COMMENTARYThis is your brain on neuromarketing reflection

COMMENTARY
This is your brain on neuromarketing: reflections on a
decade of research
Nick Lee a, Leif Brandesa, Laura Chamberlaina and Carl
Seniorb
aWarwick Business School, Warwick University, Coventry, UK;
bLife and Health Sciences, Aston University,
Birmingham, UK
ABSTRACT
In this commentary, we reflect on the last decade of research in
the field of neuromarketing and present a schematic illustration
of
the basic process of a typical neuromarketing study. We then
identify three critical points of interest in this illustration that
have not received enough discussion in neuromarketing-relevant
literature, and which we consider to be somewhat problematic.
These are the dominance of event-based designs in
neuromarket-
ing, the potential of alternative modalities in neuromarketing
and
the current focus on reverse inference in neuromarketing. We
argue that, taken together, these points have substantive
implica-
tions for the development of a more reflective neuromarketing,
which in turn has greater potential to make a positive impact on
marketing knowledge, marketing practice and public
perceptions
of marketing activity in general.

KEYWORDS
Neuromarketing; consumer
neuroscience; brain activity;
fMRI; reverse inference
In 2007, two of this present author team published one of the
first academic papers to
mention the term ‘neuromarketing’ (Lee, Broderick, &
Chamberlain, 2007). Whether that
paper, or the one of Fugate (2007), was actually the first to use
the term in a published
scholarly article (although Smidts [2002] did use this term in
his address to the Erasmus
Institute of Management) is less important than the clear
conclusion that we are now
around a decade on from the earliest attempts to provide some
kind of coalescing of the
various diverse strands of then-existing work, into what could
pass as an embryonic
‘field of research’. Before then, marketing-relevant work had
appeared across various
different disciplinary boundaries, sometimes in marketing
journals (e.g. Ambler,
Braeutigam, Stins, Rose, & Swithenby, 2004), sometimes in
economics or decision
science (e.g. Camerer, Loewenstein, & Prelec, 2005) and
sometimes in neuroscience
itself (Braeutigam, Stins, Rose, Swithenby, & Ambler, 2001).
Since 2007 however, the last decade has seen, if not quite an
explosion, then certainly
a major upsurge in neuromarketing research in the marketing
literature. From a point in
2007 where Lee, Broderick and Chamberlain felt able to point
out a ‘lack of take-up of
brain imaging methodologies in marketing science’ (p. 199), we

are now in a situation
where a special issue of one of our discipline’s top research
journals has been dedicated
CONTACT Nick Lee [email protected] Warwick Business
School, Warwick University, Coventry CV4 7AL, UK
JOURNAL OF MARKETING MANAGEMENT, 2017
VOL. 33, NOS. 11–12, 878–892
https://doi.org/10.1080/0267257X.2017.1327249
© 2017 Westburn Publishers Ltd.
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http://orcid.org/0000-0002-6209-0262
http://www.tandfonline.com
http://crossmark.crossref.org/dialog/?doi=10.1080/0267257X.20
17.1327249&domain=pdf
to neuromarketing (Camerer & Yoon, 2015), and it is no longer
unusual to see individual
studies appearing in the marketing literature that use
neuroscientific methods.
Conference sessions are regularly held to discuss

neuromarketing issues (e.g. Reimann,
Hedgcock, & Craig, 2016), and agendas for work in the area
appear on a non-infrequent
basis (e.g. Hubert & Kenning, 2008; Reimann, Schilke, Weber,
Neuhaus, & Zaichkowsky,
2011; Smidts et al., 2014; Solnais, Andreu-Perez, Sánchez-
Fernández, & Andréu-Abela,
2013). Indeed, neuromarketing and its associated term
‘consumer neuroscience’ (e.g.
Javor, Koller, Lee, Chamberlain, & Ransmayr, 2013; Kenning &
Linzmajer, 2011;
Plassmann, Venkatraman, Huettel, & Yoon, 2015) have become
increasingly popular
topics of both empirical research and conceptual theorising. It
seems opportune at
this point then, to step back and take some stock of whether or
not the promise
identified in early neuromarketing papers has been fulfilled.
In this commentary, we reflect on the last decade of research in
what we loosely
define as the neuromarketing field. In particular, we present a
basic schematic
framework that allows us to unpack a number of areas which we
see as somewhat
problematic. While they are not all unique to marketing, and for
the most part have
been covered in other fields of study (including neuroscience
itself), it strikes us that the
inherent subject matter of marketing research may make
neuromarketing a field that is
particularly susceptible to these problems. While we are unable
to provide total
solutions to these issues, we are able to point the reader towards
potential directions
in which such problems can be more coherently addressed, and

