TITLE: USING AN EMOTIONAL
MODEL TO IMPROVE THE
MEASUREMENT OF ADVERTISING
EFFECTIVENESS
Author: Orlando Wood, Innovation Director
Company: BrainJuicer®
SYNOPSIS
This paper demonstrates the importance of measuring emotional
response to advertising. Drawing on new empirical data, it shows
how an emotional model of advertising and emotional
measurement, can lead to greater effectiveness, efficiency, better
planning and decision-making. Its findings could radically change
the way we think about advertising.
1
INTRODUCTION
Advances in neuroscience and psychology in recent years have
changed our view of how the human mind works. We now
understand that emotions guide and bias our decision-making1,
and indeed are essential for it. Without our emotions we make
poor decisions, and in fact, we struggle to make decisions at all2.
Emotions shape our behaviour and our responses to everyday
situations. And not only does it turn out that emotions are more
central to our decision-making than we had ever previously
acknowledged, but the role of our core-consciousness in decisionmaking is also now being called into question. Scientists at The
Bernstein Centre for Computational Neuroscience have shown that
a decision is formed in our sub-conscious up to ten seconds before
we believe we have consciously made that decision3. That
emotional engagement is strongly associated with long term
memory encoding has also been revealed by neuroscience4. In
short, we now know more than ever about the inner workings of
the brain, and the importance of emotion for memory and
decision-making.
Much has been written by our industry in the last decade on
emotion and its role in advertising. Theories of ‘High’ and ‘Low
Attention Processing’ have been advanced by Heath to explain the
role of emotion. Binet and Field (2007) have illustrated with the
analysis of IPA7 data that high attention processing – where the
viewer brings conscious thinking to bear on an ad – is “not always
necessary” and “not always sufficient” for success8. Heath (2009)
asserts that “we always form an attitude about a decision through
emotion and subconscious rational processing before we start to
consciously and actively ‘think’ about it” (my italics).9 He proposes
that TV advertising is not goal-driven but stimulus-driven, and it is
our feelings when watching TV ads that inform sub- and semiconscious thinking, which in turn leads to covert brand-linked
attitude change and ultimately a decision.
Yet, business has still to embrace emotion as a leading indicator of
future behaviour, let alone advertising effectiveness. Perhaps it’s
because emotion has always been difficult to measure, perhaps it
is because historically and culturally, emotion has always been
seen as the enemy of sound decision-making and something that
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is not to be trusted. The research industry, with a few notable
exceptions, is no different, and consciously or unconsciously
perpetuates this prejudice. It has for years structured its pretesting techniques around a top-down, highly rational, informationprocessing model, one of ‘high attention processing’, which rests
on the belief that the communication of a well-branded message
with the impact to gain our conscious attention is the route to
effectiveness. This framework for understanding how advertising
works, whose origins are now one hundred years old, has been
attributed to early face-to-face selling theories, which were used
as the starting point for the first models of print advertising, well
before the advent of television10. The influential theories of Daniel
Colley in the 1960s, which dictate that advertising takes people
from unawareness to awareness, from awareness to
comprehension, from comprehension to conviction, from
conviction to desire, and from desire to action, persist to this day.
It is convenient for the research industry to perpetuate this model
because it is a linear, sequential and seemingly logical approach,
around which it is very easy to structure pre-testing and tracking
research. Changes in awareness and attitudes are also easy to
measure, and provide comfort to marketing, a sense of
accountability even, when no change in hard business effects such
as share or profit gain is seen.
But the approach fundamentally misunderstands the way the mind
works. It is anachronistic and does not fit at all with a whole body
of recent learning in neuroscience, psychology and in-market
effectiveness case studies, which have shown the important
contribution of emotion to decision-making — learning which
points to the fragility of relying on respondents’ self-confessed
conviction or persuasion to predict behaviour. And so while the
prevailing research model seems to work much of the time, it is
not sensitive enough in many cases to assess effectiveness. It also
restricts the creative licence of advertising agencies to develop
engaging and therefore potentially extremely effective advertising
for their clients. To sideline the role of emotion to a means of
merely generating attention and awareness is to miss the role it
can actually play in advertising effectiveness.
In this paper, I outline an important experiment that links
established pre-testing measures and our emotional metric,
3
FaceTrace®, with in-market effectiveness. The paper will show
with new empirical evidence:
• How simple emotional response is more predictive of
business effectiveness than the widely used measures of
persuasion, brand linkage or even message delivery
• How emotional response is a better indicator of efficiency
than other pre-testing measures, and can therefore be used
as a highly effective media planning tool
• How emotion plays a more central role than that of a mere
envelope for message and that successful emotional
advertising is less likely to communicate a specific message
than less successful emotional advertising
The second part of the paper reveals how an ad might be
‘conceived emotionally’ in development to meet specific business
objectives (both hard and soft) in the marketplace. It will also
reveal the characteristics of fame advertising. Finally, I will
advance a theory to explain how and why emotional advertising
works.
HYPOTHESIS
In 2009 we resolved to conduct an experiment to establish the link
between our award-winning measure of emotion, FaceTrace®,
together with other common pre-testing metrics, and
effectiveness. Our hypothesis, when embarking on our experiment,
was that established pre-testing methods are imposing an artificial,
unhelpful and unnecessary maximum headroom on advertising,
making it difficult for highly emotional ads to get clearance. Ad
pre-testing does a reasonable job of ensuring very poor ads are
not progressed, but we can no doubt all think of at least one
example of a brilliant ad that has fallen foul of traditional pretesting, only to go on to be extremely successful in market thanks
to the gut-feel of a brave marketer. How can this ever happen if
our advertising models and research frameworks are sound? Could
it be that pre-testing research has a blind spot the industry isn’t
paying sufficient heed to?
We set up a research experiment to test the hypothesis that
traditional cognitive, evaluative measures such as persuasion and
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brand linkage actually discriminate against engaging and
potentially effective advertising. We wanted to see whether these
evaluative measures could in fact be working against effective ads
and investigate whether an emotional metric could improve the
measurement of advertising effectiveness. Traditional measures
might also be less likely to punish less effective advertising than an
emotional measure, but that was not a hypothesis we would test
as a part of our experiment.
Figure 1
Our emotional measure, FaceTrace®, uses pictures of human
faces in different states of emotion to measure emotional
response. It is based on the work of psychologist Paul Ekman, who
has established that there are 7 human emotions that we all
express in the same way regardless of our background or culture:
happiness, fear, disgust, anger, surprise, contempt and sadness.
