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

Customer privacy is increasingly important to marketers. High-profile breaches of databases containing sensitive customer information, and the growing need to build the infrastructure required to support analysis of big data, present nontrivial obstacles to researchers seeking individual-level customer data from firms. In this paper, we show that recent developments in machine learning may enable firms to transfer a generative model, instead of data, thus potentially obviating the process of anonymizing and sampling customer data for release, for use in a variety of analytic use cases. We demonstrate the efficacy of a specific deep learning model, Generative Adversarial Networks (GANs), in preserving desired characteristics of original data. In real-world settings, we find that GANs can double the accuracy as compared to the best benchmark methods. We also demonstrate that GANs can be used to solve marketing problems of price markups for optimal profits and customer targeting, and that a single GAN can tackle multiple marketing problems. Furthermore, GANs have volume and velocity advantages, as the size of informational transfer grows according to model complexity, and it can readily handle real-time data streams