Matthew Schneider, PhD

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Matthew Schneider’s research enables organizations to scale the value of their consumer data while protecting user data privacy. He creates flexible, but careful statistical methodologies to protect consumer data (e.g., textual, geolocation, transaction, time series, employee survey, or IoT data) while preserving the original purpose of collecting the data (e.g., accurate forecasts, pricing of products, advertising, employee feedback in surveys, consumer demographics and topics in textual documents). For example, designing anonymization frameworks for organizations, and creating synthetic (fake) consumer data that preserves the patterns required for business decisions.

Dr. Schneider holds a PhD in Statistics and has advised a variety of financial services, privacy technology, and FinTech companies. An example of his recent work on data privacy and time series forecasting can be found here. His research has been published in the Harvard Business Review, Journal of the Royal Statistical Society, Marketing Science, International Journal of Research in Marketing, Journal of Consumer Psychology, International Journal of Forecasting, and Journal of Privacy and Confidentiality. Dr. Schneider also teaches Data Privacy Strategy to corporate executives through eCornell.

Prior to Drexel, Dr. Schneider was an Assistant Professor of Marketing at Northwestern University and a Visiting Scholar at the Samuel C. Johnson Graduate School of Management at Cornell University. He was also the Director of Research at Fort Rock Asset Management LLC, a fund of hedge funds based in Portland, Oregon. He holds a PhD and MS in Statistics from Cornell University, a MS in Public Policy and Management from Carnegie Mellon University, and a BS in Quantitative Economics from the United States Naval Academy. Before finishing his PhD, he was employed at the RAND Corporation from 2008 to 2013 and served in the U.S. Navy as an Officer of the Deck and Surface Warfare Officer on the USS Boxer from 2003 to 2005.

Areas of Expertise

  • Data Privacy
  • Time Series Forecasting

Selected Works

Articles

Gupta, Sachin, Moutafis, Panos, and Schneider, Matthew, To Protect Consumer Data, Don’t Do Everything on the Cloud. Harvard Business Review (Digital Article) (Jun 2021):

Schneider, Matthew, Protecting Survey Data on a Consumer Level. Journal of Marketing Analytics (Year 2020):

Gupta, Sachin, and Schneider, Matthew, Protecting Customers’ Privacy Requires More than Anonymizing Their Data. Harvard Business Review (Digital Article) (Jun 2018):

Schneider, Matthew, Jagpal, Sharan, Gupta, Sachin, Li, Shaobo, and Yu, Yan, A Flexible Method for Protecting Marketing Data: An Application to Point-of-Sale Data. Marketing Science (Year 2018):

Schneider, Matthew, Jagpal, Sharan, Gupta, Sachin, Li, Shaobo, and Yu, Yan, Protecting customer privacy when marketing with second-party data. International Journal of Research in Marketing 34 (Year 2017): 593-603.

Schneider, Matthew, and Gupta, Sachin, Forecasting sales of new and existing products using consumer reviews: A random projections approach. International Journal of Forecasting 32 (Year 2016): 243-256.

Schneider, Matthew, and Gorr, Wilpen, ROC-based model estimation for forecasting large changes in demand. International Journal of Forecasting 31 (Year 2015): 253-262.

Schneider, Matthew, and Abowd, John, A new method for protecting interrelated time series with Bayesian prior distributions and synthetic data. Journal of the Royal Statistical Society 178 (Year 2015): 963-975.

Iacobucci, Dawn, Posavac, Stephen, Kardes, Frank, and Schneider, Matthew, Toward a more nuanced understanding of the statistical properties of a median split. Journal of Consumer Psychology 25 (Year 2015): 652-665.

Abowd, John, Schneider, Matthew, and Vilhuber, Lars, Differential Privacy Applications to Bayesian and Linear Mixed Model Estimation. Journal of Privacy and Confidentiality 5 (Year 2013):

Education

BS Quantitative Economics - United States Naval Academy 2003
MS Public Policy and Management - Carnegie Mellon University 2008
MS Statistics - Cornell University 2011
PhD Statistics - Cornell University 2014

Media Mentions

Would you give up your privacy in exchange for pre-COVID-19 normalcy?

via KYW Radio

Matthew Schneider, PhD, assistant professor at Drexel’s LeBow College of Business, joined KYW In Depth to talk about data security before coronavirus, the privacy concerns behind tracking apps, and how we should think differently about data privacy.

Protecting Customers’ Privacy Requires More than Anonymizing Their Data

via Harvard Business Review

Matthew Schneider, assistant professor of business analytics, weighs in on the consumer privacy debate by highlighting the risk that unwanted data disclosure places on data-driven marketing.

College News

Matthew Schneider, PhD, and his co-author have proposed a new methodology to shuffle survey data so individuals aren’t identifiable even if datasets accidentally go public.

Awards from Drexel’s Graduate College reflect outstanding achievement in research and instruction.

Matthew Schneider, assistant professor of business analytics, explores how customer reviews can help predict product sales in his latest publication.