This paper examines ways to estimate the causal effect of display advertising on browser post-view conversion (i.e. visiting the site after viewing the ad rather than clicking on the ad to get to the site). The effectiveness of online display ads beyond simple click-through evaluation is not well established in the literature. Are the high conversion rates seen for subsets of browsers the result of choosing to display ads to a group that has a naturally higher tendency to convert or does the advertisement itself cause an additional lift? How does showing an ad to different segments of the population affect their tendencies to take a specific action, or convert? We present an approach for assessing the effect of display advertising on customer conversion that does not require the cumbersome and expensive setup of a controlled experiment, but rather uses the observed events in a regular campaign setting. Our general approach can be applied to many additional types of causal questions in display advertising. In this paper we show in-depth the results for one particular campaign (a major fast food chain) of interest and measure the effect of advertising to particular sub-populations. We show that advertising to individuals that were identified (using machine learning methods) as good prospective new customers resulted in an additional 280 browsers visiting the site per 100,000 advertisements shown. This result was shown to be extremely significant. Whereas, displaying ads to the general population, not including those that visited the site in the past, resulted in an additional 200 more browsers visiting the site per 100,000 advertisements shown (not significant at the ten percent level). We also show that advertising to past converters resulted in a borderline significant increase of an additional 400 browsers visiting the site for every 100,000 online display ads shown.
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