2025 - Working Papers: Marketing

Mobile location data offers granular detail into consumers’ mobility patterns that are indicative of preferences and behavior. Such data are a veritable goldmine for contextual marketing. At the same time, however, the granularity of the data can reveal sensitive information about consumers based on the places that they visit such as healthcare facilities or religious centers. Can the desires of firms seeking actionable insights and users preferring privacy be accommodated? Using device-level mobile location data, we examine the extent to which predictive performance is affected by aggregating individuals into homogeneous clusters that afford increased privacy. Our analyses reveal that predictive performance is minimally affected by forming relatively small homogeneous clusters, while the risk to privacy is reduced substantially. We also find that the use of locations of commercial activity can be used in lieu of home locations, affording consumers increased privacy. Moreover, intentionally avoiding data collection at locations deemed sensitive does not adversely affect business performance while further reducing risks to privacy. Our findings offer guidance to data providers who must balance service to their clients with consumers’ growing privacy expectations, as well as providing regulators with insight into the data granularity that firms require for their marketing operations.