AdTheorent Real Time Learning Machine: White Paper

The InTellIgenT ImpressIonTm

© 2013 AdTheorent, Inc. All rights reserved.

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.



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.

inTroducTion

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.



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.

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.

Our products and our

passion drive an evolution to

increase advertising

effectiveness to new levels

of efficiency heretofore

unimagined.

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 oPPorTuniTy: AdTheorenT’s reAl Time leArning mAchine (rTlm)™ – PATenT-Pending PredicTive Technology ThAT leArns in reAl Time



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).

The reAl Time leArning mAchine (rTlm)



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.

how does AdTheorenT rTlm work?

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 rTlmTo 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

3rd PArTy informATion

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dATABAse+ neW dATA

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AUDIENCE DATA ENRICHMENT

AUDIENCE DATA ENRICHMENT

find BrAnd mATch

choose oPTimAl Bid Price

cAlcUlATe chAnce of conversion

choose oPTimAl creATive



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.

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).

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.

A differenT APProAch To reAl Time dATA

… 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.

AdTheorent’s architecture

can learn faster and across

more data sets while still

filtering noise better than any

technology available today.



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 – delivering the Intelligent Impression™ to meet the needs of advertisers in the mobile RTB world.

conclusion

AdTheorent’s RTLM system

and its unprecedented ability to

filter-out undesirable targets sig-

nificantly improves engagement

levels in CTR, in some campaigns

by as much as 500%.”



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.

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AdTheorent Real Time Learning Machine: White Paper