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Cross-DeviceConsumer Graph

Enabling brands to have seamless conversationswith consumers across devices

Contact Ussales@drawbrid.ge

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Introduction

Approaches to Determining Cross-Device Identity

How it Works

System Overview

Data Storage and Dissemination

Third-Party Data Validation

Self-Learning Programmatic Bidder

Calibration

Data Protection

Summary

Appendix

Table of Contents3

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Introduction

Short of having user-submitted

information, there is nocompletely accurate way tosolve for consumer identityacross devices. The Drawbridgeapproach to cross-deviceidentity is a ProbabilisticGraphical Model, which makespredictions about consumersand their device ownership.By observing a variety of

event logs, including adrequests, and correlating thoseattributes, Drawbridge hasbuilt a Connected ConsumerGraphthat incorporatesindividual Device Graphs, eachof which includes severalProbability Models. Theseconnected Probability Modelsleverage collected or inferreddemographics, user accesspatterns, behavioral segments,and other information toform the base of the largerhierarchical model.

The Drawbridge ConnectedConsumer Graph currentlyconsists of more than one

billion consumersconnected

to more than three billiondevices, the accuracy andscale of which is measuredusing standard informationtheory metrics such asPrecision and Recall againstsample sets of third-partydeterministic data from trustedpartners.

By leveraging the ConnectedConsumer Graph data,Drawbridge is able tosignificantly enhanceadvertising to consumers whileadding value for advertisersand publishers. Ads becomemore relevant, advertiserscan increase their audiencereach across devices, andpublishers can add more valueto their inventory. In addition,Drawbridge is able to providevisibility into how consumersinteract with brands acrossdevices along the path topurchase with a true, unified,cross-device consumer view.

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Drawbridge has

built a ConnectedConsumer Graphthat incorporatesindividual DeviceGraphs, each ofwhich includesseveral ProbabilityModels.

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Approaches to DeterminingCross-Device Identity

Drawbridge utilizesProbabilistic Modeling

to create its ConnectedConsumer Graph, relying onlyon non-permanent, user-resettable identifierssuch ascookies or device IDs.

Probabilistic Modeling is usedcommonly across various fieldswhere large amounts of dataneed to be analyzed, includingin meteorology for forecastingthe weather, in pharmaceuticalresearch for understandingdrug behavior, and even onWall Street for predictingmarket trends.

Probabilistic device pairingrelies on similar algorithms and

machine learning systems tomake the prediction indicating,

This smartphone and this tablet most likely belonto the same person.

Fingerprinting Deterministic Probabilistic

Description

Statistical inference

based on near-unique

identifiers such as screen

resolution, fonts, installed

plug-ins, clock

skew, etc.

Massive walled gardens(social media networks,

email clients, search

platforms) or

platforms that stitch

together data from

multiple publishers with

logged-in users.

Inference of cross-

device identity based o

non-permanent, user-

resettable

identifiers, such as

browser cookies and

mobile device IDs.

Accuracy 65-95% 85-99% Up to 95%

Cross-device scale - 4 4Avoid use of personal

information - - 4Ability to

opt-out - - 4Transportableacross platforms 4 4/ - 4

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How It Works: Device andConsumer Graphing

Drawbridge technology usescorrelations to establishits Connected ConsumerGraph, which is made up ofinterconnected Device Graphs.Each of these Device Graphsconsists of collected andinferred demographic and

behavioral information storedin Conditional ProbabilityTables, which determine theprobability that devices belongto a single user. These are usedto paint a granular portraitof each individual consumer.In essence, this hierarchicalmodel combines severalprobabilities to make educatedpredictions about users andtheir devices.

Where does the data come

from?

Drawbridge observes avariety of different attributes,including browser cookies,

hashed mobile device IDs,time, and behavior inferredfrom application and web pagevisits, among others, to createthese nodes. This informationcomes from over 50 partners,including mobile and desktopexchanges, advertisers,

publishers, data managementplatforms, and other dataproviders. To date, Drawbridgehas processed over threetrillion observations.

Drawbridge cookies users ondesktop and mobile browserswhen possible through itspartnerships with a variety ofdata management platformsand ad exchanges. Onsome browsers, Drawbridgecookies users upon servingan ad impression on siteswhere third-party cookiesare accepted, otherwiseDrawbridge uses its ownproprietary signature.