advances in
neuroscientific research that may help solve them.
Neuromarketing research: an illustrative framework
Figure 1 presents what could be considered an illustration of the
typical empirical
neuromarketing study. At this point, it is important that we will
use the term
‘neuromarketing’ to refer to research using methodologies
drawn from cognitive
neuroscience that attempt in some way to measure brain
activity, that is techniques
such as electroencephalography (EEG),
magnetoencephalography (MEG), positron
emission tomography (PET) and by far the most popular in this
field, functional
magnetic resonance imaging (fMRI). We also would consider
techniques such genetic
studies, skin conductance response and the like to be within the
field of
Brain Activity
Measure
Interpretation
of Meaning
MeasureBehavioral
Response
Stimulus
Figure 1. Conceptual schematic of neuromarketing research.

JOURNAL OF MARKETING MANAGEMENT 879
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neuromarketing. We recognise that this is a reasonably informal
definition, but it does
tend to chime with similar work in management and
organisational research (e.g. Senior,
Lee, & Butler, 2011).
Figure 1 visualises the basic process of a typical
neuromarketing study, although of
course we do recognise that there will be variance here, and
also that some studies may
operationalise a subset of these tasks and links. However, we
feel this diagram presents
a useful starting point from which to discuss the various key
issues germane to
neuromarketing research. Specifically, we consider that there
are three critical points
of interest here, which have not received enough discussion in
neuromarketing-relevant
literature. Taken together, they have substantive implications
for the development of a

more reflective neuromarketing, which in turn has greater
potential to make a positive
impact on marketing knowledge, marketing practice and public
perceptions of
marketing activity in general. All three issues will be discussed
in depth below,
beginning with the dominant event-related reactive (i.e.
stimulus-response) design of
typical neuromarketing work. Following this, we will discuss
the ability of the methods
typically used in neuromarketing research to actually measure
brain activity.
Subsequently, we address important issues concerning
inference. In other words, even
if we measure brain activity in response to some stimulus, what
does this actually tell us?
This latter issue has received some attention in marketing-
relevant studies, but we
believe a significant proportion of neuromarketing work has
fallen prey to some key
errors of inference, which bears greater discussion. Following
this, we will address why
marketing research is in a particularly dangerous position with
regards to these issues
and present some recommendations for future neuromarketing
work, to maximise its
potential contribution.
On the dominance of event-based designs in neuromar keting
The typical study design used in neuromarketing could be called
event based, or
perhaps stimulus based. Such a design is essentially what most
researchers consider
as the traditional controlled-experimental design. That is,
subjects are exposed to some

(hopefully) well-designed experimental stimulus, and their brain
activity is measured,
usually along with some behavioural response (e.g. a choice).
Further, some other
physiological and/or psychological variables may be measured
(perhaps as controls),
and the variables utilised in a regression-based analysis
framework. Such approaches are
dominant in cognitive neuroscience in general, and also in
neuromarketing. Indeed, an
informal review of neuromarketing research could find no
empirical work that
significantly differed from these fundamental principles.
This event-based design implies a view of the brain as a
reactive system, where the
brain receives sensory inputs, which cause some neural activity,
which in turn cause
some behavioural or cognitive/affective response of interest
(Raichle & Snyder, 2007).
While the approach has been the foundational workhorse of all
cognitive neuroscience,
it is also not without significant limitations. In particular, it has
been observed that
responses to the same stimulus are highly variable across
multiple trials, even in so basic
a setting as the measurement of response times (Braeutigam,
Lee, & Senior, 2017). Such
variations may be due to the endogenous activity that is present
at all times within the
human brain. Specifically, it is the case that the brain is never
inactive, simply waiting for
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some stimulus to respond to. While most people readily
understand that brain activity is
necessary for basic homeostasis (i.e. the activity necessary for
us to stay alive), fewer are
aware that the brain’s spontaneous activity is far more complex
and significant. For
example, in recent years, it has become well accepted that this
intrinsic brain activity is
not simply random noise, or due to mental tasks (e.g. like
daydreaming). Rather, the
resting activity of the brain also occurs completely
spontaneously and can be described
by specific patterns of coherence, observable across many
different brain regions,
beyond those necessary to maintain life (Braeutigam et al.,
2017). Importantly, it
seems that the brain’s resting activity actually requires almost
the same amount of
energy as even very demanding task-related activity (Raichle &
Mintun, 2006), implying
that task-focused activity is simply a temporary redistribution
of energy. That resting
activity consumes such a huge proportion of the body’s

metabolic rate (around 20%)
which implies that in and of itself, it plays some crucial role in
human life.
In more recent years, various researchers have investigated
intrinsic (also known as
endogenous or spontaneous) brain activity. For example, much
attention has been focused
on what is known as the default mode network, which has been
suggested to be somehow
implicated in self-awareness and social cognition (e.g.
Schilbach, Eickhoff, Rotarska-Jagiela,
Fink, & Vogeley, 2008). Further, an emerging consensus is that
endogenous brain activity
plays some role in the variability across identical trials within
the same subject (Mennes
et al., 2010). In other words, it seems likely that endogenous
brain activity somehow
interacts with stimulus-driven activity in a complex non-linear
way (Huang et al., 2015).
In particular, Braeutigam et al. (2017) report the growing
evidence that endogenous brain
activity somehow influences perception, memory, motor control
and decision-making.
Perhaps most importantly for our purposes here, Braeutigam
(2007) showed that
endogenous brain activity differences were associated with
differences in choice-making
within a retail product choice context. In other words, the
ongoing activity of the brain
could be used to predict a subject’s choice of product before the
subject even saw the
choice option stimuli.
If endogenous brain activity is somehow implicated in our
responses to stimuli, it

seems that existing neuromarketing research is only able to give
us part of the
explanation for how we make choices or respond to marketing
stimuli. Importantly, it
is impossible to capture such influences using the traditional
event-based experimental
designs. What is needed are pre-stimulus designs, where brain
activity is measured prior
to subject exposure to the experimental stimuli. Braeutigam et
al. (2017) provide an
introduction to this field of work, as well as the complex
mathematics involved in
capturing endogenous brain activity in a useful way. However,
beyond this, it may be
necessary to move beyond the dominance of fMRI methods if
we wish to provide more
insight into endogenous brain activity. Specifically, as will be
seen below, fMRI has poor
ability to resolve temporal information, which is crucial in such
pre-stimulus designs.
Indeed, most significant research in this area uses techniques
such as EEG and MEG,
which have much better temporal resolution.
Beyond fMRI: the potential of alternative modalities in
neuromarketing
As already mentioned, fMRI dominates neuromarketing
research, although EEG is also a
popular method. The picture is similar in cognitive neuroscience
itself, where fMRI is by
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far the most dominant modality in empirical research. This
dominance is such that it
appears some critical observers of management and business
research are under the
impression that fMRI-based research is neuroscience (e.g.
Lindebaum, 2016). Figure 2
however shows that there are a number of other techniques
which could be employed
to investigate neuromarketing questions. Figure 2 focuses on the
variety of techniques
which we argue below are most useful to neuromarketing
research, and while we
include a number of other techniques for reasons of exposition,
it is important to
recognise that the toolkit of neuroscience itself is broader than
shown in Figure 2.
The first and possibly most important thing to note here is that
fMRI does not in fact
– contrary to popular believe – measure brain activity itself.
Rather, fMRI (as employed in
neuromarketing) measures what is known as the BOLD (blood
oxygenation level
dependent) response (although other contrasts are possible). In

essence, this relies on
the idea that increased brain activity in a given region results in
increased blood flow to
the active area. However, it should be noted that this response
is not actually the brain
activity itself, but in essence a proxy. It is important to
understand that there are various
limitations and caveats to the interpretation of the BOLD
response as a proxy measure of
brain activity. While most are beyond the scope of this
commentary (we refer interested
readers in particular to Heeger & Ress, 2002; Logothetis, 2008),
we focus here on issues
concerning spatial and temporal resolution (as presented in
Figure 2). Specifically, fMRI
is generally considered to have a strength in terms of spatial
resolution, in that it can
systems
EDA
Neuromarketing and Consumer
Neuroscience
MEG, EEG, SST
TMS
MRS fMRI
single cell recordings
Neuro
Psychology
PET

brain
region
column
layer
Sp
ac
e
(lo
g
Sc
al
e)
neuron
dendrite
synapse
millisecond second minute hour
Time (log Scale)
day year lifetime
Figure 2. Overview of various neuroscientific techniques with
those relevant to neuromarketing
highlighted.

Modified from Senior et al. (2011).
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accurately resolve location (dependent on the accuracy of the
BOLD contrast as a proxy
for actual neuronal activity). However, the BOLD response has
comparatively poor
temporal resolution, lagging actual activity in the order of
seconds. This implies that
fMRI is unsuitable to examine very quick or transient processes,
and most useful to
explore processes lasting a few seconds or more. In particular,
research projects
involving dynamic processes (e.g. watching TV advertisements)
do require significant
attention paid to their design; otherwise, the results are broadly
meaningless due to this
issue. Another significant issue with fMRI is the growing
concern regarding the statistical
analysis of fMRI data, based both on the necessarily huge
number of statistical

