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Jose Mendoza, PhD

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Academic Director, Integrated Marketing, Clinical Assistant Professor, NYU

Dr Jose Mendoza is the Academic Director and a Clinical Assistant Professor for the MS in Integrated Marketing at New York University. He focuses on consumer pricing, retail technologies, artificial intelligence marketing, data science for business, and consumer neuroscience. Previously, he held appointments as an Assistant Professor of Practice and Academic Director for the Master of Science in Digital Marketing at Sacred Heart University.

He is an experienced senior global marketing executive with over 20 years of experience in global marketing with brands such as Absolut, Chivas Regal, Malibu, Kahlua, The Glenlivet, Stolichnaya, Hewlett-Packard, and Tampico. He had international roles based in Latin America, Europe, and the USA, with a scope of over 50 countries. Dr. Mendoza has presented at global conferences in Latin America, Europe, and the USA.

He holds a B.S. in Computer Sciences, an MBA from the University of Pittsburgh, a Post-Graduate Degree in Marketing from IESA, and a Doctor of Business Administration (DBA) degree from Cranfield University (United Kingdom).

Bio from: Data Universe 2024

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Using algorithms for marketing is now a common practice—but there are significant concerns about their ethical implications and how they can interact with consumers. In this talk, NYU's Jose Mendoza offers a state-of-the-art review of ethical algorithms in marketing, focusing on the latest research, industry initiatives, and best practices.

Attendees will learn about transparency and accountability in algorithmic decision-making; the need for diversity and inclusion in algorithm development; and the role of marketers in ensuring that algorithms are ethical and responsible. We will also explore the challenges and opportunities of using ethical algorithms in marketing and how businesses can leverage these algorithms to drive positive change.

Come to this session for a understanding of the ethical considerations surrounding algorithmic decision-making in marketing and contemporary practices and developments.