He concludes that these seven emotions are the basic emotions
required for human existence.11 These emotions and the intensity
with which any emotion is felt (our intensity score also takes into
account those feeling nothing/neutral) are the key quantitative
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outputs from the measure. FaceTrace® uses faces because they
are a direct route to the way people are feeling, and minimise the
evaluative filters that are usually applied by respondents to market
research questions.12
Figure 2
The research experiment was undertaken in conjunction with the
IPA so that we could make use of their effectiveness data. The IPA
Effectiveness Awards are well-known as being the world’s most
rigorous effectiveness awards scheme. The competition has
allowed the IPA to build up a large database of confidential
information that can be used to help us understand how marketing
communication works. The analysis was conducted on behalf of
the IPA by Peter Field, an independent consultant, conversant with
the IPA effectiveness database. At no point was BrainJuicer party
to the effectiveness data for any individual ad.
A total of 18 historical TV ads were tested monadically with IPA
submissions from 2006 onwards from campaigns where TV had a
weight of at least 50%. The ads were from food, drink, household,
personal care and durables categories13. Each ad was tested
among 150 category users, or recent/intended purchasers in the
case of the durables ad. We resolved not to test any historical
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automotive or financial services ads given the state of the financial
services and automotive industry at the time, as the recession
might have influenced the way that people responded to
advertising for brands in these sectors in a way that they wouldn’t
have at the time of their first airing.
All the ads tested could be deemed examples of good advertising
by virtue of the fact that they were the subject of papers
submitted to the IPA for awards. However, only seven of the
eighteen ads tested actually won any kind of award (four won
silver and three won bronze awards) and it should be noted that
the intention of our research experiment was to establish how well
both traditional measures and an emotional measure could
separate good ads from excellent examples of advertising. If our
hypothesis were correct, traditional measures would be less able
to identify highly effective advertising than emotional measures.
The experiment would therefore enable us to comment more
generally, and open a wider debate, on best practices for
advertising pre-testing measurement.
The main effectiveness measure that we use in our experiment is
the average number of very large business effects reported in the
questionnaire that accompanies each IPA paper submission. These
business effects comprise market share gain, reduction of price
sensitivity, customer acquisition, profit and loyalty gain. The
number of these very large business effects has been shown by
Binet and Field in their Marketing in the Era of Accountability to be
strongly correlated with market share gain and indicative of higher
ROI. The number of very large business effects is also available for
every ad of interest, whereas ROI and market share gain data are
not. The database also holds spend data for many of these ads in
the form of Excess Share of Voice, which enables us to take spend
into account.14
BrainJuicer provided the survey data collected for the 18 ads to
the IPA across a number of pre-testing measures. The analysis
had to be at an aggregate level so that BrainJuicer could not
identify the effectiveness of any individual ad, as this is
confidential information. The ads tested were therefore ordered on
their scores for every individual measure of interest. For example,
to assess the ability of the ‘persuasion’ research measure to
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predict effectiveness, we would order the ads on how well they
had performed on ‘persuasion’, with the most persuasive ads at
the top of the list and the least persuasive at the bottom of the
list. An average effectiveness score would then be supplied by the
IPA for the best 9 ads and the worst 9 ads on ‘persuasion’, as
measured by the research. If the best nine ads on this measure
were on average more effective than the weakest 9 ads on this
measure, we could reasonably conclude that this measure was
indeed a sound predictor of business effectiveness.
In addition to business effectiveness data, the IPA database holds
information on the ability of the campaigns tested to deliver on
‘intermediate’ objectives, allowing us in a similar way to establish
which research measures will predict the ability of a campaign to
deliver on softer brand building objectives, such as increased
brand awareness, image, quality, trust, differentiation, fame and
direct response – the effects typically measured in tracking
research.
MAIN FINDINGS
The findings of our experiment are fascinating and represent a real
challenge to the established high-attention, information-processing
advertising model.
Our first analysis reveals that emotional response – simply the
intensity with which people feel any emotion after seeing an ad –
could indeed be a better predictor of effectiveness than commonly
used evaluative information-processing measures. It reveals that
persuasion and brand linkage measures are most likely to predict
mere moderate levels of effectiveness, and do indeed actively
discriminate against highly effective ads. This can be seen in the
very large business effects shown in Figure 3. Here we see that
the ads performing most strongly on Emotional Intensity achieve
2.3 very large business effects in market, whereas the ads that
perform most strongly on persuasion and brand linkage only
achieve 2.0 very large business effects, on average15. While the
differences are small, the overall pattern that we see across the
different sizes of business effect seems to suggest that our
hypothesis was indeed correct; that Persuasion and Brand Linkage
seem capable of predicting ‘large effects’, but that in some cases
8
they lack the sensitivity required to predict the ‘very large effects’
that have been shown to correlate so well with ROI and share
gain, and are not so capable of separating really great ads from
what are merely good ads.
Figure 3
New Emotional Measure
The first run of analysis revealed that some of the emotions were
more predictive of success than others. Happiness was the
strongest predictor of effectiveness and the more ‘negative’
emotions were predictive of failure (lower levels of effectiveness).
We developed a hypothesis that a measure which gave a weight to
each emotion (positive or negative) might be highly predictive of
business effects and we set about establishing a weight for each
emotion from our own survey data. The measure we devised
translates the emotional profile of an ad (the emotions felt by the
whole sample at the end of an ad) into a score (from 0-100).
Happiness is given a strong positive emotional weight, whereas
many of the other emotions are given a negative weight, including
neutrality. The intensity with which each of the emotions is felt is
9
also taken into account. We call the resulting number an Emotioninto-Action™ score.
We have catalogued all IPA winning ads we have tested and
combined these with the award-winning campaigns we tested as
part of this experiment. Ads which have won IPA awards and
which have therefore demonstrated their effectiveness in market
achieve a much higher Emotion-into-Action™ score on average
than other ads we have tested. Figure 4 shows the distribution of
scores on our norms database for Emotion-into-Action™, and how
effective IPA award-winning ads perform much more strongly on
average than other finished film ads we have tested.
Figure 4
Let’s turn now to examine the ability of the Emotion-into-Action™
score to predict effectiveness. This is shown in Figure 5, along
with a number of other measures thought to be of general interest
– percentage of the sample replaying the intended message
correctly, persuasion, an industry cut-through measure equivalent
and brand linkage16.
10
Figure 5
Figure 5 shows how predictive of effectiveness the Emotion-intoAction™ score is relative to other established measures. It strongly
outperforms persuasion, brand linkage and cut-through measures,
and even message delivery. In fact, far from predicting success,
these industry-standard measures actually mislead when it comes
to predicting the effectiveness of ads, failing to discriminate
between very strong ads and merely good ads, as suggested
earlier in the paper. This also supports Binet & Field’s own analysis
of the IPA dataMINE and their conclusion that “cases that reported
favourable pre-testing results actually did significantly worse than
those that did not”.17 Emotion-into-Action™, however, based
purely on the way people feel in relation to the ad, is extremely
predictive of business effects.
11
Case Study
A case study for a well-known drinks brand in the UK is a good
example of how emotional response can be a better predictor of
ROI than traditional evaluative measures. Figure 6 shows the
results of two subsequent campaigns for the same brand. The first
campaign tests strongly, with a high emotional intensity score and
a strong Emotion-into-Action™ score. The second campaign, which
was more message-focused, achieved a slightly lower emotional
intensity score and a lower Emotion-into-Action™ score. This
mirrors the final ROI figures calculated by Nielsen.
