Adaptive Modeling for Real Time Analytics: The Case of "Big Data" in Mobile Advertising

Mobile marketing campaigns are now largely deployed through the intermediaries of demand side platforms (DSPs) who provide a performance-intensive real-time bidding (RTB) version of predictive analytics as a service. Performance thresholds are roughly 100ms for DSPs to decide whether and how much to bid for a potential client to receive a particular advertisement via their mobile device. This decision requires simultaneous access to multiple very large databases with typically millions of rows and the ability to execute multiple predictive models (e.g., Logistic regression) to gauge the customer's propensity to engage. In this environment, analytic modeling must be automated via model feedback loops which adjust the models dynamically as real time data streams in. We call this mode of analytics adaptive modeling. We detail the process of adaptive modeling from the perspective of a DSP and describe the corresponding model management environment necessary to plan, execute, and evaluate RTB campaigns.