The Branding Power Of Advertising Online: Theory

The Branding Power Of Advertising Online: Theory & Evidence
A White Paper By TNS Interactive Solutions

© Taylor Nelson Sofres
Interactive Solutions Worldwide

TNS Interactive Solutions – White Paper

The Branding Power Of Advertising Online : Theory And Evidence1
How Recent Advertising Theory Helps Us Understand Results From The New
Brand Effectiveness Research Methods

By Gabriel Hughes PhD

1. Introduction

This paper concerns the new measures of online advertising effectiveness, and
seeks to explain what they are, how they came about and how they can be
understood in the light of recent advertising theory. It is argued that the new
measures are mainly the result of commercial pressures, specifically, pressures
affecting internet ad agencies and ad networks who have used these methods to
try to fill in the gaps in the accountability of online advertising which have been
left by ad server metrics. This has meant that issues of interpretation and
advertising theory have tended to become secondary to issues of methodology,
technology and practical measurement. Yet without a stronger basis for
interpretation, we can learn little about the power of online advertising, and
media planners and buyers will continue to undervalue online advertising
(Saunders C. 2001).

The new measures referred to are specifically those created by the online
advertising effectiveness research tools promoted by Taylor Nelson Sofres
Interactive Solutions (AdEval™), Real Media, 24/7, Yahoo, DoubleClick and
others. The paper does not focus heavily on descriptions of these tools, as they
are in fact largely similar, and have been thoroughly explained in previous papers
(e.g. Hughes & Hummerston 2001). These research tools share the approach of
linking online pop-up surveys to ad creative delivery on the internet, and have
emerged in the past two years as a compliment to ad server metrics such as the
click through and the post impression visit. The idea has been to show that there
are measurable branding effects created by online advertising which cannot be
seen using the conventional click stream (server side) data points.

1
The author is grateful for the co-operation of Real Media UK and Ask Jeeves who have generously
obtained agreement for us to use results from research carried out for them and their clients by Taylor
Nelson Sofres Interactive Solutions.

2


It is argued that these new measures are potentially very powerful tools, with
desirable features not found in the evaluation methods used for advertising in
other media. On the other hand, if we agree with the recently espoused theories
of Robert Heath (Heath 2001), the results these methods produce appear entirely
consistent with the way people process advertising across all media. That is, they
learn branding from advertising even while they largely ignore the actual creative
executions.

2. How The New Measures Came About

The new measures of online advertising effectiveness are perhaps best explained
by their origins within the new media and advertising industries. It is argued that
the inadequacy of existing online ad ‘metrics’ based on server reports, and lower
than predicted growth on online ad spending, have driven the search for new
alternatives based on more conventional research methods.

During the 1990s internet boom, it was often remarked that the web was the
most accountable all the advertising media. This was because, in the nature of
internet technology, it has always been possible to record exactly how many ads
are delivered, and how many ads are clicked on, or otherwise interacted with.
These measurements are derived from algorithms working through massive
server log files and are now widely recognised as the core metrics of online
advertising : in particular the ad impression (one ad served as a user visits a site)
and the click though rate (the total number of times an ad is successfully clicked
on as a percentage of the total ad impressions). For a while, these hard
measurements seemed to be all that web site publishers would need to draw in
precious advertising revenue.

Online advertising is one of the few proven sources of revenue online and so as
the new media has grown, so too have the agencies which handle online
advertising. It was never practical for websites to hard code each and every new
online ad, so ad networks and ad serving software was developed to link
advertisers to websites. Online ad delivery and measurement were combined by
major new media companies such as Real Media, 24/7, Engage and DoubleClick.
Ad impressions and click through metrics became part of the standard package
for all online advertising, as well as integrated into the methods of pricing online

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ads, so that before long no serious website could sell online advertising without
them.

However it has not taken long for the metrics to become less popular with the
very same companies that were responsible for their widespread adoption in the
first place. In particular the click through has been a source of much debate, as
its use presupposes that online campaigns are oriented towards driving traffic to
sites, and thus to immediate direct response. Indeed direct marketing is still
considered by many as the raison d’etre of online advertising, to the neglect of all
the conventionally understood benefits of advertising that are so widely accepted
for offline media2.