comparisons involved (e.g. Vul, Harris, Winkielman, & Pashler,
2009), and potential
issues with common software (Eklund, Nichols, & Knutsson,
2016). Concerns regarding
sample size of fMRI studies are also of note (e.g. Button et al.,
2013), although Butler, Lee
and Senior (2017) show that the issues are more complex than
are commonly
understood.
Given the dominance of fMRI in mainstream cognitive
neuroscience, it is
understandable that it also assumes a dominant position in
neuromarketing research.
However, the issues pointed out above suggest that other
modalities also have much to
offer, particularly when cost-effectiveness is taken into account.
Unfortunately, apart
from EEG (e.g. Boksem & Smidts, 2015; Pozharliev, Verbeke,
Van Strien, & Bagozzi, 2015),
neuromarketing studies since 2007 appear to have largely
ignored the potential for
insight from alternative methods. However, the superior
temporal resolution of
techniques such as MEG (along with EEG) mean they have
much to offer
neuromarketing, particularly as the complexities of the decision
process itself become
more and more apparent. Prior to 2007, a few studies did
employ MEG to investigate
consumer choice contexts (e.g. Ambler et al., 2004; Braeutigam
et al., 2001), but it
appears these studies were the result of a serendipitous
collaboration and did not
inspire the long-term take up of MEG in neuromarketing. That
said, Braeutigam et al.

(2017) aforementioned introduction of endogenous brain
activity into organisational
research may have a galvanising effect on neuromarketing
researchers regarding the use
of MEG and EEG. Another method worth some attention is
Steady State Topography
(SST). This method was pioneered by Silberstein and colleagues
(1990) and combines
EEG with special goggles. A number of studies have employed
SST in advertising
research (Rossiter, Silberstein, Harris, & Nield, 2001;
Silberstein & Nield, 2008). One
wonders however whether the fact that SST is a proprietary
technology has led to its
lack of take-up in academic research.
We certainly believe that greater attention to the use of MEG
(and EEG) would
provide a useful contribution to neuromarketing. However,
again, it is important that
researchers understand that these techniques also have
limitations, most obviously the
difficulties in localising the source of the electromagnetic
signal within the brain.
Furthermore, like fMRI, neither EEG nor MEG actually
measures brain activity, but
rather the secondary potentials arising from it (again making it
a proxy), and also
requires a reasonably large amount of synchronous neuronal
activity to occur, in order
to produce an electromagnetic signal large enough to be
detected. Finally, while EEG is
cost-effective and commonplace, MEG is more expensive and
complex than fMRI.
Electrodermal activity (EDA) has also seen some use in

neuromarketing (e.g. Gakhal &
Senior, 2008). While it is generally well understood that EDA is
not a measure of brain
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activity, it is a well-validated measure of emotional arousal
(Lee & Chamberlain, 2007). As
such, it does allow an indirect implication of cortical activity at
the overall system level
(Senior et al., 2011). Its particular benefit here is the
accessibility in terms of cost, and
ease of use, as well as the lack of invasiveness of the technique.
Furthermore, EDA can
be usefully combined with other neuroimaging methods, such as
fMRI. Similarly,
transcranial magnetic stimulation (TMS) can also be usefully
combined with other
methods, to both counter some of their drawbacks and enhance
understanding. TMS
allows researchers to safely occlude activity in cortical areas. In
other words, it allows the

researcher to stop particular areas of the brain working (Stewart
& Walsh, 2006). Doing
so can help provide positive evidence of the necessity of a given
area for a given task.
This allows one to go beyond the simple observation of brain
activity in association with
a given task as done with fMRI, MEG and suchlike, towards
inferring the causal necessity
of a given area of brain activity for the completion of the task
(i.e. the task cannot occur
if that region of the brain is not active). The key drawback of
TMS is that it can only be
used on accessible areas of the cortex, rather than deep brain
structures (many of which
seem relevant to neuromarketing explanations). However, one
can possibly use
neuropsychological participants with either natural or
pathological brain lesions in
deep areas to explore such questions (e.g. Koenigs & Tranel,
2008). That said, ethical
questions regarding the use of neuropsychological patients for
marketing research are
of some importance here.
Finally, we will discuss the other techniques noted on Figure 2,
which for better or
worse have little to no application to neuromarketing. PET
involves exposing the
participant to a radioactive tracer (either by inhalation or
injection), which can then be
used to visualise blood flow to areas of the brain, similar to
fMRI. Before the advent of
fMRI, PET was a key method of functional neuroimaging.
However, in recent years, its
relative disadvantages (cost, lower spatial resolution,
invasiveness) have meant that for