If the reader looks across to the more evaluative, informationbased measures, they will see how these mislead, favouring the
weaker of the two ads. If a brand manager were basing a decision
solely on these traditional measures, he or she would be
misinformed and might back the wrong ad.
Figure 6
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Predicting Profit Gain
In their analysis of the IPA Datamine, Binet & Field (2007) showed
how emotional campaigns can deliver more effectively than
persuasion or information-based campaigns on sales, share or
profit objectives18. In their analysis, Binet & Field’s definition of
‘emotional’ campaigns was dependent on the judgement of the
contributing authors, who determined whether they believed their
campaign to have been emotional, rational or a combination of the
two, but in our experiment we actually measured emotional
response, so would Binet & Field’s findings be borne out in our
results?
Our findings reveal that the presence of emotion (as measured by
FaceTrace® in research) is indeed indicative of share and profit
gain, and reductions in price sensitivity, and that established cutthrough measures actively discriminate against ads that deliver on
these business effects in market.
Taking profit growth as an example, Figure 7 shows how Emotioninto-Action™ is predictive of scale of profit growth, where a
traditional cut-through measure performs very poorly. Of the
eighteen ads tested, only five reported very large business effects
on profit growth in market. Four of these five ads are represented
in the best nine ads on Emotion-into-Action™, while only one of
these five highly profitable ads featured in the nine ads performing
most strongly on the established industry cut-through measure
equivalent. Higher scores on this established industry cut-through
measure equivalent were actually indicative of lower levels of
profit growth in market.
13
Figure 7
Efficiency
Let’s now turn to the ability of these measures to predict
efficiency. For a subset of our ads we are able to establish
efficiency by looking at spend in the form of Excess Share of Voice
[ESOV]. ESOV is defined as Share of Voice minus Share of Market,
and has been widely shown to be a strong driver of share
growth19.
When we examine ESOV we see that Emotion-into-Action™ is
predictive of efficiency, whereas other measures are inversely
predictive of efficiency–i.e. the higher the scores on these
measures, the less efficient the ads will be. This can be seen in the
analysis in Figure 8, which shows the ability of each measure to
explain efficiency (efficiency is calculated as Share of Market Gain
divided by Excess Share of Voice, adjusted using J. P. Jones’
published data to correct for brand size and take into account the
differing levels of ‘equilibrium SOV’ of brands of different sizes
relative to their market share).20 Positive and negative correlations
with efficiency are also shown for the purposes of clarity. The
14
result is in line with the business effects analysis already seen in
Figure 5.
Figure 8
In other words, the effective measurement of emotion in pretesting is central, not peripheral, to the measurement of efficiency.
Cognitive and evaluative measures are actually inversely predictive
of efficiency, including the established industry cut-through
measure, the original purpose of which was to help in media
planning.
15
Message and Emotion
One prevailing view of the role of emotion in advertising is that it
should simply serve as a velvet glove or envelope to carry a
message. One question we believed our experiment might be able
to answer is whether a high level of emotion coupled with a clearly
communicated message might be predictive of effectiveness.
We have already seen that the ability of an audience to take out
the key message of an ad is in fact a poor predictor of
effectiveness. Figure 9 plots message delivery and the Emotioninto-Action™ scores side by side for each ad on test. It also shows
which ads have won awards. The chart shows how it is perfectly
possible for an ad to enjoy a strong Emotion-into-Action™ score
with very low levels of message delivery, and yet still be effective
enough to win an IPA award.
Figure 9
It is therefore perfectly possible for emotional advertising to be
highly successful without the communication of a specific
message. In fact, the data suggests that to try to impart a specific
16
message in an emotional ad might actually be to reduce its
effectiveness – ads that perform well emotionally with no distinct
message perform better than emotional ads with more readily
identifiable messages. Analysis reveals that the most successful
emotional ads in our experiment were actually more likely to
impart multi-dimensional associations around the brand rather
than a clear and distinct, specific message.
Two excellent examples of emotional ads in out experiment that
performed strongly on Emotion-into-Action™ and poorly on
traditional message communication are Tropicana’s Breakfast in
New York and Burton Foods Cadbury’s Chocolate Digestives Thank
You ads. Both ads use emotion to serve the overarching brand
idea – whether it be the pleasure of an orange juice with a freshly
made breakfast or the joy of a chocolate biscuit – rather than
purely rational benefits of the product itself. For both ads
effectiveness data is available in the public domain, as both have
won IPA effectiveness awards and the papers are therefore
published21.
Tropicana’s Breakfast in New York ad achieved very low levels of
respondents identifying the intended message in our test, yet it
achieved a very high Emotion-into-Action™ score. This was also an
ad that achieved a poor ‘cut-through’ score in traditional tracking
(a Millward Brown Awareness Index of 3 vs. the UK norm of 6). It
was found to be ‘pleasant’ and ‘gentle’, responses that are not
deemed to generate ‘cut-through’ by the agency. It was also found
to have poor brand linkage.
The ad achieved 11% growth for the brand in market, reduced
price elasticity by 40% and achieved an ROI of £1.83 for every £1
spent. The ad achieved an Emotion-into-Action™ score of 82.7 (vs.
UK norm of 72.6) and an emotional intensity score of 1.79 (vs. UK
norm of 1.38) on FaceTrace®, putting it clearly in the strongest
10% of ads we have tested.
The Cadbury’s Chocolate Digestive Ad Thank You is a highly
emotional ad that dramatises ‘the joy women experience upon
discovering Cadbury Chocolate Digestives’. The feeling of
exaltation is emphasised by the gospel standard ‘Oh Happy Day’.
The ad achieved a high Emotion-into-Action™ score of 82.0 (vs.
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UK norm of 72.6), yet only a relatively small proportion of our
sample accurately replayed the message – that Cadbury’s
chocolate was now available on a digestive. The advertising ran in
Q2 of 2007, and value sales in this quarter were up 218% vs the
previous quarter and 126% vs the same quarter of the previous
year. For an investment of just under £1 million, the advertising
generated an additional £2.59 million in sales. There is also
evidence to suggest that the ad built loyalty and decreased price
sensitivity, improved perceptions of product quality and taste, and
unlocked sales growth across the entire Chocolate biscuits range.
To summarise:
1. Traditional pre-testing uses measures that appear to
discriminate against effective and efficient advertising. An
emotional measure is a very good predictor of effectiveness
and efficiency.
2. The measurement of emotion (with our Emotion-intoAction™ score) is therefore more likely to result in the airing
of ads that deliver very large business effects in market and
at relatively high levels of efficiency
3. Emotion can deliver business effects in and of itself if it
serves the overarching brand idea – it is a valid
communication strategy in its own right. A single-minded
focus on message delivery could limit the success of (even
highly emotional) advertising.
