The key to getting accurate, actionable ideas from market research is to help respondents tell the truth about themselves. http://www.tnsglobal.com

1. Research excellence
Let’s talk about you
The key to getting accurate, actionable ideas from market research
is to help respondents tell the truth about themselves
Jan Hofmeyr, Chief Researcher, Behaviour Change
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The key to getting accurate, actionable ideas from market
research is to help respondents tell the truth about themselves
Research cannot hope to deliver precise plans for growth unless it builds a precise
understanding of individuals. It seems an obvious point to make, but it’s one with
which brand tracking surveys, in particular, struggle to come to terms.
About the author
It’s an important but often ignored truth that survey data can be valid at aggregate
level and yet wrong about individual people. This comes about through mutually Jan Hofmeyr is TNS’s leading expert on
consumer behaviour, with a career spanning
compensating error, the likelihood that for everyone who says that they used over 20 years advising many of the world’s
a particular brand but didn’t there is somebody else who says that they didn’t use best-known brands.
the particular brand when they actually did. Thanks to mutually compensating
He invented ConversionModel whilst working
error, brand tracking can continue to deliver topline aggregate figures that are for the Customer Equity Company (acquired
roughly correct, even if individual data is seriously compromised. by TNS in 2000), recognising a need for better
quality insight on consumer motivations.
This possibility ought to keep researchers awake at night, since the In 2010, following a period of five years at
Synovate, Jan returned to TNS to continue
recommendations that we make about a brand’s potential and actual consumers his work in this field, updating the
depend upon individual truth and the way that each individual’s answers correlate ConversionModel methodology to cement
together, rather than aggregate data. We require respondent-level validity – and all its position as the world’s leading measure of
too often researchers do not push hard enough to achieve this. consumer commitment.
Prior to working in market research, Jan was
TNS is developing a new approach to brand tracking that focuses clearly on a senior political advisor for the African National
respondent-level validity and the adaptations that are required to achieve this. Congress during and after the first democratic
elections in South Africa. He is the co-author
Put simply, we care about whether our respondents tell us the truth about their (with Butch Rice) of Commitment Led Marketing
likely actions – and we are developing new techniques to make it easier for them and the author of numerous, award-winning
to do so. papers on brand equity.
This approach underpins the TNS ConversionModel, a global brand tracking study
that has been built around the techniques and principles outlined in this paper.
The problems with ‘big ticket’ tracking – and how to solve them
The structure of today’s brand tracking surveys makes it difficult to get to
individual truth. It’s worth pointing out early that this problem doesn’t result from
respondents hiding the truth from us – it’s a case of survey techniques making it
frustratingly difficult for them to provide us with meaningful information. The four
main barriers that surveys put in the way of respondents telling the truth are:
„„ Brand tracking surveys are far longer than they need to be – and asking too
many irrelevant and unnecessary questions has dire consequences for data quality
„„ They ask the wrong questions and often in the wrong way, using techniques
and measures that are simplistic and known to lead to false information
„„ They ask questions at the wrong time, exposing results to the fallibility of
human memory and failing to deliver the real-time insights that marketers need
„„ They fail to apply enough intelligence to the analysis of data, with the result
that clients do not get the information they need in time.
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Focusing on respondent-level validity, weeding out questions that don’t deliver it
and developing new ways of asking questions that do, are the keys to delivering
nimbler, more effective and more actionable trackers.
The TNS approach leverages available technologies and techniques to create in
the moment surveys that are able to access consumers’ instinctive responses;
to apply intelligence to these surveys to ensure relevant, responsive questions
and actionable data; and to link this to data-streams such as economic
conditions, sales information, marketing spend and digital behaviour to provide
a holistic view.
Flexible, adaptive, faster: cutting survey length
Our core proposition is that current big-budget trackers can be collapsed into one
efficient, flexible, and adaptive data stream. This data stream can be integrated
with others in a single-source approach.
This new data-stream is built upon an intelligence-driven survey populated by
learning algorithms that cut survey length, drive up validity, and automatically
create category and brand knowledge over time. The core survey is deliberately
and genuinely ‘thin’: it takes no more than two to three minutes to complete.
