The Branding Power Of Advertising Online: Theory


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Gabriel Hughes PhD – May 2002, public profile at (I hope I do not violate copyright)

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The Branding Power Of Advertising Online: Theory

  1. 1. The Branding Power Of Advertising Online: Theory & Evidence A White Paper By TNS Interactive Solutions © Taylor Nelson Sofres Interactive Solutions Worldwide
  2. 2. 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
  3. 3. TNS Interactive Solutions – White Paper 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 3
  4. 4. TNS Interactive Solutions – White Paper 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. 4
  5. 5. TNS Interactive Solutions – White Paper 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. 5
  6. 6. TNS Interactive Solutions – White Paper 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. 6
  7. 7. TNS Interactive Solutions – White Paper (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 7
  8. 8. TNS Interactive Solutions – White Paper 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 8
  9. 9. TNS Interactive Solutions – White Paper 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 9
  10. 10. TNS Interactive Solutions – White Paper 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. 10
  11. 11. TNS Interactive Solutions – White Paper 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. 11
  12. 12. TNS Interactive Solutions – White Paper 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. 12
  13. 13. TNS Interactive Solutions – White Paper 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. 13
  14. 14. TNS Interactive Solutions – White Paper • 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. 14
  15. 15. TNS Interactive Solutions – White Paper • 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. 15
  16. 16. TNS Interactive Solutions – White Paper 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. 16
  17. 17. TNS Interactive Solutions – White Paper 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. 17
  18. 18. TNS Interactive Solutions – White Paper 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 18
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