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Drawbridgeobserves a varietof differentattributes,including browsecookies, hashedmobile device

IDs, time, andbehavior inferredfrom applicationand web pagevisits, amongothers, to createthese nodes.

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System OverviewThe graphic below illustrates the flow of information into the Drawbridge ad serving and datamanagement pipeline. User data is ingested, processed, stored, and leveraged in Drawbridgessecure environment.

Data Storage andDisseminationStorage and disseminationof information within theDrawbridge ConnectedConsumer Graph is anintegral and crucial aspectof the Drawbridge ad servingsystem. Data is stored in theDrawbridge system as a tablemap of targeting data.

The list of audience segmentsdenotes audience profilesbased upon demographic,interest intent, and datainferred from the graphical

model of consumers anddevices. Drawbridge cansegment audience profiles intotens of thousands of targetingdimensions, many of which areproprietary.

MobileAd Exchanges/

DSPsSSPs & Publishers

Data Providers

Matching&

TargetingEngine

Data& Ad

Request

DesktopAd Exchanges/

DSPsSSPs & Publishers

Data Providers

Machine-LearningBidder &

Optimization

Drawbridge

Connected

Consumer

Graph

Ad

Consumer Interaction

Data& Ad

Request Ad

MobileAd Exchanges/

DSPsSSPs & PublishersOther Networks

DesktopAd Exchanges/

DSPsSSPs & PublishersOther Networks

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Third-Party Data ValidationDrawbridge uses sample setsof anonymized deterministicdata from multiple trusted

third-parties to both trainthe probabilistic model andvalidate the accuracy of theresults. In total, Drawbridgeuses over 100 milliondeterministic pairs to train andvalidate its model.

There is a negative correlationbetween consumer reach(scale) and the accuracy of

the results, as seen below. Asthe accuracy of the resultsincreases, the consumerreach becomes smaller, and asscale increases, the accuracydeclines.

Self-LearningProgrammatic BidderDrawbridge has built a self-learning programmatic bidderthat incorporates data fromRTB exchanges, historical adrequests, and the DrawbridgeConnected Consumer Graph tomake decisions in response toincoming ad requests.

When an ad request isreceived, Drawbridge matchesthe requests relevance againstdata from the ConnectedConsumer Graph and available

ad inventory across devices,and makes a decision to passor bid on the impression, andhow much to bid. This entireprocess occurs in an averageof 25 milliseconds. When anad is served, any consumerinteraction, such as a click, ispassed back to Drawbridge,and that information is addedto the bidding engine andconsidered in future biddingdecisions.

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Scale

Accuracy

Example Relationship Between Accuracy and Scale

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Calibration

The Drawbridge cross-devicepairing platform constantlycalibrates the Precision andRecallof the learning model

by constructing a ConfusionMatrixon training samplesof the same user handles onmobile and desktop devices.

A Confusion Matrixis a tablelayout that visualizes theperformance of an algorithm,and helps determine where thesystem is confusing matches.

Accuracymeasures thenumber of correct positiveand negative predictionsas a proportion of the totalpopulation.

Precisionmeasures thenumber of correctly predictedpositive results as a proportionof the total predicted positive

results.

Recallmeasures the numberof true correctly predictedpositive results as a proportionof that actual positive results.

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PopulationBlue cells are true device-matches.

Of the total population of 15, thereare 8 device-matches.

PredictionIn this example, the Drawbridge

algorithm predicts that the reddotted area are matched devices

(9), and the gray dotted area are

not matched (6). The accuracy of

the model would be the correctly

predicted positive (7) and negative

(5) matches as a proportion of the

total (15).

PrecisionThe number of correct positive

predictions (7), as a proportion

of the total predicted positivematches (9).

RecallThe number of correct positive

matches (7), as a proportion of the

total positive matches (8).

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Data ProtectionIndustry Self-Regulation

The Network Advertising Initiative (NAI) is the leading self-

regulatory association dedicated to responsible data collectionand its use for digital advertising. Drawbridge is a member of theNAI and has policies and procedures in place that meet the NAIshigh standards.

The Interactive Advertising Bureau conducts research anddevelops standards related to online advertising in order tocreate an environment of trust in the marketplace. Drawbridgeis committed to the quality assurance guidelines fostered by theIAB.

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Customer Preference

The AdChoices Advertising Option Icon gives users transparencyand control for interest-based ads. Drawbridge supports theuse of AdChoices, giving users access to the ability to managetheir preferences and opt out of online interest-based targetedadvertising.