The inadequacy of the ad server metrics is a contributing factor to the wider
problem of slower than expected growth in the online ad market. The online
publishers who sell online ad space have been continually frustrated by the
apparent failure of online advertising as measured by their own server reports.
Average click through rates have always been low and have fallen dramatically
year on year as the internet itself has grown. This fact has contributed to the
uncertainty of advertisers, who often find the new media surprisingly difficult to
understand, and will not spend significant money online until they feel the new
medium is a safer and more widely accepted way to advertise. While this
reluctance of advertisers has been the constraint on demand for online
advertising, the supply of online advertising opportunities has grown enormously
through an explosion in both internet use and the number of live internet pages
supplied by a multitude of competing online publishers. Prices for advertising
online have been forced way down as they would be in any buyers market, and as
they have fallen (almost certainly at a faster rate than ad serving costs), it has
become imperative for the sellers to prove that the right online campaign can
deliver a return on investment comparable to offline advertising.

Technical innovations by the ad serving networks can be characterised as one
attempt to address this challenge by extending the range and power of ad server
metrics. Most significantly, several ad networks have now incorporated the ‘post

2
More recently, to try and counter this perception, several online publishers have refused to even
report click through rates to emphasise just how unrepresentative they are. Yet publishers are up
against ad agencies wielding client budgets, and on this side of the equation the demand for bargain
performance related advertising deals continues to keep the click through in usage.

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impression visit’ metric into their reporting. This records the number of ad
impressions which are followed by a visit to certain pages on the advertisers site.
This automatically includes all successful click throughs, but also includes people
who visited the site after an ad exposure, not by clicking, but by just typing in the
URL or perhaps searching for the advertiser the next day. Reports showed that
more visitors to the advertisers site came from users who were pre exposed to
the ad but did not click, than from people who instantly clicked on the ads (Briggs
R. 2002). This new metric seemed to fulfil the requirement for a measure which
went beyond the immediate direct marketing effects and was therefore more
oriented towards the branding power of online.

Yet the post impression visit has still not been enough to satisfy the demands of
advertisers or the requirements of sites to show what online can do. It does seem
to capture some degree of branding effect, yet it misses out so much of what is
conventionally understood by this : what is happening in the users mind.
Critically, it tells us nothing about the users awareness of the brand and their
recall of the ad (the relationship between these two measures is explored in detail
later in this paper). Neither can it evaluate the impact on perception of the brand,
or in more practical terms, tell us anything about users thoughts or offline
behaviour in relation to the ad campaign.

So it is that attention has increasingly turned towards research based methods of
evaluation, showing us that in advertising evaluation, as elsewhere, the internet
has come to resemble the old economy more closely than had been expected a
few years previously.

However research has also had to adapt to the new medium. Conventional
awareness tracking cannot address the online measurement problem. The
internet as a whole is still not used by a large chunk of the population, and
penetration of individual internet sites is very low compared to audiences for TV
programmes. Furthermore, within the sub group of those who visit the site, only
a proportion will be served the ad at any one time of day. Thus, using
conventional awareness tracking, identification of those exposed to the ads is
frustrated by both the limited size of sample populations, and the high potential
for misidentification of those exposed vs. those not exposed.

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The new online advertising effectiveness measures use internet technology to
overcome these obstacles in order to deliver a sample for conventional
quantitative analysis. The problem of obtaining sample is overcome by randomly
sampling people as they use the internet, through a pop-up invite served for
every 1 in ‘n’ site visits after a delay period from users leaving the site carrying
the advertising. The problem of identifying users has having been exposed to the
ad is solved by means of a cookie. This cookie code is served with the ad, and is
used solely to establish whether a subsequently sampled person has been
exposed to the ad3. When a selected user later agrees to complete an online
survey questionnaire, an additional variable is included in the analysis : actual
exposure to the ads.

