AdTheorent s Real-Time Learning Machine (RTLM) The Intelligent Solution for Real-time Predictive Technology in Mobile Advertising
Worldwide mobile advertising revenue is forecast to reach $11.4 billion in 2013 according to Gartner, Inc. Worldwide revenue will reach $24.5 billion in 2016 with mobile advertising revenue creating new opportunities for app developers, ad networks, mobile platform providers, specialty agencies and even communications service providers. Gartner, January 2013 AdTheorent s application of the RTLM system has an unprecedented ability to filter-out undesirable targets, reduce cost per acquisition (CPA) rates, and improve engagement levels. We are already seeing uplift in click-through rates (CTR) and awareness by 200-300%. Dr. Saed Sayad, Chief Data Scientist, AdTheorent, Inc.
INTRODUCTION Mobile advertising enables advertisers to connect with consumers at the precise moment in which they are most likely to take action -- representing a step change in advertising ROI potential. Unfortunately, this extraordinary opportunity often goes unrealized due to deployment challenges caused by the diversity of devices and the overwhelming supply of available data. With the upside so high for marketers, many technology companies and technical solutions are vying for a piece of the action. Real-time Bidding (RTB), for example, has emerged as one of the most important components of the mobile ad ecosystem. RTB allows Advertisers to pinpoint optimum audiences by dynamically adjusting bids based on desired target information, available inventory and market conditions. RTB is an invaluable tool for advertisers because it is able to integrate, automate and optimize data within a media buying process that has traditionally been disjointed, manual and wasteful. With RTB capabilities, mobile ad buying is easier, less labor intensive, less error prone and faster to set up, resulting in greater efficiency and better conversion effectiveness. Mobile advertising enables advertisers to connect with consumers at the precise moment in which they are most likely to take an action... representing a step change in advertising ROI potential. Effective RTB Depends Upon Real Time Data Unlike first generation mobile ad buying platforms, RTB enables advertisers to buy inventory in real time through ad exchange platforms on an impression-by-impression basis. Effective use of RTB depends upon the intelligent and efficient use of data, and access to the right predictive modeling technology capable of dynamically integrating rich data (e.g. demographic behavioral etc.) into the RTB process. A critical variable in the effectiveness of a predictive model within the context of an RTB platform is the speed and accuracy with which the model can process massive amounts of data. If executed well, data-driven, intelligent RTB connects advertisers with their target audiences one impression at a time.
The REAL Opportunity: AdTheorent s Real Time Learning Machine (RTLM) Patent-Pending Predictive Technology That Learns in Real Time In simplest terms, predictive models utilize a set of algorithms that uncover relationships and patterns within large volumes of data. Unlike typical business intelligence analysis, predictive modeling is forward-looking, using historical data to anticipate future events. In the context of RTB, predictive modeling is the key to unlocking the full value of mobile advertising for brands. AdTheorent s predictive modeling innovation its patent-pending, advertising-specific application called RTLM (the Real Time Learning Machine ) is quickly becoming a driving force within the industry, built specifically for mobile advertisers to intelligently leverage immense data sets within the RTB environment. Using AdTheorent s RTLM, advertisers benefit from machine learning algorithms in real time to assess the conversion potential of each mobile ad impression available. A key differentiator is the speed of the data enrichment process as it is confronted by an always-expanding data set, including purchase data, behavioral data, psychographic data, ancillary data and social data. Even post-click data is used, derived from AdTheorent s proprietary Traktion product that delivers data-driven beyond the click analytics with highly dynamic post-click conversion data. Our products and our passion drive an evolution to increase advertising effectiveness to new levels of efficiency heretofore unimagined. RTLM not only processes vast amounts of data, but also continually learns in real time from the data, creating a cyclical feeding environment that enhances the intelligence of the Real Time Learning Machine to better predict outcomes. The result is RTLM s unmatched accuracy in identifying the best impression in any given marketing condition. A Features Overview of RTLM Although data mining algorithms are widely used in diverse industries and use cases, to date one or more structural limitations has significantly constrained successful data mining applications and initiatives. Frequently, these problems are associated with the amount of data, the rate of data generation and the number of attributes (variables) to be processed. Increasingly, this big data environment expands beyond the capabilities of conventional data mining methods. RTLM provides the only viable predictive modeling platform to process Big Data with zero-latency.
The Real Time Learning Machine (RTLM) 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 (e.g.: in a mobile device, in a satellite, etc.). 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).
How Does AdTheorent RTLM Work? AdTheorent s application of RTLM leverages technology that processes enormous amounts of data for real-time analysis and scoring based on pre-set criteria, including advertiser s demographic data, geographic data, publishers data and other information. RTLM allows variables to be added or removed from analysis on the fly so that, for example, if a demographic data point such as women 25-49 were removed from the analysis, RTLM would immediately calibrate the existing predictive models. RTLM uses a three-stage data analysis approach that refines billions of bid requests, removes extraneous data or noise and delivers the most accurate and efficient targeting for mobile advertising campaigns. Initially, the AdTheorent RTB platform extracts, transforms and uploads bid requests and impression data to the cloud. Then, another extract, transfer and load (ETL) process transforms and transfers the data to a cloud-based platform for real-time analytics and Business Intelligence (BI) reporting. Finally, RTLM uses the data to enhance Click Through Rates (CTR), Cost Per Click (CPC) and Cost Per Acquisition (CPA) predictive models. This process is an iterative learning environment that continuously assimilates new, real time data to gain ever better rates of high ROI for brands. AdTheorent s application of the Real-Time Learning Machine (RTLM) 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 AdTheorent s mobile advertisers. Under The Hood of AdTheorent s RTLM To achieve this level of nimble, real time processing, the algorithms are often completely redesigned with a focus on real world applicability. Real-time performance is paramount, thus the design of the feature vectors a collection of features that have any distinctive aspect, quality or characteristic are engineered to operate extremely efficiently. Frequently, it is advantageous to replace an exact implementation of a slow algorithm with a fast, approximate implementation that provides 95% of the value but runs hundreds or thousands of times faster. The platform is surrounded by a simulation and training infrastructure that allows unbiased models to be trained and their results simulated against real world datasets.
