  Users' behavioral footprints online enable firms to discover behavior-based
user segments (or, segments) and deliver segment specific messages to users.
Following the discovery of segments, delivery of messages to users through
preferred media channels like Facebook and Google can be challenging, as only a
portion of users in a behavior segment find match in a medium, and only a
fraction of those matched actually see the message (exposure). Even high
quality discovery becomes futile when delivery fails. Many sophisticated
algorithms exist for discovering behavioral segments; however, these ignore the
delivery component. The problem is compounded because (i) the discovery is
performed on the behavior data space in firms' data (e.g., user clicks), while
the delivery is predicated on the static data space (e.g., geo, age) as defined
by media; and (ii) firms work under budget constraint. We introduce a
stochastic optimization based algorithm for delivery optimized discovery of
behavioral user segmentation and offer new metrics to address the joint
optimization. We leverage optimization under a budget constraint for delivery
combined with a learning-based component for discovery. Extensive experiments
on a public dataset from Google and a proprietary dataset show the
effectiveness of our approach by simultaneously improving delivery metrics,
reducing budget spend and achieving strong predictive performance in discovery.
