In a world where data are plentiful, not rare, what are the new mindsets and skillsets market researchers need to prosper? The presentation will discuss New Realities (that old approaches may not be the right approaches anymore), New Acknowledgements (that assuming what people tell us is true, is often wrong), and New Uses (that MR’s role in “marketing” needs to be broader than it’s ever been).

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Several trends are
coming together to
challenge the 100
year old business
model behind Market
Research

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1.
Market Research started
because the data needed to
make decisions were rare
2.
Specialized skillsets
developed around data
collection and analyzing data
from single projects

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But we now live in a world where data are not rare…

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Market research must evolve to fit this new reality
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1
How can we capitalize on the
wealth of data now available?
How can we understand why
things are happening?2
How can we link directly to
business performance?3

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We need different mindsets…
Old Research
Asking
Infinite questions we could ask
Client business issue narrows to
precise questions – which we
formulate before collecting data
Resulting in exact answers
(and/or tried and tested ways of
interpreting the answers)
New Research
Exploring/Interrogating
Huge amount of data from multiple
sources available – but NOT
tailored to answer specific
questions
Client business issue narrows to
precise questions - which we try to
answer using data which are
already available
Meaning we need to think how
best to answer the questions with
the data we have
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…and new skill sets
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Old Research
Collect Data
Sample Design
Questionnaire Design
Banners & X-Tabs
Descriptive Analysis
New Research
Find Data
Make Connections
Programming & Statistics
Multi-source modeling & prediction
Data Science

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Big Data, Little Data and NEW Market Research
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New Realities
“Old approaches are no longer the right approaches”
New Acknowledgments
“Assuming what people tell us is true, is often wrong”
New Uses
“Market Research‟s role in „Marketing‟ needs to be
broader than it has ever been”

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New Realities
“Old approaches are no longer the right approaches”

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Most of our “Standard” survey metrics have shockingly
little relationship to actual buying behavior
Correlations versus actual panel purchase behaviour (P12M)
Countries: Laundry detergent & retailers in the UK, USA and China
Respondent-level = correlation between ratings of each individual and actual behaviour of individual
Aggregate-level = correlation between sum score of ratings and actual market share
Awareness
First Mention Awareness
Other Spontaneous
Familiarity
Aided Awareness
Brand usage
Brand strength
0.92
0.81
0.87
0.68
0.57
0.25
0.25
0.11
Stated Past 3 Months
Regularly Buy
Brand Most Often
Constant Sum (Next 10)
0.98
0.96
0.96
0.96
0.58
0.62
0.69
0.74
Brand Satisfaction (10-point scale)
Purchase Intention
Recommendation (NPS)
“Only One I‟d Ever Buy”
0.71
0.03
0.23
0.95
0.31
0.08
0.22
0.37
Individual
Aggregate
vs.
Individual
Aggregate
vs.
Individual
Aggregate
vs.
-
NOTE:
Measures in red are poor
reflectors of actual behaviour
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Multi-source, Big & Small Data
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Multi-sources of data − may
require big data platforms
Connection of data sources for
holistically understanding
attitudes and behaviors
Triangulation of multiple data
sources to connect story
Actionable Insights may be more
inferential than exact

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Consider how we use multi-source data for Exploring,
Interrogating, and Predicting
Hard integration
Predicting
Exploring/
Interrogating
Querying multiple data
sources to address specific
questions or bring the story
together
Using multi-source data on
the same platform for
prediction modeling,
running of „what if‟
scenarios, etc.
May use single-source data
or rely on lookalike modeling
Soft integration
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Using technology we know
more than ever about
customers‟ experiences without
asking
Ad exposure
Mobile usage (apps, features, etc)
Location awareness
Audio sampling
Web search and sites
By relying more on ”passive” listening, we can rely
less on questioning
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New Acknowledgments
“Assuming what people tell us is true, is often wrong”

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The reality of most questionnaires…
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Do you
remember if
you saw this
ad?
No, I don‟t
think I
did? Did I?Erm, yes…

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Observing panelist exposure to digital media
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DME Technology tracks online
ad exposure across survey
panels using cookies
Campaign
Impact and
Optimization
Attitudinal
Impact
Survey
Yes
Ad
No
Ad
Groups of exposed
and not exposed
panelists are
recruited for the
survey

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So can we use technology to link observational,
attitudinal and behavioral data?
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Saw the ad
Bought the
product
TNS conducted a digital
brand effectiveness study
for a beverage campaign
where ad exposure was
tracked across the TNS
research panel.
TNS also matched the
exposed TNS panelists
with the Kantar Shopcom
purchase data platform to
determine the lift in actual
purchase of the beverage.

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What did we learn?
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The campaign had a significant impact on dollars spent on the
beverage during the campaign…
16.25% lift in
$ per 100 HHs
CONTROL
Ratio Change: .90
EXPOSED
Ratio Change: 1.05
Pre
Campaign
Post
Campaign
$673
$607
Pre
Campaign
Post
Campaign
$689
$656

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Surely technology should be able to move us beyond
just online ad exposure?
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SINGLE SOURCE PANEL
Cross media
impact and
Optimization
Attitudinal survey
Ad
DME Technology
tracks online ad
exposure
Ad
App on mobile device
“listens” to which TV
ads are viewed
App tracks ad
exposure on
tablets and mobile
devices
Ad
Ad
TNS 4 Screen Pilot

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Example - Traditional recognition metrics are
misleading
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First, we confirmed that
Recognition should not be used
to measure effectiveness of TV
advertising.
Recognize
TV
Do Not
Recognize
TV
Exposed
to TV
Not
Exposed
to TV
Aided Awareness
70% 47% 57% 51%
Power In The Mind 3.99 1.44 2.81 1.71
Exposed
to TV
Not Exposed
to TV
Recognized the TV 27% 23%
Did not recognize the TV 73% 77%
While the TV ad positively
impacted awareness and brand
equity, traditional recognition
metrics significantly overstated
the impact of the TV advertising.

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New Uses
“Market Research‟s role in „Marketing‟ is now broader
than it has ever been”

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New Data and Technology
allows Market Research to
move into areas where it
previously hasn‟t been
able to play

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New Research: Using Market Research
to better target opportunities
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Via a 3 minute survey
it is possible to
identify the
consumers willing to
spend more with your
brand…
Buy more
Buy the same
Buy less
Growth
Segment

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Look-alike models
Look-alike modeling lets you reach your Brand Growth
Target in digital media at mass scale
Growth segment
identified via survey
AD AD
AD
AD
Internet Behavior
Purchase Behavior
AD
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Example – „Digital Segment Targeting‟ delivers more
impressions to the right consumers
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0%
% of segment
Cum % of all consumers
0%
20%
40%
60%
80%
100%
20% 40% 60% 80% 100%
20%
of “Growth Segment”
reached at random
Over 60%
of “Growth Segment”
reached using DST

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Big Data, Little Data and NEW Market Research
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New Realities
“Old approaches are no longer the right approaches”
New Acknowledgments
“Assuming what people tell us is true, is often wrong”
New Uses
“Market Research‟s role in „Marketing‟ needs to be broader than
it has ever been”

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“Someone has to do something…
…It‟s just incredibly pathetic that it
has to be us”
Jerry Garcia

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