We do not consider ten minute surveys to be ‘thin’.
The new system will not be modular. It will be adaptive. There is a difference.
Modular systems are like a layer-cake that adds survey chunks to a core using
dumb criteria. The key to an adaptive system is that it learns from the respondent
during the survey what should be asked next. In other words, adaptive surveys
go where the respondent wants to go. Modular systems force respondents to
go where the researcher thinks they need to go. The adaptive system becomes
the ‘conversation with consumers’, part of a tracking approach that integrates
attitudes and behavior through the creation of single-source data.
Smarter thinking about which questions to ask
Building intelligence into the tracker system is the key to making all aspects of
a survey more relevant to the respondent and so overcoming the problem of
boredom whilst improving data quality. At the same time, an intelligent tracker
system can reduce costs through saving time – and enabling multiple survey
trackers to be consolidated into one.
The task of creating an intelligent tracker system begins with applying a rigorous
approach to sample size and covariance, asking smart questions about how
many respondents need to answer each question, and how many questions each
individual respondent needs to be asked.
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Leveraging a database of the standard deviations of variables provides us with an
opportunity to reduce the number of irrelevant questions we ask by confirming
how large the sample size for each question actually needs to be. If you know
that a question has a small standard deviation, then you can reduce the size of
the sample you need for that question – which in turn means you can select a
random sub-sample to answer the question and allow the rest to skip through,
reducing survey time.
Let’s look at a quick example of how this could work: we know from some
twenty years of doing brand equity studies, that committed users of a brand tend
to be homogeneous in the image they have of that and other brands. As a result,
their answers to attribute association questions hardly vary. This means that you
don’t have to force them all to respond to the attribute association question:
measure a few and you will know what the rest would have said. You can allow
these few to answer the question for the others.
Our approach to leveraging covariance is similar. In this case, we use a database
of established covariance to create a skipping, interview-shortening process that
is tailored to a particular respondent. We know that some questions are highly
inter-correlated. The three questions most commonly used in loyalty studies,
satisfaction, purchase intention and recommendation, happen to be great
examples. If you know that a person’s answers to a particular question will be
highly correlated with answers they have already given, then you can skip that
question. Again, survey length could be cut without loss of information.
Intelligent pathways through heuristics
Applying heuristic (or self-educating) principles can help us to extend the idea of
learning from respondents and create intelligent pathways in surveys. The key
here lies in adapting each survey in real-time, to reflect the way that the particular
respondent makes decisions. Once again, the key focus here is on achieving
respondent-level validity.
We know, for example, that people who are uninvolved in a category behave in
one of two ways: either they develop shallow habits in which they stick to one
brand because they can’t be bothered to think about what to use; or they care
so little about brand choice that they’re influenced more by point-of-purchase/
consumption phenomena than by brand.
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There is very little point to asking people in this frame of mind an attribute
association question because the results are highly predictable: their answers will
be sparse and restricted to the brand they buy by habit. What’s more pertinent
is to ask them questions that measure their response to ‘in the moment’ brand
stimuli: discounts, special displays, prominence on the shelves, and so on.
The challenge is to develop the right, engaging virtual environments to do
this effectively.
By contrast, people who are committed to a brand are less influenced by ‘in the
moment’ phenomena. They could skip these kinds of questions. A more complex
pathway could be built using attitudinal equity configurations.
On the whole, we aren’t fans of attribute association questions. However, these
same heuristic principles provide an opportunity to make simple changes that can
dramatically improve the correlation of attribute responses with actual sales at
respondent level. The four key changes that TNS has identified in this area are:
1. Allow respondents to select the attributes that are most relevant to them
before asking them to associate attributes with brands.
2. Restrict the scope of the associations to the sub-set of brands that are relevant
to each respondent.
3. Replace the free form association question i.e. respondents only tick positive
associations; with a binary form i.e. respondents answer ‘yes-no’.
4. For driver analysis: transform the results into ‘share of mentions’ for each
brand and attribute at respondent level.
