Adaptive Modeling and Dynamic Targeting for Real Time Analytics in Mobile Advertising

Donald Kridel, Dan Dolk and David Castillo
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Donald Kridel: University of Missouri, St Louis, MO, USA
Dan Dolk: Naval Postgraduate School, Monterey, CA, USA
David Castillo: Early Warning. Inc., Scottsdale, AZ, USA

International Journal of Systems and Service-Oriented Engineering (IJSSOE), 2017, vol. 7, issue 2, 24-39

Abstract: Mobile marketing campaigns are now largely deployed through demand side platforms (DSPs) who provide dynamic customer targeting and a performance-intensive real-time bidding (RTB) version of predictive analytics as a service. Matching users with the campaigns they are most likely to engage with in extreme real-time environments requires adaptive model management, advanced parallel processing hardware/software, and the integration of multiple very large databases. The authors present (1) an adaptive modeling strategy to satisfy the performance thresholds of 40 to 100ms for DSPs to decide whether and how much to bid for a potential client to receive a particular advertisement via their mobile device. (2) a dynamic customer profiling technique to map mobile devices to specific lattices (geographic locations), and to track user behavior via device-histories. In this “big data” decision environment, analytic model management is automated via model feedback loops which adjust the models dynamically as real-time data streams in.

Date: 2017
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