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Counterfactual-based Incrementality Measurement in a Digital Ad-Buying Platform by Chalasani, Prasad; Buchalter, Ari; Thiagarajan, Jaynth; Winston, Ezra is a scholarly article available to read on EtoBox.

What is Counterfactual-based Incrementality Measurement in a Digital Ad-Buying Platform about?

The problem of measuring the true incremental effectiveness of a digital advertising campaign is of increasing importance to marketers. With a large and increasing percentage of digital advertising delivered via Demand-Side-Platforms (DSPs) executing campaigns via Real-Time-Bidding (RTB) auctions and programmatic approaches, a measurement solution that satisfies both advertiser concerns and the constraints of a DSP is of particular interest. MediaMath (a DSP) has developed the first practical, statistically sound randomization-based methodology for causal ad effectiveness (or Ad Lift) measurement by a DSP (or similar digital advertising execution system that may not have full control over the advertising transaction mechanisms). We describe our solution and establish its soundness within the causal framework of counterfactuals and potential outcomes, and present a Gibbs-sampling procedure for estimating confidence intervals around the estimated Ad Lift. We also address practical complications (unique to the digital advertising setting) that stem from the fact that digital advertising is targeted and measured via identifiers (e.g., cookies, mobile advertising IDs) that may not be st

Author

Chalasani, Prasad; Buchalter, Ari; Thiagarajan, Jaynth; Winston, Ezra

Published

2017

Language

EN