4. The shift towards emotional measurement in ad pre-testing
is more likely to reveal campaigns that will ultimately deliver
on share gain, profit gain and reductions in price sensitivity
than ads that perform well on traditional cognitive and
evaluative metrics.
HOW THE EXPERIMENT CAN HELP IN THE PLANNING AND
CREATIVE DEVELOPMENT PROCESS
The remainder of the paper looks at what we have learned from
the experiment about the way advertising can be conceived and
developed in emotional terms, and what we can learn about ‘fame
advertising’.
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The influence of emotional journeys on effectiveness
As part of our experiment, we wanted to establish what effect
emotional response during the course of an ad might have on its
effectiveness, in addition to the overall emotional impression left
at the end of the ad on the viewer that we have been examining
thus far.
This is made possible through BrainJuicer’s FaceTracingTM module,
which captures the emotion felt during the ad as it unfolds.
Respondents watch the ad for a second time and click on the ad at
any point that they feel anything. The ad is paused, they are
asked the emotion they feel, its intensity and why they felt that
way in one slick movement before the ad resumes playing. They
can stop the ad as many times as they like to tell us of any
changes in emotion, and can resume neutrality if the emotion
subsides at any point. We assume they are feeling neutral at the
start of the ad and that they continue to feel the same emotion
unless they tell us otherwise.
We identified three common patterns in the emotional paths of the
ads we had tested:
1. Evocation of negative emotions, followed by resolution of
negative emotions
2. Happiness/surprise building towards the end of the ad
3. Negative emotions building as the ad finishes
The three types of emotional journey are shown in Figures 10-12
19
Figure 10
Figure 11
20
Figure 12
A hypothesis we wanted to test was that the three emotional
journey types might have a bearing on effectiveness, as we had
long held the belief that ads which set up and resolve negative
emotions (set up and resolve tension) are more likely to be
effective than those that don’t.22
Analysis reveals that the three types of emotional journey do have
a strong bearing on effectiveness, with those that set up and
resolve a negative emotion highly indicative of success (see Figure
13). Those that ensure that happiness and surprise are building
towards the end of the ad were also successful in market. This is
the pattern we see for many ads – particularly those which seek to
convey a message. However, those where negative emotions were
found to build towards the end of the ad were the very least
successful, achieving an average of 1.75 business effects (this
sounds high, but is in fact a poor result as these are all reasonably
good ads by virtue of the fact that they have been submitted for
an award).
21
Figure 13
We have in the course of our testing seen numerous other
emotional journeys, and we will touch on one of these later in the
paper. The analysis underlines the importance of the emotional
and narrative journey within an ad – something that should be
considered early on in the creative development process and that
is given careful thought in the execution of the very best ads.
Types of Happiness
While other emotions move us to avoid behaviour or attempt to
move others to action, happiness is, we believe, the most useful of
the emotions to advertisers, because it propels us forward. It is
used to good effect with other emotions, as we have already seen,
but as the final emotion felt at the end of an ad, our evidence
suggests, it is the most useful. Happiness is a catch-all term for
what Ekman believes to be as many as twelve different types of
joy. These are listed in Figure 14, along with examples of their
triggers.
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Emotion
Amusement
Excitement
Trigger
Humour
Anticipation,
novelty
or
challenge
Pride in others’ achievement Acknowledging to self others’
(Pleased for others)
accomplishments
Pride in own accomplishment
Overcoming challenge
Wonderment (Awe-inspired)
Overwhelming incomprehension
at unlikelihood, novelty or rarity
Contentment
Relaxation
Ecstasy/bliss
Meditation, nature, joy from sex
Elevation (Uplifted)
Experiencing goodness in others
Gratitude
Appreciation of gift providing
benefit
Relief
Dissipation of another emotion
Schadenfreude
Delight in others’ suffering or
downfall
Sensory Pleasures
Sight/Sound/Smell/Taste/Touch
Figure 14
In our experiment, those respondents who felt happiness in
response to the ads tested were asked a follow-up question to
establish what type(s) of happiness they were experiencing. This
is asked as a straightforward multi-code text response question, as
there are no universally understood facial expressions to
distinguish between types of happiness. Asking this question
allows us to dig a little deeper into happiness and what types of
happiness generate very large business and intermediate effects.
Figure 15 illustrates the average level of business or intermediate
effects associated with each of these types of happiness. It shows
the ability of the best 9 ads, when ordered on each type of
happiness, to deliver business and intermediate effects relative to
the weakest 9 ads on each type of happiness. It also shows a
breakdown of the types of intermediate effect and how well the
best 9 ads on this measure delivered on those intermediate
objectives relative to the weakest 9 ads. Where a negative effect is
shown, this indicates that the weaker ads on each type of
happiness exhibited more very large effects on this objective than
23
the stronger ads on this type of happiness (i.e. if you don’t achieve
this type of happiness you are more likely to achieve this
objective).
24
Amused
Excited
Proud
Pleased
for
others
Aweinspired
Contented/
sense of
well-being
Ecstatic/
blissful
Uplifted
Sense of
gratitude
Relieved
Schadenfreude
Sensorially
pleased
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No.
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Awareness
Commitment
Quality
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Response
++ Strong positive effect
+ Mild positive effect
= No discernible effect
– Mild Negative effect
– – Strong Negative effect
Figure 15
25
What is striking is that not all types of happiness appear equally
helpful. Some types of happiness seem particularly helpful for
delivering on business objectives, particularly those involving
humour (amusement and Schadenfreude) but appear less helpful
for brand building, whereas others deliver better on intermediate
effects but less well on hard business effects, such as excitement,
gratitude, relief, sensorial pleasure and contentment. Pride doesn’t
seem to be particularly helpful for either business or intermediate
effects.
What we need to remember however, is that the intermediate
effects are measured in tracking research. Tracking research is
used by companies to establish intermediate effects such as
changes to brand awareness, image, quality and trust. The ability
of amusement, for instance, to deliver on business effects, without
registering positive effects on brand building objectives, raises a
difficult question for researchers about how effective tracking
research actually is. Our results suggest that it could be dangerous
to rely too heavily on brand image shifts seen in tracking as a
principal measure of advertising effectiveness, because amusing
ads, it seems, do not perform well in tracking research on
dimensions such as trust, quality and image, yet perform
extremely well in market. The failure here, if there is one, might
actually lie at the door of tracking research, and we might not, as
an industry, be giving ‘amusing’ ads the credit they are due. It
may well be that the research industry needs to re-examine the
way it conducts tracking research in the light of these findings,
giving greater emphasis to how people feel towards the brand, as
opposed to how they evaluate the brand.
Three of the types of happiness appear to deliver on both business
and
intermediate
effects.
These
are:
awe-inspired,
ecstasy/blissfulness and uplifted. These are not particularly easy
types of happiness to deliver on, but are particularly helpful, it
seems, and may also have a role to play in delivering fame, which
I will go on to consider later.
