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,

Cause‐related marketing (CRM) refers to the phenomenon where brands partner with causes, such as nonprofit organizations. Consumers may see some CRM partnerships as less compatible than others, however the level of perceived compatibility differs from one consumer to another. We know a great deal about how perceptions of compatibility affect attitude and behavior toward CRM partnerships, but we know less about how to predict a consumer's perception of compatibility. Therefore, our purpose was to investigate the boundaries in which balance theory could be used to make predictions about consumers’ responses to CRM partnerships. This is the first study to consider the construct of attitude strength (vs. attitude alone) when considering balance theory. We found that a consumer's attitude toward a brand, along with their attitude toward a cause, predicts their perceptions of CRM compatibility. We also found that CRM triadic balance could be predicted when attitude strength was included in the models, and that balance theory allowed us to observe preliminary evidence of attitude and attitude strength spillover effects in CRM triads. Practitioners can use these insights to determine which organizations to partner with, as well as determine how advertising these partnerships may affect acceptance of these partnerships.

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Tools for analyzing social media text data to gain marketing insight have recently emerged. While a wealth of research has focused on automated human personality assessment, little research has focused on advancing methods for obtaining brand personality from social media content. Brand personality is a nuanced aspect of brands that has a consistent set of traits aside from its functional benefits. In this study, we introduce a novel, automated, and generalizable data analytics approach to extract near real-time estimates of brand personalities in social media networks. This method can be used to track attempts to change brand personality over time, measure brand personality of competitors, and assess congruence in brand personality. Applied to consumer data, firms can assess how consumers perceive brand personality and study the effects of brand–consumer congruence in personality. Our approach develops a novel hybrid machine learning algorithmic design (LDA2Vec), which bypasses often extensive manual coding tasks, thus providing an adaptable and scalable tool that can be used for a range of management studies. Our approach enhances the theoretical understanding of channeled and perceived brand personality as it is represented in social media networks and provides practitioners with the ability to foster branding strategies by using big data resources.