This document summarizes a presentation on cross-device targeting challenges and near-term possibilities. It outlines the current state of cross-device targeting, obstacles hindering it, and two main approaches to device identification used today - deterministic and probabilistic. The near-term future will see greater standardization of identification approaches and discussions on a universal identifier to replace cookies while balancing privacy concerns. True cross-device targeting requires solving identity across fragmented devices, but full solutions are unlikely in 2015 due to outstanding questions on universal identifiers and data privacy.

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Made possible by
Cross-Device Targeting:
The Challenges and Near-Term
Possibilities
Lauren T. Fisher
Analyst
January 15, 2015

2. © 2015 eMarketer Inc.
Agenda:
 The current state of cross-device targeting
 Obstacles hindering cross-device targeting
 The two main approaches to device identification
 The near-term future

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The Current State of
Cross-Device Targeting

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What do we mean when we say, “cross-device
targeting”?
 A coordinated, strategic approach to identifying and
messaging individuals across multiple digital screens

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Cross-device
ad targeting
was a hot
topic for
advertisers in
2014

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Cross-device
will continue
to gain
momentum
as US adults’
time and
attention
grow on
and across
multiple
screens

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The desktop
remains the
dominant
screen for
researching
and buying
products—
even among
smartphone
owners—but
other screens
are gaining

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In fact,
advertisers
should expect
consumers to
take a
cross-device
approach to
fulfilling their
purchasing
goals

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Today, a good
portion of
publishers
say they offer
cross-device
advertising
packages

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But not all platforms and providers currently live
up to the true definition of “cross-device”
“ There’s lack of clarity around [device
identification] offerings. … You might see
the word ‘multidevice’ used. Oftentimes,
those people are allowing advertisers to
buy on mobile and buy on desktop, but
what they’re not doing is stitching that data
together. If the data remains
fragmented, it’s not really
cross-device.”
—Ben Manning, director of product management at
Quantcast

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The bottom line: cross-device targeting is still in
its infancy

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Obstacles Hindering
Cross-Device Targeting

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Taking an omniscreen approach isn’t easy; it
requires multiple pieces to fit together

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Marketers
have long
relied on
cookies to
track and
target
individuals
across the
digital web

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But as the digital universe expands beyond just
web activity, the cookie is no longer effective

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Today, there
is no universal
tracking
alternative,
creating
significant
cross-device
challenges

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Addressing this challenge requires an entirely
new toolset—and mindset
“Cross-device is the most important topic in digital
advertising right now. It’s a big, fundamental
shift to go from talking about devices and
optimizing, targeting and attributing against devices
to talking about people. It literally changes
every aspect of your marketing efforts.”
—Chad Gallagher, global director of mobile for AOL

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Key Takeaways:
 To stay effective, companies must consider each screen
 True cross-device campaigns require coordination when
it comes to identifying and reaching consumers. But
such coordination poses significant challenges today,
given the lack of a universal method of identifying
individuals across screens
 Overcoming this obstacle will require the industry to
take an identity-based—not device-based—approach to
tracking and targeting individuals

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An Identity-Based
Approach: The Two Main
Methods of Device
Identification

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Companies today use one of two identifying
methods:
 Deterministic identification
 Probabilistic identification

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Deterministic Identification
 How it works: Relies on known-user information to recognize,
with near-certain confidence, an individual on each of his or her
internet-connected devices.
 Examples of data used: Anonymized login data, CRM-level data,
email address or customer ID.
 Companies taking a deterministic approach: Facebook,
Google, AOL, Amazon.com, The Weather Company, eBay, etc.

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Probabilistic Identification
 How it works: Uses a mix of publicly available
ad-serving data to assess the statistical probability that a
specific device belongs to a particular individual.
 Examples of data used: IP address, browser version, operating
system data, device type, browsing behavior, content consumption
patterns, location-based data and time of day.

