A Dynamic Model for Digital Advertising: The Effects of Creative Format, Message Content, and Targeting on Engagement

摘要

The authors study the joint effects of creative format, message content, and targeting on the performance of digital ads over time. Specifically, they present a dynamic model to measure the effects of various sizes of static (GIF) and animated (Flash) display ad formats and consider whether different ad contents, related to the brand or a price offer, are more or less effective for different ad formats and targeted or retargeted customer segments. To this end, the authors obtain six months of data on daily impressions, clicks, targeting, and ad creative content from a major U.S. retailer, and they develop a dynamic zero-inflated count model. Given the sparse, nonlinear, and non-Gaussian nature of the data, the study designs a particle filter/Markov chain Monte Carlo scheme for estimation. Results show that carry-over rates for dynamic formats are greater than those for static formats; however, static formats can still be effective for price ads and retargeting. Most notably, results also show that retargeted ads are effective only if they offer price incentives. The study then considers the import of these results for the retailer's media schedules.