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Dynamic evaluation of online display advertising with randomized experiments: an aggregated approach

We perform a randomized experiment to estimate the effects of a display advertising campaign on online user conversions. We present a time series approach using Dynamic Linear Models to decompose the daily aggregated conversions into seasonal and trend components. We attribute the difference between control and study trends to the campaign. We test the method using two real campaigns run for 28 and 21 days respectively from the Advertising.com ad network.