论文标题
通过不对称预算分配的有效和不引人注目的衡量广告回报
Valid and Unobtrusive Measurement of Returns to Advertising through Asymmetric Budget Split
论文作者
论文摘要
广告平台需要可靠的广告回报量表：广告商期望在平台上获得更多预算的绩效（例如点击或转换）的增加（例如点击或转换）？即使从平台的角度来看，准确衡量广告回报也很难。选择和省略的可变偏见使观察方法的估计不可靠，而直接实验通常是昂贵或不可行的。我们介绍了不对称的预算拆分，这是一种从平台的角度进行有效测量AD返回的新方法。不对称预算拆分在平台用户群的可比较分区中创造了广告预算分配的小规模不对称。通过在不同的预算水平下观察同一广告的性能，同时保持所有其他因素恒定，平台可以获得有效的广告回报量度。该方法不引人注目，并且具有成本效益，因为它不需要在广告或市场表现中牺牲组或牺牲。我们讨论了不对称预算的成功部署到LinkedIn的就业市场，这是一个广告市场，该市场用于根据递增的工作申请人来衡量促销预算的收益。我们概述了从业人员的操作考虑，并讨论了进一步的用例，例如预算绩效预测。
Ad platforms require reliable measurement of advertising returns: what increase in performance (such as clicks or conversions) can an advertiser expect in return for additional budget on the platform? Even from the perspective of the platform, accurately measuring advertising returns is hard. Selection and omitted variable biases make estimates from observational methods unreliable, and straightforward experimentation is often costly or infeasible. We introduce Asymmetric Budget Split, a novel methodology for valid measurement of ad returns from the perspective of the platform. Asymmetric budget split creates small asymmetries in ad budget allocation across comparable partitions of the platform's userbase. By observing performance of the same ad at different budget levels while holding all other factors constant, the platform can obtain a valid measure of ad returns. The methodology is unobtrusive and cost-effective in that it does not require holdout groups or sacrifices in ad or marketplace performance. We discuss a successful deployment of asymmetric budget split to LinkedIn's Jobs Marketplace, an ad marketplace where it is used to measure returns from promotion budgets in terms of incremental job applicants. We outline operational considerations for practitioners and discuss further use cases such as budget-aware performance forecasting.