This method thus leads to the collection of two samples, one group of users who
have been exposed (test) and one who have not (control). These groups are
sampled in the same way from the same site user population during the same
days and same times of day, and so the two groups are comparable to the test
and control samples used in a classical natural science experiment. This approach
provides a base level for comparison of conventional survey response measures
of advertising effectiveness.

3. The Meaning Of The New Measures Of Online Ad Effectiveness

The method described above can be used to ask any range of questions possible
with a self completing quantitative survey. In practice when applied to
advertising, questions concern brand awareness, both unprompted and prompted,
and brand perception. These are conventional measures of branding, but when
applied to online advertising using the test / control methods they represent new
measures of advertising effectiveness. For the purposes of this paper we focus
mainly on differences in brand awareness between exposed vs. non exposed
samples of users.

The new measures perhaps make most sense when interpreted as an extension of
the previous server side metrics. Internet users can be seen to respond to online
ads at four measurable levels, each an extension of the first :

3
The method described is that used by the Taylor Nelson Sofres product AdEval™ and also by the
Real Media ‘Ad Insight’ package. No data, including cookie data, is collected without user consent; also
no personal data is collected, and data is used solely for research. Other products vary in the exact
method, but share the intention to evaluate the branding effects using online surveys.

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(i) users click on the ad to visit the advertiser’s site;
(ii) users visit the site after seeing the ad;
(iii) more users are aware of the advertised brand (a new measure);
(iv) perceptions of the brand are different for users who have seen the
ad (another new measure).

Just as the first measure is encompassed by the second, so does the third extend
measurement further, with awareness of the brand implicit in most cases where
the user consciously decides to visit the advertiser’s web site (there are some
exceptions e.g. a non branded teaser). Unpacking brand awareness and building
further, we can also measure how this is perceptually composed in terms of user
reactions to brand image statements (iv).

The new measures do not sit as well with existing offline methods for measuring
advertising effectiveness. Generally in research for offline advertising brand
awareness is recorded at a different time from the consumer’s exposure to the
ad, and is recorded in a different way. This means that the various sources of
awareness in the media mix cannot be easily disentangled in any common
analytical framework.

Yet the new measures are far from inadequate compared to what is done for
evaluating, say, TV and press advertising. In fact the method has important
benefits over CAPI / CATI omnibus style tracking studies. It tests awareness in
the users normal internet usage environment, as if the equivalent TV viewer could
be sampled in their own home as they watched TV. Instead of inferring exposure
to the ads from exposure to the media, the new measures directly record ad
exposure as it happens (many TV viewers have a habit of changing channels or
making coffee during ad breaks, which confounds analysis by market
researchers). Most significantly, the new measures do not depend on claimed
recall of the ad itself, but rather use internet technology to automatically
distinguish between those who have been exposed to the ad and those who have
not. From a research perspective they offer a chance to evaluate advertising in
ways not previously possible.

The first reactions to the results of using the measures have been a mixture of
exhilaration and relief from within the internet ad industry. Results have in
several cases shown strong differences in brand awareness between those

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exposed and those not exposed (see section 5). Yet there is also widespread
recognition that because we are dealing with a new methodology in a new media,
it is still not possible to say whether a given result is an especially ‘good’ result,
and what results really tell us about internet advertising.

Thus, although the new measures are potentially strong tools, we lack a
background of case studies to compare results against, and, perhaps more
importantly, we lack a strong theory of branding online. Neither of these
problems can be addressed easily or within the scope of a single paper such as
this. However the remainder of this paper explores how a recently revived theory
of advertising and branding might apply to the internet, and how this theory
could even be uniquely testable using the new measures. While we cannot draw
very firm conclusions, some interesting observations do emerge from some of the
online research carried out so far.

4. ‘Low Involvement Processing’, Online Ads, And The New Measures

Here we consider a potential link between the new measurement techniques and
recent developments in advertising theory as promoted by Robert Heath in his
book ‘The Hidden Power of Advertising’ (Heath 2001). Heath’s book has attracted
considerable attention, as it appears to revert to an older model of advertising
which claims that advertising can work subliminally and contains insights which,
although not wholly new, have not been so comprehensively expressed until now
(McDonald 2002).