‘Share of mentions’ is a simple transformation: instead of using values of
‘0, 1’ when performing driver analysis, use values that are based on the share
of mentions the brand gets for each attribute. So, for example, if a person
associates two brands with an attribute, then the values for that attribute for that
respondent in a driver analysis would be ‘0.5, 0.5’.
A binary response format results in much greater response stability and
reliability1. And reducing both the attribute and brand lists ensures that relevant
information is collected and reduces the tedium associated with the classical
attribute association task.
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Mobile capabilities: asking questions at the right time
Mobile capabilities have a vital role to play in improving brand tracking surveys,
since they have the potential to solve the problem of fallible human memory and
to deliver fast-turnaround results. Leveraging mobile technology enables us to
kick-start all surveys at the appropriate moment.
TNS has almost a decade’s experience of creating short-term panels in which
panelists record their daily buying and consuming as it happens. These ‘In the
moment’ mobile purchase and consumption diaries are less subject to memory
errors; they can be used to collect ambient point-of-purchase or consumption
information; and they provide a single-source of attitude and behavioral data.
The events covered by the diaries could include drinking an alcoholic or
non-alcoholic beverage, the complex and varied stages involved in planning
a car purchase, exposure to an ad for the first time, and a huge range of
other occasions.
In our experience, people create records of each event within an hour.
In categories involving many events, people send up to eight records a day.
If a panelist hasn’t sent anything for six hours since ‘waking’, they’re sent
a reminder. Although each record looks long, it typically takes three minutes
or less; and 70 percent of the panelists complete their diaries.
We validate overall consumption using external sources such as Kantar World
Panel, Nielsen, IRI. Respondent-level validation involves setting flags to
measure response consistency.
Mobile as listening device
TNS has developed an app called MobileBehave that leverages the mobile’s
potential as a listening device for all manner of brand-consumer communications
taking place through the mobile channel. MobileBehave data builds over time
as people become relaxed about the fact that the app is on their phone. It has
multiple uses:
„„ A source of passive (i.e. ‘listening’) mobile behavioural data
„„ A single-source of ‘listening’ data combined with ‘in the moment’ data
„„ Can be used to recruit panelists for non-mobile ‘listening’
„„ Enables the building of online communities based on revealed interests
„„ Can be used as a sample source for instant surveys
„„ Becomes the basis for creating causal models of behaviour over time
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Asking the right questions in the right way
We have always known that there is a gap between what people say in surveys
and what they actually do. Thanks to contemporary neuroscience, we know the
various reasons why the gap exists – and this can help us to fix it. By basing
questions around the parts of the brain that become active when brand
attachment forms, we are able to fix common mistakes that our industry makes
when it comes to communications modeling.
There are many ways in which current approaches to measuring and modeling
communications impacts ignore reality. Here’s a short list:
„„ Over-reliance on memory to establish communications exposure. As a result,
modeled effectiveness coefficients are faulty;
„„ Failure to take account of what’s already in the brain about brands,
in particular pre-existing brand commitments;
„„ Failure to model communications effects holistically (for example, in
the context of other information that affects brand image like competitor
communications);
„„ Overly narrow focus on characteristics of the advert at the expense of
measuring impacts on the person.
Neuroscientists tell us that there are genuine differences between the way the
brain reacts to favoured and non-favoured brands2. All forms of exposure to
brands create neural tracks over time that link favoured brands to personal goals
and values. Favoured brands then show up in complex networks in the brain that
include the areas that guide decision-making, and those that deal with affective
memories. By ‘affective’ we mean more than ‘emotional’. Affective refers to
feelings with deep personal meaning.
Brand connections are built in multiple ways, most notably, through direct brand
experience, through endorsements by others – most notably experts, friends, and
what can best be called ‘the mass of humanity’, and through own-brand and
competitor messaging.
A holistic approach to communications measurement and modeling can help.
This is based on the single-source approach to information that we described
earlier. We looked at the options for collecting information about brand use in
a way that overcomes problems of memory; and gives access to context-
relevant information. To model this information more effectively, we need to
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link it to metrics that reflect the neural connections that form around brands.
