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

Abstract

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 different sized static (GIF) and animated (Flash) display ad formats; and consider whether different ad contents, related to brand or a price offer, are more or less effective for different ad formats and targeted or re-targeted consumers. To this end, they obtain daily impressions, clicks, targeting, and ad creative content data for a six-month period, from a major US retailer; and develop a dynamic zero-inflated (DZI) count model. Given the sparse, nonlinear, and non-Gaussian nature of the data, the study designs a particle filter/MCMC scheme for estimation. Results show that carryover rates for dynamic formats are greater than for static formats; yet static format can still be effective for price ads and re-targeting. Most interesting, results also show that re-targeted ads are effective only if they offer price incentives. The study then considers the import of these results for the retailer’s media schedules.

Keywords: Online Advertising, Ad Formats (Static versus Animated), Ad Content, Media Planner, Dynamic Zero-Inflated Poisson (DZIP), Particle Filtering/Smoothing, Sequential Monte Carlo (SMC), Markov Chain Monte Carlo