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Computational advertising (CA) is a rapidly growing field, but there are numerous challenges related to measuring its effectiveness. Some of these are classic challenges where CA offers a new aspect to the challenge (e.g., multi-touch attribution, bias), and some are brand-new challenges created by CA (e.g., fake data and ad fraud, creeping out customers). In this article, we present a measurement system framework for CA to provide a common starting point for advertising researchers to begin addressing these challenges, and we also discuss future research questions and directions for advertising researchers. We identify a larger role for measurement: It is no longer something that happens at the end of the advertising process; instead, measurements of consumer behaviors become integral throughout the process of creating, executing, and evaluating advertising programs. Computational advertising (CA) presents an unprecedented opportunity for measuring the short-and long-term effectiveness of advertising. Simply defined, CA is personalized communication that uses computational power to match the right ads and advertisers with the right consumers at the right time in the right place with the right frequency to elicit the right response. Computational advertising-and the myriad digital media through which it is delivered-offers an explosion in the volume, variety, and velocity of data available; therefore, it provides new fuel for today's more powerful machine learning and analytical techniques. At the same time, CA is being deployed in environments where highly increased personal identification and tracking across touch points, formats, and media create an opportunity to measure effectiveness at a personal level across disparate elements of a campaign and over time. The nature of these touch points presents new types of data and presentation opportunities, from geotemporal data, search histories, and voice interaction to personalized placement opportunities embedded in other media. Together,

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