Privacy Controls

TRUSTe is a leading data privacy management company thatdelivers compliance controls and privacy assessments andcertifications. Drawbridge has taken the step of partnering withTRUSTe to jointly develop the Drawbridge/TRUSTe Universal Opt-out mechanism that allows consumers to opt out from targetingacross multiple devices all at once.

Ghostery (formerly Evidon and The Better Advertising Project)provides tools for companies to comply with self-regulatory

guidelines for online privacy, and allows users to monitor andcontrol how websites and networks track user data. Drawbridgeworks with Ghostery to ensure compliance with privacystandards and offer transparency to users.

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Summary

Nielsen estimates thatAmericans own an average offour digital devices and engagewith media content acrossscreens for more than 60hours per week. As consumerswork, socialize, research, andbuy products across devices,

marketers will continue to shiftfocus to reaching audiencesmore effectively across theirdevices, and they will dependon advanced advertising toolsto do this. In fact, citing newtargeting technologies as adriver of growth, both Gartnerand eMarketer expect mobileadvertising spending to reach

$42B by 2017.

Forrester research indicatesthat 71% of consumers donttake well to inconsistent cross-channel messaging, and one in10 consumers even go so faras to say that inconsistencies

in the brand experience acrossdevices would make themstop interacting with a brandaltogether. The marketersadopting smart, scalable cross-device technology today are atthe forefront of these trends.

Drawbridge enables brands tohave seamless conversationswith consumers across theirdevices, including desktops,smartphones, tablets, andconnected televisions. Byleveraging its ConnectedConsumer Graph, Drawbridgeis able to gain insights and amuch deeper understanding

of consumer behavior to drivebetter results for marketers -from creating brand awarenessto driving incremental sales.

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Drawbridge isable to gaininsights and amuch deeperunderstandingof consumerbehavior to drive

better resultsfor marketers -from creatingbrand awarenesto drivingincrementalsales.

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AppendixWhat is a cookie?

A cookie is a piece of data issued inan HTTP response (an ad response)for future use by the HTTP client (aweb browser). The client then re-supplies the cookie in subsequentrequests to the same server. Thismechanism allows the server tostore user preferences and identifyindividual users.

Drawbridge HTTP Response

Drawbridge Ad/Data Servers supplycookies by populating the set-cookieresponse header with the followingdetails:

Name: UValue:Expires: Thu Apr 14 14:59:17 2022Path: /Domain: .adsymptotic.comHere is the sample Drawbridge HTTPresponse:HTTP/1.1 200 OKContent-Type: text/html;charset=utf-8Set-Cookie:U=3D9efb55e39df30077629fefd7731d6a93; expire=Thu Apr 14 14:59:17;domain=.adsymptotic.com; path=/

Mobile Application

In the case of a mobile application,a cookie can be set using thestandard HTTP cookie approach.Additional options for a cookie are oneof the following:

An anonymous one-way hash of arandomly generated fully anonymous40-character string. This does notcarry across applications:Example-An anonymous one way

hash of a device IDName: NSUUIDValue: 68753A44-4D6F-1226-9C60-0050E4C00067

Example-AndroidName: ANDROID_IDValue 643ba7cf4165bf8b

Example-iOSName: advertisingIdentifier

Value:68753A44-4D6F-1226-9C60-0050E4C00067

A confusion matrix containsinformation about actual andpredicted classifications done by aclassification system. Performance ofsuch systems is commonly evaluatedusing the data in the matrix. Thefollowing table shows the confusionmatrix for a two class classifier.

The entries in the confusion matrixhave the following meaning in thecontext of our study:

a is the number of correctpredictions that an

instance is negative b is the number of incorrectpredictions that an

instance is positive c is the number of incorrectpredictions that an

instance is negative d is the number of correctpredictions that an

instance is positive

Several standard terms have beendefined for the two-class matrix:

Precision (P)is the proportion ofthe predicted positive cases thatwere correct, as calculated using theequation:

Recall (R)is the proportion of positivecases that were correctly identified,as calculated using the equation:

Predicted

Actual

NegativePositive

Negative Positive

a bc d

P=d

b + d

R=d

c + d

11

1. Glossary of Terms, Editoria

for the Special Issue on

Applications of Machine

Learning and the Knowledge

Discovery Process, MachineLearning (1998), 30(2-3), Ro

Kohavi, Foster Provost

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Cross-DeviceConsumer Reach

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