Directly targeting consumers based on their motivations and values can increase the relevance of marketing messages. Resonate uses machine learning models trained on survey responses and online behaviors to target over 150 million people based on 150 different motivations. Academic research shows targeting campaigns based on motivations outperforms tactics based only on demographics. Resonate's approach analyzes survey data and online behaviors to model motivations for large populations, enabling more effective targeting at scale.

1. Motivations in the
Marketing Mix

2. 2
Direct targeting of
consumers based on their
motivations increased
message relevance
WHAT HOW WHY
To establish relevance,
Resonate enables direct
targeting on values and
purchase motivations in
addition to traditional
audience definitions like
demographics and behaviors.
Advanced machine learning
models, trained on hundreds
of thousands of survey
responses and content
signatures, can be applied to
target 150 million online
adults.
As measured by trusted 3rd
parties, targeted campaigns
on motivations outperform
other tactics and industry
benchmarks.

3. What’s the difference
between a consumer
and a customer?
Motivation.
3
Marketing is undergoing a fundamental change.
As brands war for share of voice and share of
wallet, busy consumers are looking for just one
thing: Relevance. Does this product meet my
needs?
What is a motivation? Think back to the last car you
bought…Did you research safety ratings and fuel
economy? You are motivated by safety and value. Did you
seek out horsepower and surround sound? Innovation and
enjoyment are important to you. Or did you scan the
driveways of your friends and neighbors? Belonging and
self-image drive your actions. Your income and zip code
may inform how much you can afford, but your ultimate
brand and model selection is a much more personal
decision driven by what you value.
• Values are attributes that are important to
a person.
• Values are motivations because they cause
that person to act.
Media targeted on consumer motivations is more relevant,
resulting in higher incremental response and longer
engagement. In this overview of our methodology, we’ll
introduce you to why values matter, how we model
motivations to a population, and the performance delivered
on values-targeted media.

4. Motivation targeting, in
combination with
demographics, is a more
powerful predictor of purchase
than demographics alone
4
A New Application for Decades of Academic
Research
Consumer motivations and values (the “why”
behind behaviors) and their application in
marketing have been studied for more than 30
years.
Resonate uses a customized version of the “List
of Values” (LOV) methodology developed at the
University of Michigan Survey Research Center in
the early 1980s by researchers (Kahle, Douvan,
and Kulka), building on pioneering work by
Abraham Maslow among others to define
systems for classification of values.
LOV was validated in large-scale research in 1987
by Novak and MacEvoy (Columbia University and
SRI, respectively), in which its predictive power for
lifestyle activities, product purchases, and media
consumption was measured.
Specifically, their study tested the predictive potential of
LOV versus demographics alone, and what they found was
striking: The addition of values segments improved
correlations by an average of 20% across 64 tested
behaviors. And the improvement for specific categories of
behavior was substantially higher: 23% for home
improvement, 29% for categories of TV programming, and
44% for civic involvement. Novak and MacEvoy conclude
that values systems are of ”considerable practical
importance for media decisions…”
Kamakura & Novak, JCR
1992
Resonate Analytics
2013

5. 5
Motivations increase the likelihood of purchase
SELF-IMAGE BELONGING
© H&M, 2013
© Urban Outfitters, 2013
Fast-forward to 2013: Resonate has validated our
customized LOV methodology against self-reported and
third-party purchase data and found similar results. Using
our methodology, Resonate has successfully replicated
23% lifts in prediction of purchase in Apparel, 20% in home
décor, and 18% in Auto while decreasing false positives
associated with demographic models by 10%.
For example, the age, gender, and income distributions of
H&M and Urban Outfitters customers are 98% correlated,
but values like “Belonging”, “Self Image” or purchase
drivers such as “Fun & Exciting” display 10x the
differentiation between those customers than
demographics alone. Meaning that both customer sets
look exactly the same, until motivations are factored in.

6. 6
Values and motivations,
for insights and targeting,
can be effectively scaled
from a super sample of
survey and content
consumption to the entire
online population
Resonate takes a novel approach to solving this
challenge. Inspired by techniques developed at Microsoft
in 2006, Resonate built the largest primary research
platform in the U.S. matching hundreds of thousands of
online interviews to lengthy individual observed online
content signatures. An ensemble of machine learning
algorithms identify patterns of behaviors (up to 60,000
per attribute) which train our models to recognize the
motivations of individuals as expressed in their content
selection. This method requires no PII and thus protects
the privacy of browsing behaviors while providing deep
insight into the motivations of each individual.
Marrying behavioral data with massive survey
sample
Until now, identifying values and purchase
motivations required that consumers be surveyed
directly. This may be acceptable for market
research, but to use values for targeting of
addressable audience and in-market insights, it’s
necessary to model values segments out to Internet
scale.