cognitive neuroscience research at least (rather than medical
purposes), PET has fallen
from favour. Nevertheless, it does have some advantages over
fMRI, not least that it is far
less sensitive to small movements. Single cell recordings are
included not because they
are a useful neuromarketing technique, but because they
exemplify the difference
between measuring actual brain activity and the various proxies
discussed above.
Single unit (or single cell) recording involves the use of
microelectrodes to directly
measure brain activity at the level of a single neuron. However,
this is exactly as
invasive as it sounds. This renders its use virtually impossible
for neuromarketing or
other organisational research purposes (Butler, Lee, and Senior
2017). Indeed, it is only in
clinical contexts that they can be used in humans (although they
are commonly used in
animals). That said, Cerf, Greenleaf, Meyvis, and Morwitz
(2015) demonstrate that
neuromarketing-relevant concepts (along with others) can be
studied while patients
are receiving clinical treatment for epilepsy, making single-cell
recording at least
somewhat viable in the right circumstances.
Inference in neuromarketing: what do brain scans actually tell
us?
Finally, we address questions regarding what inferences can be
drawn from
neuroscientific data, even if it does accurately reflect actual
brain activity. In essence,
the issues at hand concern (a) whether or how we can actually

usefully infer anything
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about psychological theories from neuroscientific data, and (b)
a more fundamental
metaphysical concern regarding the reality of our subjective
experiences of the world.
Both issues are of course necessarily more philosophical than
empirical in nature. While
the former has received some attention in neuromarketing (e.g.
Plassmann et al., 2015),
the latter is yet to be examined at all and has in fact rarely been
touched on outside
philosophy (Bagozzi & Lee, 2017).
Beginning with issues of inference, there are two basic ways
that inferences can be
drawn in neuromarketing-type studies. The first is termed
forward inference, introduced
by Henson (2006). In essence, a forward inference approach
uses differential patterns of

brain activity to distinguish between different psychological
theories. For example, if
there are two competing explanations of a given phenomenon,
and if ‘theory 1 predicts
that the same cognitive processes underlie two different
experimental tasks, and theory
2 predicts that the tasks differ in terms of at least one cognitive
process, then theory 2
will be supported when patterns of brain activity differ between
the two tasks’ (Heit,
2015, pp. 2). Such an approach depends on the assumption that
there is at a minimum
some kind of meaningful mapping from hypothesised
psychological processes to actual
brain activity, as well as that there is no unknown but correct
third theory nor any
significant extraneous differences across experimental tasks.
Such issues appear soluble
(notwithstanding the metaphysical issues to be discussed later),
but a more pressing
problem is that not all psychological theories may make clear
predictions of brain
activity, rendering the forward inference concept moot. This
may be why forward
inference approaches appear to be very rare in neuromarketing
research, although the
same could also be said for cognitive neuroscience in general
(Lee, Senior, & Butler,
2012).
Poldrack (2006) explains the concept of reverse inference in
depth, and a number of
conceptual neuromarketing papers have drawn from this to
present their own take on
the topic (e.g. Breiter et al., 2015; Plassman et al., 2015).
Poldrack (2006, pp. 2)

characterises reverse inference as the ‘logical fallacy of
affirming the consequent’, and
thus invalid for deductive inference. Poldrack (2006) shows
how reverse inference is
extremely prevalent in cognitive neuroscience using fMRI, and
we see the same in
neuromarketing. In simple terms, a reverse inference is made as
follows (as described
by Poldrack, 2006, pp. 1):
(1) In the present study, when task comparison A was presented,
brain area Z was
active.
(2) In other studies, when cognitive process X was putatively
engaged, then brain
area Z was active.
(3) Thus, the activity of area Z in the present study
demonstrates engagement of
cognitive process X by task comparison A.
The fallacy was most famously demonstrated by the 2011 New
York Times op-ed by
Martin Lindstrom entitled ‘You Love Your iPhone, Literally’,
which claimed that activity in
the insular cortex when subjects heard their phones indicated
that they loved them. Of
course, as pointed out by Poldrack (and 44 other
neuroscientists) in their letter to the
NYT on 5 October 2011, this inference is nonsensical, being
that the insular cortex is
‘active in as many as one third of all brain imaging studies . . .
[and] . . . more often

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associated with negative than positive emotions’. Similar
problems can be detected very
frequently in both popular and scholarly neuromarketing-
relevant research. The key
problem with reverse inference is that it is rare to see a
consistent mapping between
a given brain area, and a particular psychological process. That
said, reverse inference is
only a true fallacy if it is used in a deductive fashion and can
indeed be useful to develop
hypotheses for further study (Hutzler, 2014; Poldrack, 2011),
particularly when utilised in
conjunction with large-scale machine learning or meta-analysis.
Even if one could justify strong inferences from brain scans to
presumed mental
processes though, the question would still remain, just what
does this mean? In other
words, is there anything more to mental experience than brain
activity? What exactly are
these psychological processes that our theories refer to? Do they

