Digital Marketing Twins

This research provides a novel methodology, Digital Marketing Twins, that automatically extracts latent features from individual-level brand survey responses to inform a statistically principled, deep generative model of customer-side brand affinity and firm-side performance factors. The proposed model enables marketers to find drivers of individual-level brand affinity, as opposed to traditionally observed metrics that must be analyzed in aggregation.

The framework serves a counterfactual purpose at the customer level. The generative part of the model completes the distribution of survey responses over time, and across firms — thereby addressing the archetypal missing data problem — by imputing customer responses in counterfactual regimes.

This research applies Digital Marketing Twins methodology to the competitive landscape of the U.S. wireless telecommunications retail market, leveraging a unique dataset of large-scale quarterly brand surveys from all three major carriers (AT&T, T-Mobile, and Verizon) from 2020 to 2022. Empirically, this approach reveals latent asymmetries in competition in terms of brand affinity, together with a nonlinear increase in brand affinity for certain types of drivers, such as satisfaction with network speed, but a nonlinear decrease in brand affinity for customers who report greater likelihoods of changing plans, providers, or devices, relative to their current wireless services.