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MBA Exclusive Lecture: Rethinking Marketing for the Digital Age
💡 About the lecture

Digital technologies have profoundly impacted how consumers interact with products and companies. Today, the sale of a product marks the beginning of the customer-brand relationship instead of the end of a transaction, especially as consumers continue to engage with the brand through multiple touchpoints. Given this new landscape, in which data can further help companies to understand consumer needs and behaviour, business leaders need to evaluate the essence of their offerings to reinvent their business scope and business models.

In this Exclusive MBA lecture, CEIBS Assistant Professor of Marketing Zhang Lingling will demonstrate how classic marketing concepts and models can be adapted to understand consumer journeys in the digital age. She will draw on her research and case studies to help future business leaders to prepare for these challenges by providing examples of how mature companies have achieved such transformations.

During this event, participants and prospective MBA students can expect to gain insights into the challenges and key considerations behind marketing transformation in the digital age, as well as what it is like to sit in the classroom of the MBA programme ranked #1 in Asia by the Financial Times and Forbes.

💡 Who should attend?

Young professionals interested in pursuing an MBA.

💡 Our speaker

Prof. Zhang Lingling is an Assistant Professor of Marketing at CEIBS. Prior to joining CEIBS, she was an Assistant Professor of Marketing at the Robert H. Smith School of Business, University of Maryland. Prof. Zhang holds a DBA in Marketing from Harvard Business School, an MA in Applied Statistics from University of Michigan, and an MS and BA in Information Science from Nanjing University.

Dr. Zhang’s research interests focus on platform competition, digital marketing, and AI-powered consumer insights. Being an empirical modeller, Dr. Zhang uses large-scale field data to understand market competition and consumer behaviour.
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