There are two of these metrics: first, a quantified measure of ‘affective impact’
(remembering here that ‘affective’ means more than just ‘emotional’); second,
open-ended questions to create verbatims that can measure ‘affective content’. Affective impact
We can combine these two approaches to measure the affective impact of a By affective impact we mean the extent to
which a piece of communications links the
communications piece. First, we ask a simple open-ended question: what does brand to experiences that have a deeper
the advert bring to mind; in what ways has the brand become part of your life personal meaning. It’s about placing the brand
and who you are? Second, we explore the sequence of emotions: The lesson in the context of personal goals and values.
of most of the current emotional measurement is that ‘positive’ is good. Yet
advertising is storytelling. And we know from great storytelling that it’s the
management of an emotional sequence that really matters. So, for example,
‘negative’ need not be bad if it’s followed by ‘positive’. Examples might include:
‘problem – resolution’; ‘surprise – delight’; ‘threat – victory’. And so on. Affective content
By affective content we mean articulating
The next step is to relate this view of the affective content of communications deeper motivations in words. Qualitative
researchers use projective techniques and rich
to how people actually make decisions in the market. TNS has developed a two- stimulus material to try to link instinct and
pillar model of brand equity that gets to the heart of what actually drives sales. intuition to words – so that a person can say
what’s more deeply in their mind.
Theories of choice based on the idea that what people do is the result of
psychological preferences combined with situational factors, probably pre-date
ancient philosophers. In modern times, they show up in the distinction between
attitudinal and behavioral loyalty. Usually, attitudinally loyal people will buy the
brand to which they’re loyal if they can. But sometimes market (i.e. situational)
factors nudge people towards an alternative; or even prevent people from buying
the brand they want. And sometimes, when people have no strong first choice,
market factors tip the scales in favour of one brand rather than another.
We’ve used this simple framework for understanding brand sales for many
years3. In our framework, sales are a function of a brand’s ‘Power in the mind’
(attitudinal equity) and ‘Power in the market’ (market factors, brand presence,
market equity). These two dependent variables anchor our analysis of brand
equity and sales drivers.
We’ve recently updated these measures using surveys on behaviour panel
data. We can show that our new metrics outperform other similar metrics at
respondent level4, and we expect to continue to improve them in the months
to come.
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Power in the mind
Power in the mind is a respondent-level measure of brand attachment that
correlates better than similar measures with the real (panel validated) share
of spend that each brand gets from that person. And it achieves this with
a significant reduction in survey length.
We measure a brand’s power in the mind in two steps. First, we identify the
brands that are relevant to each respondent. Second, we ask for two ratings for
each relevant brand. The two dimensions that have to be measured are brand
performance and brand involvement. We use scales derived from the most up-
to-date neuroimaging survey measures5, and an algorithm underpinned by our
original theories of brand relationship6 to calculate from these a ‘one number’
measure of attitudinal brand equity. This correlates better with a person’s share
of consumption in panel data than other comparable metrics.
We use this number as a dependent variable for equity modeling; and also to
create equity segments and a brand health ‘ladder’. Because we leverage heuristic
principles7, this measure typically takes less than 30 seconds of survey time yet
results in brand health scores for every respondent for every category and brand
in a study. Continued improvements will further enhance accuracy over the
coming 12 months.
Power in the market
Power in the market is a respondent level measure of the market factors that
drive consumer behaviour. It offers a vital improvement in taking into account
the law of double jeopardy. According to this law, bigger brands gain in two
ways over smaller brands: they have more users, and their users tend to use
them more.
There are important problems with the law of double jeopardy, most notably
with its assumption that individual brand preferences are stationary over time8.
Nevertheless, the law highlights the benefits of scale that accrue to big brands.
These drive incremental sales for locally dominant brands; and create market
barriers for smaller brands.
There are a number of important ways in which brands can pull marketing levers
to drive sales: distribution, point-of-sale visibility, greater affordability, getting the
product mix right (packs and variants), purchaser preference (leveraging the fact
that the person who buys isn’t always the end-user), and creating
local monopolies.
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Like our ‘power in the mind’ measure, our ‘power in the market’ measure
leverages heuristic principles to cut survey time while increasing the validity of the
results. It typically takes less than 30 seconds and gives granular, respondent-level
information about the market drivers of sales for brands.