7. Motivations can be
modeled with precision
7
How does this work in practice? Here’s an
example: About 6% of consumers are strongly
motivated to buy Health and Beauty products
which make them feel “Fun and Exciting”, as
established by 8,000+ Resonate panelists.
Our models analyze the traffic of those panelists and
compare it, daily, to traffic we observe across 150 million
cookies. The result: We can detect (and target) those “Fun
and Exciting” Health and Beauty buyers at 2.9x higher
sensitivity. Similarly, if an electronics marketer wants to
reach consumers who value “Unique” devices or “Popular”
devices, we can identify those shoppers at 2.5x and 2.8x
their natural rate of occurrence.
The ability of our models to predict individual attributes
from “super samples” has been validated by comScore,
which in 2013 verified that Resonate delivered up to a 5x
lift in prediction of cookie attributes.
Microsoft: 2006 Gender prediction Resonate: 2014 Motivation prediction

8. Media invested on values and purchase motivations
returns incremental campaign performance and content
engagement versus other targeting tactics
8
2Q2013
Campaign
Data/Results
Leading Appliance Manufacturer TubeMogul
Ad
Type Resonate
Completion
TM
Benchmark
Completion
Resonate
Improvement
2Q2013
Campaign
Data/Results
Pre-­‐roll
:30 76% 65-­‐75% Pre-­‐roll
:15 90% 65-­‐75% iPre-­‐roll
:30 68% 55-­‐60% Ad
Type Resonate
Completion
TM
Benchmark
Completion
Resonate
Improvement
Pre-­‐roll
:30 76% 65-­‐75% 9%
Pre-­‐roll
:15 90% 65-­‐75% 29%
iPre-­‐roll
:30 68% 55-­‐60% 19%
Top Performer; 30x Control on KPI Improved complete rate by up to 29%
Building on the success of one thousand completed campaigns.
It makes intuitive sense that more relevant messages generate
increased ad response, but do the numbers prove it?
One of our larger engagements, via a top holding-company media
agency, is for an appliance manufacturer with annual sales
exceeding $10B. They have a broad portfolio of brands and line
extensions, and used Resonate to better understand the
motivations of consumers who prefer each brand, and target media
accordingly. With an objective of driving engaged visits, Resonate
delivered a 97% incremental lift (30x that of a control group) and far
exceeded the demographic, contextual, and behavioral targeting
options on the plan.
Earlier this year, Resonate completed a first-in-class user-level data and
targeting integration with TubeMogul.
• For 15-second Pre-Roll, Resonate delivered video complete rates of
90%, which is 29% higher than industry average.
• We see similar levels of performance in branding and persuasion
campaigns, recently delivering a 15% (40% to 46%) and 20% (69% to
83%) lifts in behavior intent against versus 3rd-party control.
• The ultimate indicator of Resonate’s effectiveness is the satisfaction
of our clients and partners. In 2013, we received confidential
campaign evaluations from 42 unique engagements, and 93%
reported that Resonate equaled or exceeded the performance of
competing solutions, with 88% satisfied with the performance of
their campaigns, and 100% satisfied with the quality of the analysis
provided.

9. Resonate unifies
Research and
Targeting
Resonate is the only firm in the industry that targets online audiences
based on consumer’s motivations. We start by filtering the audience on
demographics and behaviors. But where others stop and claim to deliver a
“targeted audience,” we’re just beginning. Resonate then targets your
audience based on their values, beliefs and product preferences - the
9
reason why they act
Display
Mobile
• Personalize delivery and creative
with motivations based targeting
• Target your audience with effective,
relevant messaging
• IAB standard ad sizes
• Deliver ads across mobile and tablet
• Target your audience cross device to
echo messaging
• Retarget cross-device
Social
Video
• Reach users of all social platforms
• Native and off platform inventory
• The only way to target personal
motivations on Facebook
• Premium Pre-roll
• Brand Safe In-banner inventory
• Target audiences across all video sources

10. 10
Michael Horn, VP Research
Michael has been measuring digital media since 1998, and has led marketing performance
strategy engagements for ten members of the Fortune 100. Prior to joining Resonate, Michael
spent three years in San Francisco as SVP Managing Partner of Universal McCann’s Decision
Sciences unit, directing a team of 32, including seven Partners in five North American offices.
There he directed measurement and optimization across global and North American clients
including Microsoft, Wells Fargo, Johnson & Johnson, Verizon, Chrysler and Charles Schwab.
Lauren Kreisberg, Research Director
Lauren oversees research collection
and survey methodology
development for Resonate’s core
survey instrument, developed the
company’s ad effectiveness
methodologies, and supported the
creation and maintenance of much
of the insights reporting.
Aleksey Ashikhmin, Data Scientist
Aleksey is a Lead Data Scientist at
Resonate, working on design,
optimization, and developing
machine learning that reveals
consumer motivations, values, and
beliefs. Prior to joining Resonate,
Aleksey Ashikhmin worked in
Security Operations Center at
Humana Inc. as Operations Analyst,
concentrating on analysis of
intrusion detection systems alerts.
Christopher Cox, Client Services
Director
Christopher has over eight years’
experience addressing such issues
as brand strategy, audience
measurement and social media
marketing, for both brand and public
affairs clients. Christopher holds a
MA in Communications, Culture &
Technology from Georgetown
University and a BA in Political
Science from the University of North
Florida and resides in Arlington VA.
ABOUT THE AUTHOR
ABOUT THE CONTRIBUTORS

11. About Resonate
11
Resonate has pioneered a new model for using “big data” to develop a sophisticated
understanding of consumer motivations, values, attitudes and beliefs. Marketers need to
understand “why” the audiences they target take action. Resonate answers that question, while
making it simple to put that knowledge to work creating positive results for political campaigns
and marketing initiatives.
For more information, visit: resonateinsights.com

12. 12
“The essential difference between
emotion and reason is that emotion
leads to action while reason leads to
conclusions.”
Dr. Donald Caine, Neurologist
BA, MA, DM Oxford University