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Rachel Fan

Google



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Tim Hesterberg

Google



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Ying Liu

Google



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Lu Zhang

Google



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432 – Contributed Poster Presentations: Section on Statistics in Marketing

Methods for Measuring Brand Lift of Online Ads

Sponsor: Section on Statistics in Marketing
Keywords: A/B experiment, brand lift, imperfect control, bootstrap

Rachel Fan

Google

Tim Hesterberg

Google

Ying Liu

Google

Lu Zhang

Google

We describe ways to measure ad effectiveness for brand advertisements using online surveys. We estimate the causal effect of ads using randomized experiments. We focus on some technical issues that arise with imperfect A/B experiments-corrections for solicitation and response bias in surveys, discrepancies between intended and actual treatment, and comparing treatment group users who took an action with control users who might have acted. We discuss different methods for estimating lift for different slices of the population, to achieve different goals. We use regression, with a particular form of regularization that is particularly suited to this application. We bootstrap to obtain standard errors, and compare bootstrap methods.

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