A202. Uncovering Data-Driven Segments by Applying Unsupervised Learning to Location Data

Location data is a powerful tool. To help marketers understand and best meet holiday shoppers’ needs, Foursquare applied unsupervised learning methods to location data to derive meaningful segments of individuals based on their demographics and shopping behaviors. Rather than invest in reaching more general segments like moms or Millennials, marketers are now able to focus their efforts by targeting these data-driven segments. Dimensionality reduction methods such as principal component analysis (PCA), combined with clustering methods such as k-means can isolate which features describe the most variance among users. Those features can then be used to group like users together in an unsupervised manner to analyze results. This information empowers marketers to determine which segments present the largest opportunity and what strategies to use to best target them. Speaker: Ali Rossi, Senior Data Scientist, Foursquare