Using Deep Learning to Overcome Privacy and Scalability Issues in Customer Data Transfer

Both authors contributed equally and are listed in alphabetical order. This paper is part of P. Anand’s thesis work. The authors thank Vithala Rao, Olivier Toubia, Glen Urban, K. Sudhir, and the participants in the 2018 Marketing Science and Frontiers of Empirical Marketing conferences for helpful comments. All errors are the authors’ own. Researchers’ own analyses calculated (or derived) based in part on data from Nielsen Consumer LLC and marketing databases provided through the NielsenIQ Datasets at the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business. The conclusions drawn from the NielsenIQ data are those of the researchers and do not reflect the views of NielsenIQ. NielsenIQ is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein.