Tal Shoshani

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.

Retailers’ efforts to monetize consumer location data remain dominated by inefficient protocols (e.g., geofencing) that customize marketing interactions based solely on app users’ current location. Although extant trajectory mining techniques can remedy these shortcomings, they require high-frequency location data, which poses severe risks to consumers’ privacy. The authors present a novel method to extract marketing value from low-granularity urban mobility data and demonstrate its use in analyzing gas station choice to value customers. The data, also used to infer gas station visits, contain 1.06 million location records on nearly 27,000 devices observed near selected retailers including gas stations during a six-month period in Staten Island, New York. The authors pool consumers’ mobility trajectories from several days to dynamically calculate the distance of stores from consumers’ anticipated trajectories. They then supplement the data with station-level daily fuel prices and estimate a conditional logit model to assess how consumers trade off gas prices versus store distance. In addition to a generally high station loyalty, the authors find that consumers strongly prefer not to deviate far from their common trajectories for fueling trips. Applying their methods in a predictive context, the authors infer the value of newly acquired customers to the studied gas stations to be between $3.00 and $7.59.

In this paper, we show how retailers can use consumer mobility data to assess the relative performance of each store within their network. We use mobile location data from over 5M devices in Manhattan, NY to construct a weighted network of Starbucks stores as nodes, with the edge weights between any two stores reflecting both the overlap between the customers of as well as the distance between the stores. We then compute network centrality measures to capture consumption dynamics in the network. Finally, we employ these variables to train machine learning models predicting whether or not each store closed down during the 20 months following our observation period. Our findings indicate that including network centrality measures derived from urban mobility data using our methods can lead to a better identification of underperforming stores in a retailer’s network, revealed by subsequent store closure decisions.