This analysis provides a route map for planners and creative
teams, showing how some types of happiness can meet certain
campaign objectives better than others. Consideration can now be
given by advertisers early on in the creative process to the types
26
of happiness that will deliver best on client campaign objectives.
This knowledge could even serve as an inspirational starting point
for creative teams working to a particular brief and set of
objectives.
How Music Contributes to Emotional Response
Music has long been thought to increase the emotional impact of
an ad, so what does this experiment tell us about the role of music
in emotional engagement and effectiveness?
An analysis of the ads on test reveals that music does indeed help
to deliver emotion, and therefore effectiveness, in advertising. Ten
of the ads tested amongst our set of eighteen used music. Of the
seven IPA award-winners, six employed music. Seven of the top
nine ads on Emotion-into-Action™ used music, and the average
Emotion-into-Action™ score for those with music is 74.3 compared
with 69.8 for those without music.
On average, ads with music have:
•
•
•
•
greater emotional intensity
higher levels of happiness
less neutrality
higher scores on ‘uplifted’, ‘contented’ and ‘sensorially
pleased’ types of happiness
A greater proportion of the ads with music delivered very large
effects on profit gain, and where we have share gain data, it
seems that a greater proportion delivered large increases in
market share too, and with less spend, pointing to greater
efficiency. The ads with music were also more likely to deliver on
intermediate effects – a greater proportion of them delivered on
awareness, differentiation, quality and image objectives.
Ads with music were also less likely to communicate a specific
message than those without (the average key message score of
those with music was 32% vs 39% among those without). Again,
we see how over-reliance on information-processing measures can
discriminate against a key contributor to effectiveness.
27
The ability of a piece of music to engage and to contribute to the
effectiveness of an ad is dramatised in Figure 16, a case study
where the same ad was tested with three different pieces of
music, taking an above-average ad on emotional response to the
strongest ad we have ever tested. With the strongest soundtrack,
sadness (which was present with the other tracks) all but
disappears, as does neutrality, and happiness increases at their
expense.
Figure 16
This case study also revealed that music can convey emotional
associations that are negative as well as positive. A music track
sets up complex inter-relationships between the visual, textual and
narrative elements of an ad in a number of ways, in addition to
exciting an emotional response in the listener. Harnessing and
aligning the emotional power of music in the service of an ad
requires considerable insight and skill, if the music track is to work
effectively.
All this suggests that music is as important in creative
development as the narrative and visuals it is meant to serve, and
28
emphasises how important it is to consider it early in the creative
development process.
Fame Ads
Campaigns that seek to deliver fame for a brand have been shown
by Binet & Field to be particularly strong when it comes to
delivering on business effects in market. They generate more
business effects than ads adhering to other influence models such
as persuasion, information or even pure emotional campaigns, and
are particularly effective when it comes to delivering on sales,
profit and penetration gains, as well as reductions in price
sensitivity.
So what is the definition of a fame ad and what type of emotional
response do they achieve? Binet & Field (2007) use a definition
that stipulates a fame ad ‘[gets] the brand talked about/[makes] it
famous’, and that in this way a fame ad will build perceptions of
the brand’s strength or authority in the category23. Fame ads have
taken on a new relevance in the age of the internet, because it is
now so easy to share advertising with other people at the click of a
button.
Fame ads have certain hallmarks. First, we have found them to
evoke enormously intense emotional responses. We have observed
an exponential relationship between the intensity with which
people feel any given emotion and their likelihood to talk about or
share the ad with others, as measured by YouTube hits. Fame ads
evoke particularly high levels of happiness, and we have found
that they are more likely than average to trigger types of
happiness such as ecstasy/blissfulness, awe and uplifting feelings;
they also amuse, but tend to deliver a more complex emotional
response than merely humour alone.
Figure 17 shows the emotional profile of two ads that have
generated millions of YouTube hits for their respective brands:
Cadbury’s Dairy Milk Gorilla and Heineken’s Walk-in Fridge, and
could be said without doubt to have delivered fame.
29
Figure 17
Their emotional profile is incredibly similar, both enjoying very
similar levels of surprise, happiness and overall intensity, well
ahead of our UK norm.
They are both also more likely than average to deliver certain
types of happiness. Figure 18 shows their scores relative to the
average levels of happiness seen for the IPA ads on test in our
experiment.
30
Figure 18
That they should score particularly well on ecstasy, awe and
elevation tallies very well with our earlier analysis on types of
happiness, which showed these types of happiness to be
particularly effective when it comes to delivering on fame
objectives. They also evoked ‘contentment’ and ‘pleasure for
others’ less than the IPA ads on test. These types of happiness
were less predictive of delivery on fame objectives in our earlier
analysis.
There is another important parallel between the two ads – the
emotional journey they take the viewer on. Both ads produce a
double peak in happiness/surprise and successfully evoke and
dispel negative emotions during the course of the ad [Figures 1920].
31
Figure 19
Figure 20
32
Neither ad feels in any way formulaic or seems even vaguely
similar to the other upon viewing, yet both follow the same
pattern. Both warm the viewer up with the first emotional peak
and then surprise and deliver an overwhelming second peak in
happiness later on, resolving any negative emotions that might
have built up in the intervening time since the first peak. It is
particularly impressive that the Heineken ad achieves this (and
such an impressive overall emotional intensity score) given that it
is only a 30 second ad, and goes to show that it is possible within
a short spot to achieve these high levels of emotion and this type
of emotional journey.
Neither ad feels like an advert. They both perform exceptionally
well on difference and memorability measures, but perform poorly
on rational evaluative measures such as ‘makes me want to buy
product’, relevance and persuasion. Both might therefore struggle
to make it through traditional ad pre-testing.
Both ads reward re-viewing with incredible amounts of visual and
aural detail, and neither relies on language to evoke emotion. In
fact, the Heineken ad achieved these impressive scores in the UK
even though it was tested in the Dutch original. The facial
expressions evident in the nostrils of the Gorilla in the Cadbury’s
ad and the (some might argue) feminine and exaggerated
expressions of delight in the men in the Heineken ad reward the
viewer even after multiple viewings, and new details become
apparent every time. And if the viewer wants to watch it again it is
surely a good sign that he or she will want to share the ad with
others and witness similar delight in their responses. They also
allow for development of the brand idea (or brand feeling) through
other spots and channels.
