Professor Clarence Lee is an assistant professor at the Johnson Graduate School of Management, where he is a Breazzano Family Sesquicentennial Fellow. Professor Lee’s research examines the drivers behind consumer adoption, usage, and purchase dynamics of digital goods, where he models consumer behavior using Bayesian statistics, structural econometrics, and machine learning techniques. Digital products and platforms, such as the ones produced by many Silicon Valley and NYC tech start-ups, are increasingly present in almost all consumer interactions. In such settings, understanding consumer choice and the dynamics of engagement and usage become critically important in order to acquire, serve, and retain consumers. He currently teaches Digital Marketing and Data Analytics & Modeling at both the Ithaca and Cornell Tech campuses.
Professor Lee received his doctorate from Harvard Business School and holds undergraduate and graduate degrees in electrical engineering and computer science from MIT. Prior to pursuing graduate studies, he has conducted nanotechnology research at IBM and space system design at MIT Lincoln Laboratory.