have some
independent reality over and above their physical manifestations
(i.e. brain activity
etc.), or are terms like ‘emotion’, ‘attitude’, ‘thought’ or any
subjective experience at
all, simply metaphors or folk-terms that have been developed to
describe what was
heretofore mysterious? If so, should we now devote all our
attention to further study of
their physical manifestations and phase out theories which refer
to these metaphorical
entities or properties, since we now have little justification to
consider them real? Such
questions are rarely explored by neuroscientists themselves,
who appear to work under
the assumption that indeed the mental experience is ultimately
reducible to physical
events (Bagozzi & Lee, 2017), and this view would appear to be
shared by many in
neuromarketing. This is especially evident in the common
justifications of neuroscientific
methods as being able to somehow uncover ‘hidden’ or ‘more
accurate’ data (e.g.
Couwenberg et al., 2016; Rampl, Opitz, Welpe, & Kenning,
2016). While such an
approach has its attractions, it does little to provide a
convincing explanation of how
our subjective experiences (e.g. consciousness) are the
necessary (as opposed to
contingent) consequence of our physical brain activity (Nagel,
2012) and thus does
not discount the possibility that brain states and subjective
mental states are actually
different things (Kripke, 1980). Such questions have significant
bearing on how we
should approach the future of neuromarketing, and indeed social

sciences in general
(Bagozzi & Lee, 2017), but they are yet to be addressed in any
depth within the field.
Conclusions and directions for the coming decade of
neuromarketing
Ten years on from the publication of the first scholarly articles
using the term
‘neuromarketing’ (at least that we can find), have we really
come that far? There
are at least two ways of answering that question. On the more
negative side, it might
be argued that for the most part, we remain in the same basic
position as we did in
2007. That is, a reasonably fragmented set of research teams,
individually pursuing
what could be seen as quite piecemeal topics, spread across a
wide variety of
publication outlets, both within and outside marketing itself.
Few articles appear to
have addressed whether neuroscientific insights can help us
build new and improved
explanations of marketing phenomena, and the majority of
published studies tend to
use neuroscientific methods (most usually fMRI and sometimes
EEG) to gain what are
considered more accurate insights into existing marketing
explanations. Rarely are
competing theories tested (which would hopefully enable a
forward inference
approach), and often researchers fall into the tempting trap of
reverse inference,
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assuming that complex psychological processes can be localised
to individual brain
areas, which are necessary and sufficient for their occurrence.
Such temptations are
most clearly seen in that work which receives significant public
attention, and
marketing is far from alone here. But in general, marketing and
other social
sciences are fertile ground for the growth of dangerous over-
inferences from brain
activity to psychological and social processes.
On a more positive note, it is undeniable that neuroscientific
methodologies are now
accepted as a viable tool to study marketing phenomena. This
has to be seen as a
positive, as it expands the set of tools available to scholars and
also provides reasonably
strong evidence that neuroscientific methods can provide strong
contributions to
advancing knowledge. Further, the increasing attention given to
neuromarketing in

the top marketing journals should have the effect of inspiring
more and more
researchers to investigate the potential of neuroscience for their
own work. However,
for these positive effects to become more dominant in future, a
number of key issues are
in need of greater attention.
The most important issues remain based around understanding
(a) the capabilities
and drawbacks of different neuroscientific methods, (b) the
benefits of studying biology
and the brain for understanding marketing phenomena and (c)
the conceptual problems
which must be solved before drawing conclusions from
neuroscientific data.
Considerable work on various aspects of these issues has
appeared in management
and organisational research (e.g. Becker, Croponzano and
Sanfey, 2011; Healey &
Hodgkinson, 2014; Lindebaum, 2016; Senior et al., 2011;
Waldman, Balthazard, &
Peterson, 2011; Butler, Lee, & Senior, 2017), but marketing has
yet to have a robust
discussion around many of these issues. Further, few marketing
studies have engaged
with the relevant management literature on these topics, nor
with the more
foundational neuroscience work. Gaps in understanding such as
this are dangerous
and may lead to poor research with meaningless results, such as
how we love our
iPhones. Work such as this (i.e. Lindstrom, 2011), while not
published in scholarly
journals, is often placed in the same general category as
academic work by both the

