Measuring the incremental value of advertising (incrementality) is critical for financial planning and budget allocation by advertisers. Running randomized controlled experiments is the gold standard in marketing incrementality measurement. Current literature and industry practices to run incrementality experiments focus on running placebo, intention-to-treat (ITT), or ghost bidding based experiments. A fundamental challenge with these is that the serving engine as treatment administrator is not blind to the user treatment assignment. Similarly, ITT and ghost bidding solutions provide greatly decreased precision since many experiment users never see ads. We present a novel randomized design solution for incrementality testing based on ghost bidding with improved measurement precision. Our design provides faster and cheaper results including double-blind, to the users and to the serving engine, post-auction experiment execution without ad targeting bias. We also identify ghost impressions in open ad exchanges by matching the bidding values or ads sent to external auctions with held-out bid values. This design leads to larger precision than ITT or current ghost bidding solutions. Our proposed design has been fully deployed in a real production system within a commercial programmatic ad network combined with a Demand Side Platform (DSP) that places ad bids in third-party ad exchanges. We have found reductions of up to 85% of the advertiser budget to reach statistical significance with typical ghost bids conversion and winner rates. By deploying this design, for an advertiser in the insurance industry, to measure the incrementality of display and native programmatic advertising, we have found conclusive evidence that the last-touch attribution framework (current industry standard) undervalues these channels by 87% when compared to the incremental conversions derived from the experiment.

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