Both are enjoyable pieces of theatre whose emotional energy
nevertheless serves a powerful overarching brand idea. They are
not so much films advertising a product (you could argue that they
could have been made for any brand within the category) as films
sponsored by the brand to celebrate the brand idea—as ads
designed to engage. They seek to own the emotion that is more
widely associated with the category, simultaneously imbuing it in
the on-screen characters and evoking it in the viewer. The level of
branding within each ad, it could be argued, is weak by traditional
33
explicit standards; it is subtle and implicit, and relies on the clever
use of colour. The ads develop authority for the brand precisely
because the branding is subtle and there is no clear product
message; they are bold enough in a sense to speak for the whole
category, and let the product speak for itself.24 In short, the
emotion their creativity evokes is what is designed to be linked
with the brand, not a message. Highly creative ads, such as these,
that are conceived emotionally and which generate high levels of
emotional response, stand both to generate fame for the brand
and to deliver business effectiveness.25
It is also this precisely sort of ad that performs particularly well on
the internet. The internet demands a different advertising model
from one that is structured to deliver a message, persuade or
promote a brand explicitly – a different model from the ‘top-down’
information-processing model. Sites such as YouTube that enable
us to view ads on our own terms usually reveal the brand within
the spot’s title, re-enforcing branding and further reducing the
need for explicit branding within the ad itself (i.e. people actually
search for the ad by the brand name). Ads that succeed in driving
word of mouth on the internet do so because they engage the
viewer emotionally, not because they persuade through messaging
and have strong internal explicit branding.
In summary:
1. The emotional endpoint of an ad is important, but how you
get there also has a real bearing on effectiveness. Set-up
and release of negative emotions (emotional tension) is
highly effective. End on a high and do not leave your
audience with mounting negative emotions.
2. Certain types of happiness are better able to meet certain
campaign objectives than others. This can be helpful in the
creative and planning process.
3. Music is an important contributor to effectiveness and needs
to be considered early on in the creative development
process.
4. Fame ads can be recognised in pre-testing because they
enjoy a particular type of emotional profile:
a. They exhibit high levels of emotional intensity, high
levels of happiness and some surprise.
34
b. They uplift, inspire awe and evoke a particular type of
blissfulness/ecstasy that is very rarely seen in most
advertising. They often use humour, but don’t rely on
it entirely.
c. They carry the viewer on an unusual emotional journey
– a double peak in surprise and happiness being one
proven route.
d. They achieve high scores on memorability and
difference, but achieve low scores on rational
measures such as relevance, persuasion and ‘makes
me want to buy’, and therefore don’t feel like an ad –
more like sponsored entertainment.
e. They serve a single-minded overarching brand idea or
(more accurately) feeling, and repeatedly reward with
highly evocative human detail. They do not rely on
language to generate emotion.
DISCUSSION
In his Nobel prize lecture, Daniel Kahnemann (2002) outlines the
mental processes we use to make decisions. He proposes that two
cognitive processes exist, which he refers to as Systems 1 and 2.
System 1, he explains, is a perceptual and intuitive system,
generating impressions that are not voluntary or verbally explicit.
This system is fast to react, automatic, associative, effortless and
slow to adapt and learn. System 2 is involved in all judgements
whether they originate in impressions or deliberate reasoning; it is
slow, effortful, rule-governed and flexible enough to assimilate and
process new information. Judging the depth or volume of a pack
of cards might be a System 1 process (it’s intuitive), but
calculating the surface area of all the cards might be a System 2
process (it requires a deliberate and conscious mental process). All
explicit judgements are endorsed at least passively by System 2,
he states, but this monitoring is quite lax, allowing many intuitive
judgements to express themselves – whether they are right or
wrong. Kahnemann suggests that “people are not accustomed to
thinking hard, and are often content to trust a plausible judgement
that quickly comes to mind”.
35
We are, then, very susceptible to our intuitive and less explicit
internal responses. That we don’t apply conscious reasoning to the
entire field of possible options in decision-making has also been
advanced by Damasio (1994):
“a pre-selection is carried out for you, sometimes covertly,
sometimes not. A biological mechanism makes the preselection,
examines candidates, and allows only a few [options] to present
themselves for the final exam”.26
This sits well with Heath’s assertion (2009) that our feelings inform
both sub- and semi-conscious thinking, covert brand-linked
attitude change and ultimately our decisions.
Kahnemann also refers us to the work of Loewenstein (1996), who
asserts:
“[…] the ‘hot states’ of high emotional and motivational arousal
greatly increase the accessibility of thoughts that relate to the
immediate emotion and current needs, and reduce the accessibility
of other thoughts.”27
In other words, our emotional response can override our ability to
access other thoughts or rational counter-arguments.
Kahnemann also asserts that “the performance of more effortful
tasks will collapse under cognitive load”, so our ability to process
information using System 2 is compromised by the quantity of
information we receive.
These theories, supported by scientific experiments, explain how
our emotional response to an ad and the way we feel at that
moment can later influence decision-making. Emotions belong to
the System 1 mechanism, to borrow Kahnemann’s terminology,
which is only poorly monitored and controlled by an overseeing
and reasoning System 2. Ads do not need to ‘persuade’ via System
2 to be effective.
Information-processing models of advertising operate on a System
2 platform, and traditional pre-testing models assume that System
2 is the only system that exists. When an ad comes along that
36
operates on System 1, that uses emotion to build associations and
impressions, it makes the viewer feel good and reduces the
effectiveness of any rational resistance to the brand offered by
System 2 (remember Loewenstein: “‘hot states’ of high emotional
and motivational arousal greatly increase the accessibility of
thoughts that relate to the immediate emotion and current needs,
and reduce the accessibility of other thoughts”). Respondents can
tell us how they feel (if we ask the question well) but they are not
necessarily in a position yet to know how this will affect their
future behaviour, and in all likelihood System 1 will get the better
of System 2 in the end. The ad therefore flies under the traditional
pre-testing radar, which, with all its evaluative measures, is still
scanning the skies for System 2 responses.
These theories help us to understand the results of our IPA
advertising experiment. We have seen that the ability of an ad to
communicate a message is not predictive of its effectiveness in
market, which raises serious questions over this model of
advertising. This is because it is an effort to process message after
message as we sit and watch television, and we don’t focus our
full conscious attention on all that we see (remember Kahnemann:
“the performance of more effortful tasks will collapse under
cognitive load”).28 Emotion can serve as a focusing mechanism, of
course, making us pay attention to the ad, and alert us to a
message, but this is not how emotional advertising works when it
works at its best. Ads that are principally emotional and convey no
real product message, such as the Tropicana or the Cadbury Dairy
Milk Gorilla ad, work on System 1. They work on a different level,
and do not require our conscious attention (although fame ads
tend to get that too). They do not need to persuade through
reason. They do not need to ‘cut-through’ because these ads
quietly bypass our System 2 filtering mechanisms as we watch,
and are picked up instead by our emotions as part of System 1.
This is why they are so efficient; they require fewer opportunities
to be seen. This is also how they are able to reduce price
sensitivity, because they influence our attitudes towards a brand
by making us feel closer to it, lowering out System 2 defences. In
traditional language, they speak to our ‘heart’ rather than our
‘mind’. So we might believe that they are not persuasive ads, not
relevant; we may think that we are no more likely to buy the
product as a result of seeing the ad, but somewhere within us a
37
knot is likely to have unravelled and we move a step closer to
accepting and buying the brand.