general public, and also those who may work in neuroscience
itself. This leads to a
generally negative perception of neuromarketing amongst just
those people who we
would wish more enthusiastic about working with marketing
colleagues to investigate
important problems. Indeed, fruitful collaboration between
marketing and
neuroscientific scholars is most likely to lead to genuine
contributions to our
knowledge (e.g. Breiter et al., 2015).
So, in conclusion, our manifesto for a neuromarketing which
can in the future make a
greater contribution to knowledge would probably run along the
following lines:
(1) A robust debate amongst both supporters and detractors of
the neuroscientific
approach to marketing research, hosted by high-impact journals
with wide read-
erships. This debate should take in how neuroscience can help
understand
marketing phenomena (or not), the ethics of employing such
methods, what
inferences can be drawn and suchlike.
(2) Greater attention to diverse modalities of neuroimaging,
such as MEG, and TMS,
as well as much greater attention given to the disadvantages
associated with each
method and the analysis of data.
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(3) More research that focuses explicitly on testing competing
theories (forward infer-
ence), and on developing broad explanatory frameworks (e.g.
Breiter et al., 2015).
(4) Following the framework proposed in Bagozzi and Lee
(2017), greater attention
paid to linking various levels of explanation, from
physical/objective, to mental/
subjective. Or at least, further investigations and discussions on
the relevance of
both/either for our marketing (and social scientific) theories and
explanations.
(5) Perhaps most importantly, greater collaboration across
marketing and neuros-
cientific researchers to create work with both stronger
methodological founda-
tions, and larger theoretical contributions. We would also add
that collaborations
with philosophers may be useful, to further develop ideas
regarding objective/
subjective experience, inferences and suchlike.

While the above may seem a daunting list, it is not particularly
far from how the fields
of management and organisational research have addressed the
very same issues. We
have every confidence that marketing scholarship is willing to
engage in a similar way,
with the same goal – that of increasing the value of what we do,
and further advancing
knowledge into important marketing phenomena.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Prof. Nick Lee (BCA, BCA (hons.), PhD) is a Professor of
Marketing at Warwick Business School. His
research interests include sales management, social psychology,
cognitive neuroscience, research
methodology and philosophy of science. Prof. Lee is the Edi tor-
in-Chief of the European Journal of
Marketing, and the Associate Editor of the Journal of Personal
Selling and Sales Management. Nick
has received multiple awards for his research and published
over 60 articles since 2005 in journals
such as Organizational Research Methods, Organization
Science, the Journal of Management, Human
Relations, the Journal of the Academy of Marketing Science,
the Journal of Business Ethics and
Frontiers in Human Neuroscience. His work has received over
4000 citations, while also featuring in
popular outlets such as The Times, the Financial Times and
Forbes and on numerous radio and
television programmes. He is also the author of Doing Business

Research and Business Statistics
using Excel and SPSS. He received his Ph.D. from Aston
University (UK) in 2003.
Dr Leif Brandes is an Assistant Professor of Marketing and
Behavioural Science at Warwick Business
School. His research interests include judgement and decision-
making, online word of mouth and
information transparency. Leif’s research has appeared in
journals such as Management Science,
Marketing Letters, Journal of the Royal Statistical Society
(Series A) and Journal of Economic Behavior
& Organization. His work has also been mentioned in popular
outlets such as Wall Street Journal,
Ivey Business Journal, the Independent, the Sun, Yahoo! and
BBC Radio. Leif holds a Ph.D. from the
University of Zurich (Switzerland) and a Master in
Mathematical Finance from the University of
Konstanz (Germany).
Dr Laura Chamberlain is a Principal Teaching Fellow of
Marketing at Warwick Business School. Her
research interests include consumer behaviour, neuromarketing,
social marketing, advertising,
research methodology and experimental design. Her work has
appeared in Frontiers in Human
Neuroscience, Academy of Marketing Science Review, BMC
Neurology, Journal of Marketing Education,
888 N. LEE ET AL.
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International Journal of Psychophysiology and Annals of New
York Academy of Science. Laura holds a
PhD from Aston University (UK).
Carl Senior joined Aston University in 2004, after holding a
visiting fellowship in Behavioural
Sciences at the National Institutes of Health, USA. He has over
100 publications and has con-
tributed to securing over £3M in various research income. He
has served as Associate Editor for the
journals Cognitive Neuroscience, Frontiers in Cognitive Science
and The Leadership Quarterly and was
elected a Fellow of the British Psychological Society in 2015.
He is currently a member of the Aston
Behavioural Insights Group.
ORCID
Nick Lee http://orcid.org/0000-0002-6209-0262
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http://dx.doi.org/10.5465/AMP.2011.59198450
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Article Notes The Metrics that Marketers Muddle-
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Article Notes 3
Bendle, N. T., & Bagga, C. K. (2016). The metrics that
marketers muddle. MIT Sloan Management
Review, 57(3), 73-82.
Despite their widely acknowledged importance, some popular
marketing metrics are regularly misunderstood and