Heath points out that conventional approaches which ask about advertising recall
miss a significant point about advertising : that it makes use of consumers’
reliance on ‘Low Involvement Processing’ to make purchase decisions. This is a
form of information processing which lies somewhere roughly between
subconscious processing (say, walking) and fully conscious rational processing
(say, evaluating a business proposal). The link with the new measures is that
they reflect actual exposure and not potentially flawed claimed exposure to ads
based on low levels of involvement with the ads.

Drawing on work in the field of psychology, Heath explores the subject of memory
and learning. Of particular interest is ‘implicit memory’, which is where memory
can be shown to reveal itself without conscious recollection. Experimental findings

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in psychology have shown that implicit memory can be more enduring than
explicit memory, so that acquiring information implicitly can be more effective
than consciously learning it (Heath 2001 : 51). For Heath, implicit memory is
crucial for explaining how advertising works, since it is evidence of how brand
learning can occur without necessarily any attention being paid or any conscious
recollection of the actual advertising.

Implicit memory can work despite the existence of another psychological
phenomena, ‘perceptual filtering’, which is where non-salient or useless
information is not consciously processed in order that the human mind is not
overloaded. Crucially, Heath shows that, ‘perceptual filtering is powerless to
prevent implicit learning taking place’, so that branding can occur even when the
creative execution of an ad is largely ignored (Heath 2001 : 75).

One analogy that Heath uses is driving a car, where we pay a low level of
attention which allows us to do and think other things at the same time, while still
absorbed in driving and able to move to a higher level of involvement if required.
Other types of low involvement mental activity are even more automatic than
this, and Heath uses the phrase ‘low involvement processing’ to cover a wide
range of ways that people learn and behave without paying full conscious
attention.

Heath brings the various threads of psychological theory together in his ‘Low
Involvement Processing Model Of Advertising’. Summarising this model, Heath
asserts that most advertising is processed at a low level with people often paying
little attention to it, while still implicitly remembering the brand associations. This
drives intuitive brand choice, which is in reality a far more common method of
choosing brands than rational consumer choice (Heath 2001 : 76-79).

Are Heath’s theories applicable to advertising on the internet ? The changing role
of different media is a subject he addresses in the book. Although most of the
case study information he uses relates to TV advertising, he firmly believes the
benefits of TV are over rated by retailers in particular, and that other media will
benefit as advertisers realise that it is an inefficient medium (Heath 2001 : 117).
On the subject of internet advertising, he is ambivalent, believing on the one
hand that online ad formats are limited, but on the other that the potential exists

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to draw users in to learning about brands through newer and more subtle
methods of advertising (Heath 2001 : 118).

One possible objection to the use of the Low Involvement Processing model for
online ads is that internet is a much more involving medium than others. The
user is often actively seeking information using the internet, and is perhaps
operating at a higher level engagement than the average TV viewer. On the other
hand, what is true of internet content is probably not true of the associated
advertising content, which users may largely ignore and yet process at lower
levels of involvement as they do for other advertising. This is perhaps an issue
best resolved by the psychologists who Heath draws so heavily upon, but shows
that integrating an understanding new interactive media into old media theories
still requires more theoretical and experimental work.

Assuming for now that Heath’s model is relevant to online, consider how it relates
to the new measures of online ad effectiveness. In his model, it is quite possible
(but not necessary) for a consumer to fail to explicitly recall particular advertising
creatives, and yet still respond to the branding effects of the overall campaign.
Thus, the obvious link between Heath’s approach and the new measurement
techniques is that for internet ads we can now establish whether users have been
exposed to online ads without explicitly asking them, and therefore without
requiring their active recall of the ads for our analysis.

With the users consent, this ad exposure information is tied into brand recall. In
this way brand recall can be understood as a function of actual exposure rather
than mere claimed exposure. This allows the researcher to sidestep Heath’s
objection to the use of claimed ad recall in market research, and consider
whether brand awareness can occur even without the consumer explicitly
recalling the ad creative.