Put the two together and you have a powerful system of core metrics that takes
less than a minute of survey time to deliver equity and market information about
all brands at respondent level.
Gamification: a better way to ask questions
The gamification methods pioneered by Puleston and others can help us to solve
the problems of length, irrelevance, and boredom; and tap more effectively
into less conscious motivations by engaging the parts of the brain that are not
activated by classical, word-driven surveys.
Even when gaming methods aren’t very game-like, tests show that respondents
are much more engaged by these devices than they are by classical survey
methods. Mobile can play an important contributory role in applying gamification
more widely, since mobile devices provide a channel for incorporating this
approach into face-to-face interviews.
Intelligent, pro-active systems for ‘just-in-time’ information
Besides making surveys shorter, more relevant and more responsive, intelligent
systems can also be used to deliver actionable information and insight more
pro-actively. We are skeptical about the use of ‘early warning’ systems that rely
on single trend analysis such as moving averages, Bollinger bands, and the like.
Our reason is: a single trend doesn’t contain enough information to provide
intelligent alerts. We set more store by the analysis of anomalous gaps across
trends. By analyzing multiple trends gaps, we should be able to identify that
stresses are developing in the system. These stresses can be a powerful indicator
of opportunity or threat.
An example of a potentially anomalous gap would be when sales are under-
supported by equity. Over twenty years of brand health modeling, we’ve seen
such under-support often enough to know that it’s a sign that the brand’s sales
will come under pressure. Similarly, when equity exceeds sales, it’s a sign of
potential opportunity.
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How can we build anomalous gaps into analytical systems? A database of
relationships between the key variables in data streams can help to establish
the key anomalous gap values between such data points as marketing spend,
attitudinal equity and sales. We can then build intelligence into the tracking
system by automating the discovery of values in the data. This is a three-fold
process: automating the collection of instances such as turning points in market
share; populating a database with relevant instances that can trigger analysis;
and automating the updating process so that the the data-stream delivers
new instances.
Putting it all together: survey architecture for intelligent adaptive tracking
The TNS ConversionModel has been redeveloped along the principles set down in
this paper, to deliver respondent-level validity within an adaptive tracking approach
and reduced survey time. This approach enables the model to deconstruct market
share precisely and provide clear guidance on opportunities for brand growth.
The core ConversionModel study will now form the basis of future tracking
that is able to leverage an adaptive, heuristic architecture to ensure fewer, more
relevant questions and respondent-level validity around individual behaviour.
ConversionModel takes into account that people care about some decisions more
than others – and that this prioritisation varies by individual as well as by category.
In further developing the ConversionModel, and applying a new approach
to tracking more generally, we will develop survey architecture along the
following lines:
‘In the moment’ tracking activation
By taking measurement close to behavioral events, we can measure three key
things, with no more than three to four minutes for each event, diminishing over
time as machine learning kicks in:
„„ What people actually buy;
„„ Basic context information: where were they, what were they doing;
„„ Complete brand equity and market barrier information at a situational level
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The development of smart mobile devices and gamification survey techniques,
will improve compliance and validity of responses. Among respondents from
whom we get permission to install the MobileBehave app, we will enable
a three-fold integration of event-based behavior, situational brand equity,
and mobile ‘listening’ over time.
Analysis and delivery
We apply two levels of near real-time reporting and analysis:
„„ Basic: feeds back trend information e.g. buying, consuming; that can be
disaggregated according to ‘who’, ‘where’, ‘when’, ‘for what purpose’
„„ Analytic: feeds back information that requires algorithms based on trend
changes and, more importantly, gaps across trends
Examples of basic feedback include ongoing, real-time trend information about
what people are buying and consuming, where, and why. Basic feedback also
includes real-time information about category/brand situational equities and
situational drivers.
The analytic components of the system will be programmed to learn from
experience, identifying when positive or negative equity stresses develop. As an
example: when equity is high and consumption is low, this suggests a failure
of marketing. When equity is low and consumption is high, this suggests that
consumption is unsupported by psychological demand.