misused. One major reason for marketing’s diminishing role is
the difficulty of meaning its impact: The value marketers
generate is often difficult to quantify. The main goals of this
article are to understand how these marketing metrics are
used and understood and to develop ideas to help marketers
unmuddle their metrics. The authors conducted surveys
from managers from all functions across the business-to-
business and business-to-consumer industries.
5 Best Known Marketing Metrics:
- Market share
- Net Promoter Score (NPS)
- The Value of a ‘Like’
- Consumer Lifetime Value (CLV)
- Return on Investment (ROI)
Market Share
Market share is a popular marketing metric. One reason for why
manager value market share is that research
from the 1970s suggested a link between market share and ROI;
however, the linkage may be less clear: the
studies have found it is often correlational rather than causal.
The survey found that there were two ways
managers used market share: as an ultimate objective or as an
intermediate measure of success. Increasing
market share is not a meaningful ultimate objective for
maximizing shareholder value and stakeholder
management: If the aim is to maximize the returns to
shareholders, increased market share offers no benefits

unless it eventually generates profits. In some markets, bigger
can be better; however, economies of scale do not
automatically apply all markets.
Unmuddling Market Share:
The authors suggest a simple set of rules for the appropriate use
of the market share metric:
- Managers should not consider market share as the ultimate
objective or as a proxy for absolute size.
- Managers should evaluate it from the competitors’ and
consumers’ point of view. If an increase in market
share is not going to get positive feedback from competitors and
consumers, then an increase in market share
will not lead to a productive result.
- Managers should analyze whether market share drives
profitability in your industry. Companies with
superior products tend to have high market share and high
profitability because product superiority causes both.
This means that the two metrics are correlated, BUT it does not
necessarily mean that increasing market share
will increase profits.

Net Promoter Score (NPS)
This metric is used to measure customer loyalty to a firm.
Companies among diverse industries have embraced
NPS as a way to monitor their customer service operations
while NPS also has been seen as a system that allows
managers to use the scores to shape managerial actions.
One of the advantages of NPS is its simplicity: It is easy for
managers and employees to understand the goal of
having more promoters and fewer detractors. However, there are
weaknesses: E.g., in the net promoter literature,
a customer’s worth to Apple has been described as the
customer’s spending, ignoring the costs associated with
serving the customer. It is also easy to imagine how to increase
the net promoter score (such as making
customers happier) while destroying even to-line growth (by
slashing prices). Another problem with NPS as a
metric is the classification system: The boundaries between
scores of 6 and 7 (detractors and passives) and 8 and
9 (passive and promoters) seem somewhat arbitrary and
culturally specific.
Unmuddling NPS:
The value of NPS depends on whether a manager sees it as a
metric or as a system. The authors suggest that the
NPS metric cannot change the marketing performance.
However, they advise using this metric as a part of a
system employed in evaluating the performance which might
lead to a cultural shift within the organization.
The Value of a ‘Like’
This metric is used for measuring the social media capital of the

company. New approaches are being developed
all the time and they have the potential to aid understanding of
how social media creates value. It is measured as
the difference between the average value of customers
endorsing the company and the average value of the
customers who are not endorsing the company. The majority of
managers link between their social media
spending the value of a ‘like’. However, it does not mean that
the cause of the differences in users’ value is
attributable to a company’s social media strategy. And the
reason that social media strategy shouldn’t be seen as
the driver of value difference between fans and nonfans is
because customers who are social media fans will
differ from nonfans for reasons unrelated to the company’s
social media strategy.
Unmuddling the Value of a ‘Like’:
This difference between two groups of consumers does not
suggest an effect of online marketing activity or lack
thereof. It should be investigated thoroughly by the managers. If
the management is using the revenue to
measure customer value, then this marketing metric does not
give a good estimate. However, if the company
does want to understand the impact of social media marketing,
they should use randomized control experiments
to derive causal answers.
Consumer Lifetime Value (CLV)
Consumer lifetime value (CLV), which is the present value of
cash flows from a customer relationship, can help
managers in decision making related to investment in
developing customer relationships, as it is used to measure
the value of the current customer base. If the management is
using the customer value in their decision-making

process, then CLV is a useful tool for them.
Unmuddling CLV:
The authors suggest that CLV calculations should not include
the customer acquisition cost and the estimated
CLV should be compared to the estimated acquisition cost to
derive conclusions. The bigger the difference
between the estimated CLV and the estimated acquisition cost,
the better the acquisition campaign.
Return on Investment (ROI)
Return on investment is a popular and potentially important
metric allowing for the comparison of disparate
investments. A critical requirement for calculating ROI is
knowing the net profit generated by a specific
investment decision. According to the authors, there is
confusion within management over the use of ROI.
However, as ROI is understood across disciplines, it is a
powerful metric to communicate across the
organization.
Unmuddling ROI:
The authors advise that if a manager is assessing the financial
return on an investment, then ROI is an
appropriate metric and can be calculated by dividing the

incremental profits by the investments. Agribusiness
marketing managers who are passionate about establishing the
credibility of the value created through marketing
should be thorough in their use of metrics. Most importantly,
they should be able to understand the metric, its use
and what it represents.

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