CONCLUSIONS
This paper has sought to reveal the importance of emotional
measurement for advertising pre-testing. It has shown how an
emotional model of advertising measurement, and indeed of
advertising, underpinned by the latest advances in neuroscience
and psychology, could work. This new model of advertising will
stand us in better stead, not only for TV advertising, but for a new
digital age, where the ability of an ad to go viral (‘word of mouse’)
will become increasingly important.
The paper has shown, when it comes to the very strongest ads,
how traditional evaluative and cognitive measures of advertising
have their limitations, and how they are unable to distinguish
extremely strong advertising from merely good advertising—how
they only measure part of the story. More than that, it has shown
how these traditional evaluative measures can actually mislead,
how reliance on them can restrict effectiveness, efficiency and
creativity, and how they are unlikely to predict highly viral fame
ads. It has also raised questions over the effectiveness of
traditional tracking research, which is an area we believe warrants
further investigation by the industry.
The paper has shown how emotional measurement, on the other
hand, is able to discriminate between good and extremely strong
advertising. We have seen:
• The predictive ability of emotional response in advertising for
effectiveness and efficiency
• How emotional advertising is more effective than messagebased advertising, and a focus on communicating a message
might actually inhibit success
• How an ad might be conceived emotionally to deliver on
specific business objectives, through the use of happiness,
emotional journeys and music
• How to identify a fame advert through emotional pre-testing
measures
38
Our results have shown that emotional response is indicative of
success at the highest levels of effectiveness, when it comes to the
best ads the creative industry has to offer, but it is our firm
contention
that
emotional
measurement
is
extremely
discriminating across the whole spectrum of advertising, and
indeed we have seen its predictive ability among ads that have not
been submitted for IPA awards in other experiments we have
conducted29.
At a broader level, this paper has revealed how it is possible to
achieve success by means of a purely emotional communication
strategy. It has shown how spend might be better allocated to
emotional advertising with the greater efficiency it commands. It
has armed advertisers and agencies with an emotional map to
effectiveness and shown them a means to measure and optimize
their work. The paper has also sought to explain why emotional
advertising works; why it is that emotional advertising might be
better placed to reduce price sensitivity, be more efficient, more
profitable. In short, it has shown how measuring emotional
response should be central and not peripheral to pre-testing.
Leading commentators have recently shown that creative
advertising is efficient advertising.30 A new emotional framework
for advertising measurement will give the creative industry the
permission it needs to deliver more effective and more efficient
advertising, enabling agencies to put emotion at the heart of their
planning, to use emotional response, rather than message receipt
or persuasion, to drive integrated communications. Effective
emotional measurement will ensure that we don’t unfairly punish
strong emotional ads and will lead to better ROI. It will tell us
where to focus our investment and better predict profit growth for
our brands. Our hope is that this paper will give the industry a
new way of thinking about how advertising works, how it might be
conceived and how it should be measured.
39
Acknowledgements
I would like to thank Janet Hull and the IPA for their agreement to
access the IPA DataMINE and use the IPA effectiveness data in
this experiment.
I would like to thank Peter Field for his patient analysis, good
humour and for our many lively discussions.
I also wish to thank Les Binet (DDB Matrix), Mark Earls (Herd
Consulting) and Michael Spencer (Sound Strategies) for the
excellent and thought-provoking discussions we have had
regarding the results of this experiment.
Note from the Author
This paper contains a number of updates and revisions since the
first publication of this experiment in a paper written for the MRS
Research Conference in 2010.
Author
Orlando Wood is Innovation Director at BrainJuicer®. His work in
the field of emotion and communication testing secured for
BrainJuicer the ESOMAR 2007 Best Methodology Award and the
ISBA Advertising Effectiveness Award of the same year. He is a
frequent conference speaker having spoken at ESOMAR, MRS,
AMSRS, WARC, ThinkBox and EphMRA events.
40
References
Biel, A. “Do You Really Want to Know?”, ARF Advertising and Copy Research Workshop
(1995)
Binet, L & Field, P Marketing in the Era of Accountability, WARC (2007)
Bonney, D. “Sad-vertising”, Admap Magazine, Issue 478 (2006)
Damasio, A. R. Descartes' Error, London: Vintage Books (2006)
Ekman, P. Emotions Revealed, Understanding Faces and Feelings, Pheonix, London (2003)
Field, P. “The Creation of Buzz and Fame”, Admap Magazine, Issue 493, pp.14-16 (2008)
Haynes, J and Rees, G Decoding mental states from brain activity in humans, Nature Reviews
Neuroscience 7(7):523-34 (2006).
Haley, R. & Baldinger, A. “ARF Copy Research Validity Project”, Journal of Advertising
Research, December/January (2000)
Heath, R. “How the best ads work”, AdMap Magazine, Issue 427 (2002).
Heath, R. & P. Feldwick, “50 Years of Using the Wrong Model of TV Advertising”, MRS Golden
Jubilee Conference (2007)
Heath, R. “Emotional engagement: how TV builds brands at low attention”, AdMap Magazine,
Issue 507 (2009).
Heath, R. “Creativity in TV ads does not increase attention”, AdMap Magazine, Issue 512
(2010)
IPA, Advertising Works 15, WARC (2007)
IPA, Advertising Works 17, WARC (2009)
Kahnemann. D. “Maps of Bounded Rationality: A Perspective on Intuitive Judgement and
Choice”, Nobel Prize Lecture (2002)
Morris, J.D., C. Woo, J.A. Geason, & J. Kim, “The Power of Affect: Predicting Intention”,
Journal of Advertising Research, 42 (2002): 7-17
Penn, D. “Neuroscience can add insight in complementing classical research”, AdMap
Magazine, Issue 512 (2010)
Poels, K. & S. Dewitte, “How to capture the heart? Reviewing 20 years of emotion
measurement in advertising”, Department of Marketing and Organisation Studies (MO),
Katholieke Universiteit Leuven (2006).
Soon, CS, Brass, M, Heinze, HJ, and Haynes, JD, Unconscious determinants of free decisions
in the human brain, Nature Neuroscience (2008)
Young, C. “The use of negative emotions in advertising”, Admap Magazine, Issue 476 (2006)
Wood, O. “Using Faces; Measuring Emotional Engagement for Early Stage Creative”, ESOMAR
(2007)
41
Notes
1
Kahnemann in his Nobel Prize Lecture (2002) states “an automatic affective valuation – the
emotional core of an attitude – is the main determinant of many judgments and
Behaviors” and Damasio asserts in his book Descartes’ Error (1994), p185, that “While the
hidden machinery underneath has been activated, our consciousness will never know it.
Moreover, triggering of activity from neurotransmitter nuclei, […] one part of the emotional
response, can bias cognitive processes in a covert manner and thus influence the reasoning
and decision-making mode.”
