Validating the Combined Approach to Online Reach Prediction

Few online advertising topics have attracted as much attention recently as the introduction of traditional planning metrics to online media campaigns. Brand marketers can now directly compare the effectiveness of online and offline campaigns, and make more consistent data-driven decisions about how to allocate marketing dollars across advertising media.

The publicity about online reach prediction has focused on the effectiveness of two available sources of online data: user-centric measures (UCM) and server-centric measures (SCM). Each has strengths and weaknesses. User-centric data—which is offered by syndicated panel companies such as Nielsen//NetRatings and comScore—provides detailed demographic profiles for a limited number of internet users. Using user-centric data exclusively allows advertisers to immediately interpret demographic information, but usually overstates actual reach—the cost of not being able to identify the high-frequency users who consume a disproportionate number of impressions. On the other hand, server-centric data—which is provided by third-party ad serving companies—gives a complete picture of impression delivery for advertising campaigns, but does not tie those impressions to detailed demographic information.

The goal of this study is to quantitatively test for the first time whether combining user- and server-centric data is a valid approach to online reach prediction.

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