There now follows results from four separate research projects conducted by
Taylor Nelson Sofres using test / control online ad related survey methods i.e.
new measures. The idea was to re-analyse this data to try and establish if there
are indeed discrepancies between ad recall and brand awareness, and to look for
any related observations that might shed light on these new approaches to
advertising evaluation.

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5. Results And Observations

Here we present brand and ad awareness measures for different samples from
four studies of online campaigns – see table 1. None of the brands involved can
be revealed for reasons of client confidentiality, however they do include (in no
particular order) a make of car, a personal finance brand, a corporate recruitment
awareness campaign and a major travel company.

All projects related to online ad campaigns with branding objectives which each
ran for no more than a few weeks at different times throughout 2000-2001. All
the sites involved in these campaigns were included in each project. All the
projects were able to distinguish users who had been exposed to ads as against
users who had not. The great advantage of this test vs. control method is that
offline effects on brand awareness, such as advertising in other media, can be
expected to affect both groups to an equal extent. The only thing that
distinguishes samples is whether or not they have seen the ad(s), which allows
the campaign effect to be isolated. All of these projects were collected during the
same sample days and times of day, with the exception of the third reported
project which used a ‘pre’ and ‘post’ sampling method (as the campaign was a
site sponsorship and was thus delivered to all site users). In the case of this third
project, the disadvantages of this approach were mitigated by the fact that no
offline campaign ran during the overall sampling period.

The various items reported here need to be explained in more detail, as follows -

Brand Awareness – Top of Mind
This is unprompted brand awareness, the first question in the survey, asking
users to name three brands which come to mind in the broadly defined product
category. No clue has already been given as to what brand is being tested. The
percentages show the proportion of users who named the brand being tested.

Brand Awareness – Prompted
This is where the user is given a list of around ten brand names in the product
category, including the brand being tested randomly positioned within the list.
They are asked to tick a box for all those brands they are aware of. Again, no clue
has already been given as to what brand is being tested. The percentages show
the proportion of users who indicate that they are aware of the brand.

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General Internet Ad Recall
Later in the survey the user is asked if they recall seeing any internet ads for the
brand being tested – and so the brand is finally named and revealed to the user.
This could include ads served for previous non tested campaigns. The percentage
shows those who respond that they have seen internet ads for the brand. If they
indicate that they have seen ads, this may relate to earlier ads from previous
campaigns.

Specific Ad Recall
The user is then shown the ads that are actually being tested, and about which it
is automatically known whether they have been exposed to them or not. They are
asked if they recall seeing these specific ads, and the percentage shows all those
who say that they do. For every project the specific ads had been newly created
for the campaign being tested, and had not been shown before.

Non Exposed / Exposed
This heading shows which sample is which in terms of automatically recorded
exposure to the ads being tested. The ‘non-exposed’ gives a baseline against
which the exposed group can be compared. The ‘exposed’ are users who will have
seen the ad in a previous session, or if in the current session, who are surveyed
at least 3 minutes after leaving the site carrying the advertising.

Top line results from the four projects cover a total of 2,044 internet users -
Table 1.

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Online Brand Awareness & Ad Recall In Four Separate Projects (2000 – 2001)4

Non Exposed Exposed All samples (size)

Project 1
Brand Awareness - Top of Mind 3% 7%
Brand Awareness – Prompted 30% 44%
General Internet Ad Recall 12% 24%
Specific Ad Recall 17% 42%

Sample size 295 104 399

Project 2
Brand Awareness – Prompted 81% 79%


Project 3


Project 4


Considering the analysis presented from the four projects, we can make several
interesting observations, as follows.

4
These results remain anonymous for reasons of client confidentiality. Analysis is by Taylor Nelson
Sofres Interactive Solutions. Once again the author is grateful for to both Real Media and Ask Jeeves
for agreeing to use these results.

