AI and Analytics for Business
Research Paper Series
Online Advertising Response Models: Incorporating Multiple Creatives and Impression Histories
Online advertising campaigns often consist of multiple ads, each with different creative content. We propose a model that evaluates the effectiveness of each creative in a campaign given the targeted individual’s ad impression history, as characterized by the timing and mix of previously seen ad creatives. We examine the impact that each ad impression has on both visitation and conversion behavior at the advertised brand’s website. Our model is constructed at the individual level and takes into account correlations among the rates of ad impressions, website visits and conversions. We also allow for the accumulation and decay of advertising effects, as well as ad wear-out and restoration effects. Our results highlight the importance of accommodating both the existence of multiple ad creatives in an ad campaign as well as the impact of an individual’s ad impression history. We demonstrate with a simulation how this modeling approach can be used for online ad targeting. Specifically, our results suggest that, using our model, online advertisers can increase the number of website visits and conversions by varying the creative content shown to an individual according to that individual’s history of previous ad impressions. For our data, we show a 12.7% increase in the expected number of visits and a 13.8% increase in the expected number of conversions.
Keywords: Online Advertising, Advertising Response Modeling, Online Visitation and Conversion Rates, Bayesian Models