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Both approaches come with advantages and
trade-offs advertisers must consider:
 Deterministic:
– Near-perfect accuracy
– Relatively transparent in
matching methodology
– ID method and data limited
to specific publishing
environment
– Might lack in scale
 Probabilistic:
– Varying degrees of accuracy
(50% to 90%+)
– Matching algorithms deemed
proprietary
– General practice that can be
applied to any property or
campaign
– Greater scale

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Today’s reality: Advertisers typically use a
combination of the two
“ Even the deterministic IDs won’t be
as complete as we’d want them to be.
So you’ll always have to
augment with some probabilistic
approaches.
—Yosha Ulrich-Sturmat, vice president of
product marketing at Neustar

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How much advertisers rely on one vs. the other
will depend on a variety of factors:
 Breadth of CRM or first-party data
 Campaign objectives
 Typical media-buying portfolio

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Such methods solve for identity, but still don’t
deliver a holistic, universal view of an individual
“ Identity-based advertising at
scale is really the Holy Grail. To
know that we are actually reaching
and engaging the consumer that we
intend to target with our
advertising as opposed to sort of
guessing … that is really the Holy
Grail of marketing.”
—Natalie Bokenham, vice president and
managing director of digital at UM

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Key Takeaways:
 Device identification is the first step on the path to
coordinated cross-device targeting
 Today, advertisers rely on both probabilistic and
deterministic methods of pairing individuals’ identities
to their devices
 Though both solve for user identity, lack of a universal
approach leaves advertisers with fragmented
cross-device targeting efforts

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The Near-Term Future

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The near-term future for cross-device success
will largely be shaped by:
 Discussions surrounding a new, identity-based universal
identifier to replace the cookie
 Demand for greater standardization of identification
approaches and methodologies
 Data privacy discussions

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Data is the key to coordinated cross-device
targeting, and advertisers want to be the ones in
charge of coordinating those efforts
“ What we see today is not the long-
term solution. Marketers don't want …
every single publisher having a
proprietary way to do cross-device.
They want the ability to activate
and leverage their own data in
a consistent way across any
media channels they might use. ”
—Surag Patel, vice president of global product
management and marketing at AdTruth

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But is a universal identifier on the horizon?
 Advancers:
– Advertiser demand
– Industry movement from the
4A’s Media Leadership
Council and IAB
– Privacy concerns
 Inhibitors:
– Competition among ID
providers
– Disagreement over which
methodology to use
– Privacy concerns

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In the short term, expect to see greater
standardization, especially to probabilistic:
 Greater transparency around probabilistic data
collection and usage to determine ID matches
 Third-party validation of reported match rates
 Publisher push to incorporate deterministic methods
when possible

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Also expect both consumer and company privacy
concerns to influence data collection and usage

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Key Takeaways:
 Cross-device targeting is in high demand as consumers
diversify their digital media consumption, but it is still a new,
evolving tactic
 Before advertisers can do anything cross-device, they first
need to match devices back to a single individual
 Deterministic and probabilistic approaches match identities to
devices, but neither is foolproof nor 100% scalable
 2015 will be an important year for advancements in
cross-device targeting, but solutions are unlikely to be fully
realized, thanks to outstanding questions surrounding the
creation of a universal identifier and data privacy.

35. 35

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What drives relevance?
Variables
Behavior
Traits

37. 37
environment
variables
IP address
Country of origin
Time zone
Device type
Operating system
Browser type
Screen resolution
site behavior
variables
Customer /
Prospect
New/return visitor
Previous visit
patterns
Previous Product
interests – top level
Previous Product
interests – low level
Searches
Previous online
purchases
Previous Campaign
exposure
Previous Campaign
responses
offline variables
CRM
Call center
Ticket sales
3rd-party data
temporal
variables
Time of day
Day of week
Recency
Frequency
referrer
variables
Referring domain
Campaign ID
Affiliate
PPC
Natural search
Direct/bookmark
Social graph/login

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Personalize in real-time
through automated decisions.
Test content & optimize
offers against revenue goals.
Target visitors across devices
leveraging combination of data
& marketer insights.
ADOBE® TARGET—Test, target & personalize for greater revenue impact.
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Connect with us to learn more
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at: http://adobe-target.com
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 +1.800.309.9301
Download the 2014 Adobe Digital
Optimization Survey Results:
http://adobe.ly/1IKM4gJ

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Lauren T. Fisher
Cross-Device Targeting:
The Challenges and Near-Term
Possibilities
 Cross-Device Targeting: Success Hinges on Device
Identification Methods
 Omnichannel Trends 2015: Mobile is the New Retail Hub
 Key Digital Trends for 2015: What’s in Store—and Not in
Store—For the Coming Year
 Cross-Platform Attribution: A Status Report on Overcoming
Select Attribution Challenges