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• Online Ads Boost Brand Awareness
In all projects there was a greater level of either unprompted top of mind brand
awareness or prompted brand awareness for those who have been exposed to the
ads. Only prompted awareness in project 2 is not higher for those exposed to the
ads. Some random variation can be expected between the non exposed / exposed
samples, but in the case of both project 1 and project 3, the increase in
awareness (prompted and unprompted respectively) was found to be statistically
significant using a Chi-Squared test5.

It is precisely this kind of result which has been seized upon so enthusiastically by
the internet advertising industry. Hence in July 2001, a joint press release from
the US Internet Advertising Bureau, DoubleClick, MSN and CNET declared that,
‘online advertising can be used effectively for branding’ and that multiple research
projects, ‘overwhelmingly reinforce the effectiveness of online advertising’ (during
the same month 24/7 Europe and Coca Cola reported an independent study
touting similar results). Their findings echoed earlier research by other industry
leaders such as Real Media, and also reported by notable industry observers (e.g.
Russell M.J. et al 2001, also see Briggs R. 2002). That internet advertising can be
used for raising brand awareness should not now be in doubt; although this fact
alone is not enough to lift the continued economic uncertainty that currently
hangs over the online ad industry.

• Raising Brand Awareness From Initially High Levels Appears Harder
This is a result common with offline research, and says that the proportion of
people who can be brand aware has a limit, and that the marginal difficulty of
raising awareness increases as we approach this limit. This is probably the factor
explaining why brand awareness appears more static for projects 2 and 4 than for
projects 1 and 3. The remaining 20-30% of users for the former two projects are
most probably people who will quite stubbornly resist any attempt to inform them
about the brand. For brands which can be expected to have a higher level of
awareness to begin with, questions which relate brand perceptions to the
advertising may be a more relevant way to compare exposed and non-exposed
groups – this is also done using these new methods, although is not examined in
this paper.

5
This is the appropriate non-parametric test comparing categories of aware / non aware vs. exposed /
non exposed. For both projects 1 and 3 the hypothesis that there was no relationship between
exposure and awareness is rejected at the 95% level of significance.

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• People Claim to Recall Ads Even When They Haven’t Seen Them
All those who are served an ad are known to been exposed at the time they are
asked about ad recall, as were those who have not been served an ad. It is
possible for some users to see the ad on one machine, and then get surveyed on
another – so there is some possibility of error here6. However this alone is not
enough to explain why, on average across the three projects which measured
this, 22% of users who have not been recorded as exposed to ads should
nonetheless claim to recall unique and specific online ads when shown them.

In fact, when the research is used to ask about recall of ads in other media, a
similar finding occurs – users claim to have seen ads in the cinema or heard them
on radio, when no ads had ever been run in these media. This phenomena is not
uncommon in offline advertising research, where false recalls of ads have been
found to make up a significant proportion of advertising awareness (see
Sutherland M. & Friedman L. 2000, and Moran 1990). What seems to be
happening is that people are learning about brands through advertising, but once
aware of the brand, do not know how they learnt it. Once aware, recognition of
the brand becomes confused with recognition of an ad, and users get an ‘I know
this’ reaction to branded ads which they have never seen but which echo motifs,
images and themes present in previous advertising campaigns even in different
media.

• Changes In Ad Recall Are Not Indicative Of Shifts In Brand Awareness
Specifically the difference between general ad recall for non exposed vs. exposed
users gives no guide to the difference in brand awareness. This can be seen by
comparison of project 1 and project 2, where we see that the levels and
difference in general internet ad recall are quite similar for both samples in both
projects; but also that the difference in prompted brand awareness is not at all
similar. Project 1 shows a 48% difference in prompted awareness, a significant
result, whereas project 2 shows no discernable difference except for an
apparently slightly lower level of awareness in the exposed group - an
observation than can only be explained by random deviation between the
awareness levels of samples of the site user population.

6
The author has been asked whether this possibility undermines the positive brand awareness
findings which appear to be demonstrated by these new measures. In fact it does not : in so far as
there is any ‘cross contamination’ between the test and control groups, this could only be expected to
reduce the size of the difference in brand awareness, as the two samples become more similar, and
thus leads only to an underestimation of the branding effect.