2
Damasio recounts the case of a patient with ventromedial prefrontal brain damage who
had, to all intents and purposes, lost his ability to feel emotion. This displayed itself in a
number of ways. On one icy day he was able to drive calmly and dispassionately past
skidding cars involved in accidents all around him, and then on another day he was evidently
completely unable to decide between two similarly acceptable alternative dates for his next
appointment. His attempts to weigh up the pros and cons of the two dates through pure
reason alone meant that he was almost completely unable to make a decision, until he was
steered in the direction of one of the dates by the hospital staff. Damasio describes how this
patient’s behaviour was not influenced by his emotions in the usual way. Descartes’ Error
(1994), pp. 193-194
3
For an account of the method, please read Haynes, J and Rees, G Decoding mental states
from brain activity in humans. Nature Reviews Neuroscience (Volume 7):523-34 (2006). For
details of the experiment itself, please read Soon, CS, Brass, M, Heinze, HJ, and Haynes, JD
Unconscious determinants of free decisions in the human brain. Nature Neuroscience
(Volume 11): 543-545 (2008).
4
Thinkbox (UK) have conducted neuroscientific experiments using fMRI and SST technology
that reveal that moments of intense emotional response and engagement are highly
correlated with moments of long-term memory encoding (2010). They show how visual
attention or mental effort on the other hand is not correlated with long-term memory
encoding. This work has been presented publicly at a Thinkbox event but will not be formally
published until late 2010. For further details of the experiment please contact Thinkbox:
http://www.thinkbox.tv
7
Institute of Practitioners in Advertising
8
See Binet, L & Field, P Marketing in the Era of Accountability, WARC (2007), pp. 89-91
9
See Heath, R. “Emotional engagement: how TV builds brands at low attention”, AdMap
Magazine, Issue 507 (2009).
10
See Heath, R. & P. Feldwick, “50 Years of Using the Wrong Model of TV Advertising”, MRS
Golden Jubilee Conference (2007), ‘From AIDA we get the idea that selling is a sequence,
moving a prospect from ignorance to action. Both these formulas were of practical usefulness
in the context in which they were developed, but later were applied in situations where they
made little sense, by people who had little idea of their origins.’
11
See Ekman, P. Emotions Revealed, Understanding Faces and Feelings, Pheonix, London
(2003)
12
BrainJuicer’s FaceTrace® technique won ESOMAR’s Best Methodology in 2007 and also the
ISBA Advertising Effectiveness Award in 2007. For a full account of its development and a
review of other emotional research metrics please see Wood, O. “Using Faces; Measuring
Emotional Engagement for Early Stage Creative”, ESOMAR (2007)
13
Ads were tested for the following brands:
Actimel
Aqua Optima
Ariel
Bendicks
Bertolli Olivio
Cadbury Digestives
Carex
Cathedral City
Fairy Liquid
42
Heinz Beans
Horlicks
Irn Bru
Lynx
Magners
Petits Filou
Ryvita Minis
Tropicana Pure Premium
Wall's Sausages
14
For a fuller explanation of the contents of the IPA dataMINE, how the data is collected and
Very Large Business Effects please refer to Binet, L & Field, P Marketing in the Era of
Accountability, WARC (2007), pp. 11-18.
15
Persuasion is a mean score derived from a 7-point positive to negative scale in response to
the question ‘Please indicate how persuasive you found this ad?’. Brand linkage is a 5 point
scale that is widely used in the industry from ‘It could have been an ad for almost anything’
to ‘You couldn't help but remember the ad was for this brand’. This question was analysed
among those who had not seen the ad before, as vastly different results for the same ad
were deemed to be possible on this question amongst those who had and hadn’t seen the ad
before, once an ad is established in market.
16
The intended message is known from the IPA paper submissions. The industry cut-through
measure equivalent is a weighted composite measure composed of brand linkage, a brand
enjoyment scale and a passive/active measure derived from a number of single word
emotional attributes. It seeks to emulate an important and recognised cut-through measure
used in ad pre-testing.
17
See Binet, L & Field, P Marketing in the Era of Accountability, WARC (2007), p 99. They go
on to say that their analysis ‘casts considerable doubt on the ability of such research to
reliably pick the winners’.
18
Binet, L & Field, P Marketing in the Era of Accountability, WARC (2007), pp.55-66.
19
Binet & Field cite two separate studies in addition to their own that show this to be the
case. Marketing in the Era of Accountability, WARC (2007), p46.
20
It should be noted that where before in Figure 5 we were looking at the average number of
business effects, here we are looking specifically at the relationship between share gain and
excess share of voice. Please also note that spend and share gain data is only available for
ten of the eighteen ads we tested.
21
See Advertising Works 15, WARC (2007) for Tropicana and Advertising Works 17, WARC
(2009) for Burton Foods Cadbury Digestive effectiveness case study.
22
David Bonney, in his paper ‘Sad-vertising’ (2006), underlines the effectiveness of sadness
when it is resolved by more pleasurable emotions such as happiness, and that “sad-vertising
probably works best when the brand offers a positive, cathartic finale after a glut of sadder
emotions”.
23
Binet, L & Field, P Marketing in the Era of Accountability, WARC (2007), pp.55.
24
Thinkbox’s 2010 neuroscience study has revealed that long-term memory encoding
increases and decreases in line with emotional episodes and that branding that is embedded
within an emotional episode is therefore likely to be more effective than deliberate explicit
branding that follows immediately after an emotional episode.
25
For an excellent analysis of devices that can be used to generate fame, see Field, P. “The
Creation of Buzz and Fame”, Admap Magazine, Issue 493, pp.14-16 (2008)
26
See Descartes’ Error (1994), p189
27
Kahnemann refers us to Loewenstein, G ‘Out of Control; Visceral influences on behaviour’.
Organizational Behaviour and Human Decision Processes, 65, p272-292 (1996)
28
How many times have we sat in front of a weather forecast, watching intently, only to
come to the end of the bulletin and have no idea what has been said?
29
See Wood, O. “Using Faces; Measuring Emotional Engagement for Early Stage Creative”,
ESOMAR (2007) for details of another experiment conducted by BrainJuicer across several
categories, that showed the ability of our emotional measure to separate ads that had won
43
IPA awards (and therefore demonstrated business effectiveness), from ads in the same
category that had not won or even been entered for the IPA awards, and which achieved
lower levels of emotional response.
30
In analysis conducted on behalf of Thinkbox and the IPA (2010), Peter Field shows how
advertising that wins creative awards is more likely to be effective than advertising that does
not win creative awards. He shows that advertising that wins creative awards has an 11:1
efficiency advantage over advertising that does not win creative awards. He reveals that this
is because creatively-awarded ads are more likely to employ an emotional communication
model than non creatively-awarded ads. This work was presented at a Thinkbox event in
June 2010 and will be published in late 2010 by the IPA. For further details please contact the
IPA: http://www.ipa.co.uk
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