The RTLM Technology Architecture 1sT PArTy information AUDIENCE DATA ENRICHMENT mobile Ad request database 3rd PArTy information + new data BrAnd interaction choose optimal Bid Price find BrAnd match choose optimal creative calculate chance of conversion
A Different Approach to Real Time Data The term Real Time is used to describe how well a data mining algorithm can accommodate an ever increasing data load instantaneously. Such real time problems are usually closely coupled with the fact that conventional data mining algorithms operate in a batch mode where having all of the relevant data at once is required. In short, most other predictive models are updated only as full data sets are available. AdTheorent s RTLM is unique in that it continuously updates in real time an important advantage that allows it to identify better quality impressions more quickly. This real time learning capability, wholly unique in mobile predictive technologies, sets AdTheorent RTLM apart from traditional methods. In the traditional predictive modeling, the process flow starts from data>model>prediction (scoring). This creates limits for RTB platforms to process the volume of data (mostly textual and noisy data) in the limited time of execution (100 milliseconds). However, in the AdTheorent RTLM process flow there is a new component called Learner which processes the data in real time. Then another component called Modeler uses the processed data to build predictive models. Finally, a third component called Predictor applies the predictive models on the input data to generate scores. It is important to emphasize that all three RTLM components are independent, linearly scalable and capable of distributed and parallel computing. Conventional data mining algorithms operate in a batch mode where having all of the relevant data at once is required. AdTheorent s RTLM is unique in that it continuously updates in real time an important advantage that allows it to identify better quality impressions more quickly. To deal with textual (categorical) attributes, RTLM uses a very effective encoding algorithm to decrease the number of dimension of predictive models. RTLM generates very compact models with very small CPU footprint that collectively guaranty the high speed scoring process (50,000 QPS on one server). AdTheorent s architecture can learn faster and across more data sets while still filtering noise better than any technology available today. The real time availability of an iteratively learning predictive model is possible because it is linearly scalable; it is able to process any size of data and immediately update a model with each new observation without the necessity of pooling new data with old data. This effectively makes RTLM the only predictive model that updates in real time. AdTheorent s architecture can learn faster and across more data sets while still filtering noise better than any technology available today. As more data and more is processed, RTLM is able to iteratively improve the model so that it can more accurately adjust the bids while building its knowledge base, continually improving the quality of impressions delivered to the advertiser.
Conclusion RTLM is not a new predictive algorithm -- it is a new formula for deploying and using data analysis given modern demands. Traditional predictive modeling is a much more time-intensive endeavor, and nowhere near as flexible as RTLM technology. For example, with RTLM, 100 different predictive models can be tested in less than a second, and then 80-90 percent of those noisy or uncorrelated variables can be eliminated on the fly. RTLM is a breakthrough that has produced successes that include 50% -500% uplift for RTLM-powered mobile advertising campaigns. The predictive muscle of RTLM is able to expose new triggers or motivators that can zero-in on new advertising opportunities based on real time events, i.e. stock market volatility. AdTheorent s ability to learn faster across more data at faster speeds than competitors allows AdTheorent s advertisers to purchase the most optimum advertising impressions given their target audience, yielding maximum ROI. AdTheorent s RTLM system and its unprecedented ability to filter-out undesirable targets significantly improves engagement levels in CTR, in some campaigns by as much as 500%. AdTheorent delivering the Intelligent Impression to meet the needs of advertisers in the mobile RTB world.
About Dr. Saed Sayad A pioneering researcher in real-time data mining and Big Data analysis, Dr. Sayad has designed, developed and deployed many business and scientific applications of predictive modeling. The author of An Introduction to Data Mining, Dr. Sayad teaches a popular graduate course in data mining at the University of Toronto, where is an adjunct professor. About AdTheorent AdTheorent is the world s first intelligent Real Time Bidding (RTB)-enabled mobile ad network, powered by a platform built from the ground up to address the specific needs of the mobile advertising ecosystem. AdTheorent s RTm Platform integrates its 35,000+ mobile inventory sources and analyzes hundreds of thousands of potential impressions per second based on highly enriched demographic information, behavioral factors, location data, device features, as well as other advertiser-specified targeting criteria. Using predictive modeling to identify impressions with a higher propensity for conversion and awareness lift, the AdTheorent RTm Platform places bids in real-time within the pricing parameters established by the advertiser. The result to brands and marketers is higher conversion rates at a significantly lower cost -- The Intelligent Impression. For more information visit: www.adtheorent.com.