More Related Content More from InsightInnovation (20) Big Data, Little Data, New Data and the Future of Market Research by Kirsten Zapiec and Larry Friedman of TNS - Presented at the Insight Innovation eXchange North America 20132. ©TNS 2012 2
Several trends are
coming together to
challenge the 100
year old business
model behind Market
Research
3. ©TNS 2012 3
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
5. ©TNS 2012
Market research must evolve to fit this new reality
5
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
6. ©TNS 2012
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
6
7. ©TNS 2012
…and new skill sets
7
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
8. ©TNS 2012
Big Data, Little Data and NEW Market Research
8
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”
9. ©TNS 2012 9
New Realities
“Old approaches are no longer the right approaches”
10. ©TNS 2012
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
10
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Multi-source, Big & Small Data
11
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
12. ©TNS 2012
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
12
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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
13
14. ©TNS 2012 14
New Acknowledgments
“Assuming what people tell us is true, is often wrong”
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The reality of most questionnaires…
15
Do you
remember if
you saw this
ad?
No, I don‟t
think I
did? Did I?Erm, yes…
16. ©TNS 2012
Observing panelist exposure to digital media
16
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
17. ©TNS 2012
So can we use technology to link observational,
attitudinal and behavioral data?
17
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.
18. ©TNS 2012
What did we learn?
18
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
19. ©TNS 2012
Surely technology should be able to move us beyond
just online ad exposure?
19
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
20. ©TNS 2012
Example - Traditional recognition metrics are
misleading
20
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.
21. ©TNS 2012 21
New Uses
“Market Research‟s role in „Marketing‟ is now broader
than it has ever been”
22. ©TNS 2012 22
New Data and Technology
allows Market Research to
move into areas where it
previously hasn‟t been
able to play
23. ©TNS 2012
New Research: Using Market Research
to better target opportunities
23
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
24. ©TNS 2012
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
24
25. ©TNS 2012
Example – „Digital Segment Targeting‟ delivers more
impressions to the right consumers
25
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
26. ©TNS 2012
Big Data, Little Data and NEW Market Research
26
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”
27. ©TNS 2012 27
“Someone has to do something…
…It‟s just incredibly pathetic that it
has to be us”
Jerry Garcia
Editor's Notes Saw the adTNS conducted a digital brand effectiveness study for a beverage campaign where ad exposure was tracked across the TNS research panel. Groups of exposed and not exposed panelists were surveyed by email to measure the lift in brand metrics (e.g., brand awareness, opinion, consideration, purchase intent, etc.)1,204 exposed (TNS surveys)412 control (TNS surveys)Bought the productTNS also matched the exposed TNS panelists with the Kantar Shopcom purchase data platform to determine the lift in actual purchase of the beverage. Stefan, can we build the three screens? Let’s talk about how to best design this slide. I would like to make the following points:1.) Recognition is not an appropriate measure of a TV ads success. And it should never be used as a proxy for exposure.2.) In this specific case (and likely many others). Awareness and attachment to the brand drove recognition of the ad.3.) Without actual exposure data, the campaign appeared to be much stronger than it actually was. How we do it A major CPG company wanted to target its “Growth Segment”Using Digital Segment Targeting, the campaign reached their “Growth Segment” 2-4x higher than with an untargeted, random approachFor example, over 60% of the U.S. “Growth Target” were reached by targeting just 20% (the “right” 20%) of the U.S. online population