the branding power of advertising online: theory

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Gabriel Hughes PhD – May 2002, public profile at http://uk.linkedin.com/in/gabrielhughes (I hope I do not violate copyright)

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Page 1: 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

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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.

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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 - Top of Mind 2% 4%

Brand Awareness – Prompted 81% 79%

General Internet Ad Recall 16% 25%

Specific Ad Recall 33% 53%

Sample size 324 338 662

Project 3

Brand Awareness - Top of Mind 6% 10%

General Internet Ad Recall 22% 37%

Sample size 150 224 374

Project 4

Brand Awareness - Top of Mind 68% 70%

General Internet Ad Recall 70% 67%

Specific Ad Recall 11% 25%

Sample size 207 402 609

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 ads, and recalling ads

Users exposed to ads but not recalling them

Baseline – users not exposed to ads

Sample sizes (bracketed figures refer to sub groups)

Project 1 prompted awareness

64%

30%

30%

399 total

(44, 60, 295)

Project 2 prompted awareness

83% 74% 81% 662 total (178, 160, 324)

Project 3 unprompted awareness

23% 2% 7% 287 total (82, 86, 119)

Project 4 unprompted awareness

70% 30% 32% 609 total (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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