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Differences in recall of the specific ad do not present any clearer indication of the
brand awareness difference. Again for project 2, a difference of 61% in the recall
of the specific ads used in the campaign seems to have no bearing at all on the
brand awareness levels.

Thus, the act of recalling an ad and recalling the advertised brand seem almost
unrelated. Almost, that is, but not quite -

• Awareness Levels Are Highest In Those Exposed To And Recalling The Ads.
This is apparent when the data is analysed in a slightly different way, as follows –

Table 2.
Awareness Results Grouped by Ad Recall and Exposure
Users exposed to Users exposed to Baseline – users Sample sizes
ads, and recalling ads but not recalling not exposed to (bracketed figures
ads them ads refer to sub groups)

Project 1 prompted 64% 30% 30% 399 total
awareness (44, 60, 295)

Project 2 prompted 83% 74% 81% 662 total
awareness (178, 160, 324)

Project 3 unprompted 23% 2% 7% 287 total
awareness (82, 86, 119)

Project 4 unprompted 70% 30% 32% 609 total
awareness (101, 301, 207)

This is perhaps not a surprising finding – comparing results in the first column
just shows us that if someone can recall an ad they are more likely to recall the
advertised brand – but this does remind us that ads themselves are (naturally)
associated with brand awareness, even though we cannot use ad recall to predict
a branding effect.

What is also interesting is that the least aware group are those who have actually
been exposed to the ads, yet do not recall them (the second column). Most
probably this group perceives the ad and the brand as completely irrelevant.
Quite possibly they are not even in the target market, and so do not pick up on
the semiotics of the ad and brand. Alternatively, these people may simply be the
forgetting type, unable to recall what they had for breakfast, never mind what
online ads they have seen and what brands they are aware of.

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The slightly higher levels of awareness among the non exposed base samples
echo the finding that a significant minority who are not shown the ads
nonetheless seem to specifically recall them when shown them in the survey. Ad
recall and brand awareness are clearly associated, but for reasons more to do
with the vagueness of the human mind operating at low levels of processing, than
to any easily measured cause and effect relationship.

7. Concluding Remarks

Advertising on the internet really can work for branding, and there is no reason
for thinking the way this occurs should be any different from the way it occurs in
other media. The results are consistent with the idea that users gain brand
awareness without realising it. As Robert Heath puts it,
‘If, as low involvement processing suggests, we can ‘learn’ motivating information
about brands implicitly, i.e. without knowing that we have learnt it, we are going
to be incapable of recalling much, if anything, about the learning process’.
(Heath 2001 : 104).

Its clear from the evidence that users do not know where they get their brand
awareness from – their memories deceive them, especially when they are not
paying much attention. We know that users pay a particular kind of close
attention when they use the internet, but nonetheless they most probably ‘filter’
apparently superfluous content such as the associated advertising. This may
mean that implicit learning is occurring, and using the new measures we can
show that brand awareness can certainly be affected by the ads themselves, if
not by ad recall.

There is clearly no justification for online advertisers seeking to increase ad recall
for its own sake, as an increase in ad recall does not necessarily imply an
increase in brand awareness – the more desirable objective. This is exactly as the
Low Involvement Processing Model predicts. However, it is also clear that ad
recall is nonetheless a desirable secondary objective, at least for the internet, as
the most brand aware users seem to be those who have both seen an ad and
remember seeing it.

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Online publishers can take great comfort in what interactive research can show
them. The ad server metrics that have been used so extensively in recent years
are indeed limited in what they tell advertisers. Research is strengthening the
case for brand oriented advertising online as a realistic and cost effective
proposition. Although relying on older methods of quantitative analysis, the new
measures of online branding effectiveness also utilise the medium in ways that
cannot be done for offline advertising research. In doing so, they are revealing
that the new medium works very much like the old in driving intuitive brand
choice.

Gabriel Hughes PhD – May 2002
Global Product Development Manager
Interactive Solutions, Taylor Nelson Sofres Plc

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Bibliography

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