Intelligent, adaptive follow up surveys
The ‘in the moment’ survey process is the thin core. It gives us basic purchase and
consumption information coupled to situational brand equities and market barrier
information. As the diary builds, fewer questions will need to be asked. Questions
about situational equities, for example, only need to be asked once.
The follow-up survey happens after a set time period that could be daily, weekly
or monthly. Respondents will be channeled into questions that are relevant to
the way they make decisions, with different subsets for people with strong brand
preferences and people without, for example. We will know this from our analysis
of patterns of attitudes and behavior revealed in the diary survey.
By creating live adaptive questioning that is tailored to each respondent,
we can integrate big-ticket trackers into one system that combines all
relevant measurement areas: actual behaviour, brand equity, market factors,
communications influences, path-to-purchase, and point-of-consumption.
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The future of tracking conversations
A lot is spoken about the need for brands to engage consumers in meaningful
dialogue. Tracking surveys are no exception. The measures outlined in this paper You may
leverage what we know about consumers, markets and the human brain in order be interested in...
to conduct conversations that are relevant and meaningful for each respondent.
It makes for a more stimulating and enjoyable experience for those involved in The trouble with tracking by Jan Hofmeyr >
our surveys. And it makes for more valid, holistic and actionable information for ConversionModel >
our clients.
Commitment Economy >
The early results of this new approach can be seen in the insights delivered by
the 2012 TNS ConversionModel. However, evolving trackers to reflect consumer
decision-making more closely is an ongoing process. We are passionate about
delivering questions and answers that are valid for individual respondents in our
surveys. And we will continue to explore and apply new techniques in order to
do so.
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About TNS
TNS advises clients on specific growth strategies around new market entry,
innovation, brand switching and stakeholder management, based on long-
established expertise and market-leading solutions. With a presence in over Sources
80 countries, TNS has more conversations with the world’s consumers than
anyone else and understands individual human behaviours and attitudes 1: Dolnicar, Sara, Bettina Grun, and Friedrich
Leisch (2011) ‘Quick, simple, and reliable: Force
across every cultural, economic and political region of the world. binary survey questions,’ International Journal
of Research in Marketing, 53:2
TNS is part of Kantar, one of the world’s largest insight, information and 2: Plassman, Hilke, Peter Kenning, and Dieter
consultancy groups. Ahlert (2007), ‘Why Companies Should Make
Their Customers Happy: The Neural Correlates
Please visit www.tnsglobal.com for more information. of Customer Loyalty,’ Advances in Consumer
Research, 34:2
Get in touch 3: Hofmeyr, Jan H. and Butch Rice (2000),
Commitment-Led Marketing, John Wiley
If you would like to talk to us about anything you have read in this report, and Sons, Chichester
please get in touch via enquiries@tnsglobal.com or via Twitter @tns_global
4: Hofmeyr, Jan, Victoria Goodall, Marting
Bongers, and Paul Holtzman (2008), ‘A new
measure of brand attitudinal equity based on
the Zipf distribution,’ International Journal of
Marketing Research, 50:2;
Keiningham, Timothy L., Lerzan Aksoy,
Alexander Buoye, and Bruce Cooil (2011),
‘Customer Loyalty isn’t Enough. Grow your
Share of Wallet,’ Harvard Business Review,
October
5: Reimann, Martin, Requel Castano, Judith
Zaikowsky, and Antione Bechara (2011),
‘How we relate to brands: Psychological and
Neurophysiological insights into Consumer-
Brand Relationships,’ Journal of Consumer
Psychology, (forthcoming)
6: Hofmeyr, Jan H. and Butch Rice (2000),
Commitment-Led Marketing, John Wiley
and Sons, Chichester
7: Gigerenzer, Gerd, Peter M. Todd, ABC
Research Group (2000), Simple Heuristics That
Make Us Smart, Oxford University Press, USA.
8: Hofmeyr, Jan, Victoria Goodall, Marting
Bongers, and Paul Holtzman (2008), ‘A new
measure of brand attitudinal equity based on
the Zipf distribution,’ International Journal of
Marketing Research, 50:2;
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