There are a number of challenges keeping marketers from taking a more holistic, cross-platform approach to attribution, and big data is the biggest. Topics in this webinar include: What approaches are marketers taking to attribution? How are marketers organizing their data to facilitate attribution? What can marketers do to get closer to a single customer view? How are some marketers putting the pieces together to gain a more cross-platform picture?

1. © 2015 eMarketer Inc.
Made possible by
Making Attribution Work in the Age
of Big Data
Lauren T. Fisher
Analyst
October 22, 2015

2. © 2015 eMarketer Inc.
Agenda
 Why attribution: The challenges created by big data
 The current state of attribution
 The challenges of moving to a more cross-platform
approach
 Examples for dealing with today’s shortcomings

3. © 2015 eMarketer Inc.
What is attribution?
 A method of
assigning credit
to a particular
marketing- or
advertising-driven
action or other
brand-imposed
touchpoint
Company
Level
Is my
advertising/marketing
successful?
Department Level
Was this campaign successful?
Team Level
Was this tactic successful?

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Big data is a
top attribution
challenge, but
it’s not the
only one

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Attribution is the simple answer to big data
woes, but its implementation is anything but
“The need for cross-platform attribution is
completely clear, but also
unbelievably complex at the same
time. Everybody sees the potential power of
data to understand the contribution of every
device to an ultimate action. But
understanding how to track all the
data in a truly holistic way is a whole other
ballgame.”
—Cindy Gustafson, Managing Director, Invention
Studio, Mindshare North America

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Attribution Today

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Given the
confusion
around
attribution,
there’s little
alignment on
which
approach to
attribution to
take

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But by 2017, half of all US digital marketers will
transition to multitouch approaches

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Moving to a multitouch model is a must to see
the interplay between channels and tactics
 Most multitouch
models are built
using either a
top-down or
bottom-up
approach

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Two distinct attribution approaches:
Top Down
 Branding, traditional media
(print, TV, etc.)
 Econometrics, marketing
mix modeling
 Uses GRPs/impressions,
spend, sales data, external
data to model attribution
 Budget allocation and media
planning
Bottom Up
 Native to digital
 Path analysis
 Uses cookies and other
web data to analyze
actual activity
 Optimization,
performance

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To answer the
ultimate question,
most
acknowledge a
blend of both is
required
Company
Level
Is my
advertising/marketing
successful?
Department Level
Was this campaign successful?
Team Level
Was this tactic successful?

12. © 2015 eMarketer Inc.
But combining top-down and bottom-up
approaches isn’t a simple matching game
“Organizations just have too many
systems in place. Attribution is still
very technical and requires too many
people and too many hands in the pot.
I think that’s one of the reasons the
top-down and bottom-up
approaches have yet to meet in
the middle somewhere elegant.”
—Aaron McLean, Partner at Stuzo

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The same big data challenges that demand
attribution are the ones that hinder it
 Accounting for all the disparate data sets, channels and
reports
 Arriving at a single audience view (universal identifier)
 Reaping actionable insights from the data

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Challenge #1: Organizing
Disparate Data Sets

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Identifying
and gaining
access to all
the necessary
data can be
an onerous
task

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Making sense
of that data
can cause
even more
frustration

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What companies need is a single receptacle for
collecting, storing and normalizing all that data
 Today, one of
the most
common
solutions is a
data
management
platform (DMP)

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The utility of a
DMP extends
well beyond
organizing and
normalizing
data

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Challenge #2: Lack of a
Universal Identifier

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Attribution can be done without identity
matching, but bottom-up models really demand it
“Yes, you can look at some
correlations and make some
assumptions. But if you don’t
know it’s all one consumer,
the data starts to degrade in
usefulness pretty quickly because
you’re only attributing on a
portion of someone’s daily
life, not the entirety of it.”
—Nick Jordan, SVP, Global Strategy, Tapad

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But so few
have that kind
of insight into
their data

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Furthermore,
obtaining a
single,
universal view
of one’s
audience and
data is hardly a
standardized
practice

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Most companies today use one or both of the
following 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.

25. © 2015 eMarketer Inc.
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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Example: OEM uses DMP to tie digital influence
back to car dealership sales
 The goal: To understand how a digital advertising campaign is
driving actual sales at the dealership
 Data in: website activity, search, social, email, display, mobile,
cross-device IDs, third-party data, dealership data
 The result: Ability to tie dealership sales back to digital
touchpoints (cross-device) to find influence of each exposure
and channel on the sale
 Added benefit: Improved customer service experience at
dealer; knowledge of channel influence is used to optimize
customer experience

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Challenge #3: Gaining
More Real-Time Insights

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Looking
ahead,
it’s not just
about
attributing,
but taking
more
real-time
action on
that info

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The real aim of attribution is to move from
something like this:
To this:
Company
Level
Is my
advertising/marketing
successful?
Department Level
Was this campaign successful?
Team Level
Was this tactic successful?
Company Level
Is my advertising/marketing
successful?
Department Level
Was this campaign
successful?
Team Level
Was this tactic successful?

30. © 2015 eMarketer Inc.
The shift to more real-time use of attribution will
continue, though it will never be complete
“It’s unrealistic to expect 100% of all
channels will be optimized in real
time.”
—Allegra Kadet, Managing Director, neo@Ogilvy

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Example: Taking a more “real time” approach to
TV advertising to drive in-store sales
 The goal: Mindshare working with national advertiser
looking to drive in-store sales in an eight-week window
 Data in: Marketing and advertising data (digital,
traditional, etc.), in-store purchase data, competitive data
 Model used: Primarily top down (marketing mix model)
enhanced with digital and competitive data
 Benefits of this approach: Ability to optimize in near real
time, even TV

32. © 2015 eMarketer Inc.
Key Takeaways:
 Big data is causing big problems for companies tracking
advertising and marketing success
 Attribution is the answer, but transitioning to a holistic
approach is not without difficulties
 DMPs are critical to organizing and normalizing data
 Gaining a single customer view is critical for a bottom-up
approach, but it’s still a work in progress
 Looking ahead, attribution will be just as much about
influencing success as it will be about measuring it

33. NEEDS
Leverage & own your data
to market with precision
Bridge online & offline
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your dialogue
Analyze & optimize
across channels and
devices
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& Segmentation
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Attribution
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Lauren T. Fisher
Cross-Platform Attribution:
Overcoming Big Data Challenges
 Cross-Platform Attribution 2015: Device Identification, Big Data
Pose Continued Challenges
 The Mobile Attribution Gap: Five Missing Links in Mobile That
Makes Attribution Harder
 The State of US Digital Advertising 2015: Mobile Trumps Desktop,
People Trump Devices
 Digital Display Advertising: Nine Things to Know for 2015
 Cross-Device Targeting: Success Hinges on Device Identification
Methods