ADTHEORENT, Inc. announced the introduction of the first real-time learning and predictive modeling platform. The AdTheorent Real-Time Learning Machine(TM) learns in real time, generates data-driven predictive models ‘on the fly' and predicts faster than any other data mining technology, yielding demonstrable results for the company's mobile advertisers. Based on the data mining technology developed by the company's Chief Data Scientist, Dr. Saed Sayad, the company's RTLM application analyzes 50,000 bid requests per second on a single server, filtering out bids with a low probability of click, conversion or awareness lift.

The RTLM system ‘learns' from incoming bid requests and builds and modifies predictive models as it learns, applying such models in live campaigns to match each mobile advertisement with the optimum mobile impression. As a result of the company's RTLM system and its unprecedented ability to filter-out undesirable targets, participating advertisers have enjoyed improved engagement levels, such as an uplift in click through rates (CTR) and awareness, and reductions in cost per acquisition (CPA) rates. In some campaigns the uplift has been as high as 500%.

The company's RTLM application is a foundation of its second-generation mobile ad network, facilitating powerful modeling for click and post-click analytics and providing real-time intelligence for more effective ad targeting. the company's RTLM leverages the company's recently introduced Traktion(TM) product, which affords mobile advertisers a seamless way to track post-click behavior across the entire spectrum of mobile advertising channels. The RTLM's power to learn in real time, model ‘on the fly' and predict faster than any other data mining technology distinguishes AdTheorent from other mobile ad networks.

Using the RTLM, advertisers can increase CTR (and decrease CPA) by dividing the population and filtering various segments, and then more definitively targeting appropriate audiences. In a recent CPA engagement, the company's RTLM was able to run 2000 model variations in under five seconds to extract the most efficient model for prediction of conversion events. The AdTheorent RTLM leverages technology that processes Big Data for real-time analysis and scoring, based on criteria including advertiser's demographic data, geographic data, publishers' data and other information.

The RTLM enables variables to be added or removed from the analysis as data evolves so that, for example, if one demographic data point were removed, the RTLM would almost instantly re-calibrate the predictive model without that data. The company's RTLM is unprecedented in mobile advertising, featuring, incremental learning (Learn): immediately updating a model with each new observation without the necessity of pooling new data with old data. Decremental learning (Forget): immediately updating a model by excluding observations identified as adversely affecting model performance without forming a new dataset omitting this data and returning to the model formulation step.

Attribute addition (Grow): Adding a new attribute (variable) on the fly, without the necessity of pooling new data with old data. Attribute deletion (Shrink): immediately discontinuing use of an attribute identified as adversely affecting model performance. Scenario testing: rapid formulation and testing of multiple and diverse models to optimize prediction.

Real Time operation: Instantaneous data exploration, modeling and model evaluation. In-Line operation: processing that can be carried out in-situ. Distributed processing: separately processing distributed data or segments of large data (that may be located in diverse geographic locations) and re-combining the results to obtain a single model.

Parallel processing: carrying out parallel processing extremely rapidly from multiple conventional processing units (multi-threads, multi-processors or a specialized chip).