ad and brand perception

Evaluating advertising effects on

brand perceptions: incorporating

prior knowledge

Jenni Romaniuk and Emma NichollsEhrenberg-Bass Institute, University of South Australia

Received (in revised form): 18 November 2005

One of the key objectives of advertising is to influence the perceptions customers

hold about a brand in their memory. Therefore, when assessing the effectiveness

of an advertising campaign, researchers often look at changes in responses to

brand-attribute linkage questions. Drawing on two cases in the fast-food and

financial services markets, we show how using known patterns in perceptual data

to create expected values can more clearly isolate the effect of advertising on

brand perceptions. This technique removes the overall shifts in brand usage or the

relevance of the attribute to the category, which when trying to isolate the effects

of advertising a specific message are essentially ‘noise’. Removal of this ‘noise’

reduces the number of changes that need attention and highlights advertising-

related changes.

Introduction

Assessing the effect of advertising expenditure is an activity undertakenand debated by both marketers and researchers. One approach has soughtto link advertising directly with sales. This has produced mixed results,and even those studies showing advertising as being effective have rarelyprovided any indication as to why. An alternative approach seeks todetermine the effect of advertising on intermediate variables such as brandperceptions, attitudes, awareness or equity. One such intermediate variableis the link between the brand name and desired attributes in buyermemory. These brand-attribute linkages have long been acknowledged asimportant aspects of brand equity/knowledge (Keller 1993, 2003).

One strategy of advertising campaigns is to focus on a central theme(e.g. ‘we offer excellent service’), with the objective of developing specific

International Journal of Market Research Vol. 48 Issue 2

© 2006 The Market Research Society 179

perceptions about a brand. Advertising effectiveness is then assessedthrough examining the specific link between the brand and the perceptionof ‘excellent service’. The aim is to ascertain whether advertising effortshave made a difference in establishing, reinforcing or shifting these percep-tions. Brand perceptions are important because they are said to influenceconsideration and evaluation, and therefore purchase (Nedungadi 1990;Keller 2003). They are of particular interest from an advertising evaluationperspective because short-term sales can be affected by various non-advertising factors (such as, for example, price promotions) and also thevast number of people exposed to the advertising may not have an oppor-tunity to purchase from the category. Analysing effects on intermediatevariables can overcome these limitations. Further, these consumer mindsetmeasures, of which brand perceptions are one, are considered animportant aspect of brand equity due to their diagnostic ability (Ailawadi2003).

Identifying the effect of advertising on brand perceptions has mostlyfocused on examining direct changes in the proportion of respondents whomention the brand, for specific brand attributes (for example, 46% ofpeople interviewed thought Brand X was ‘good value for money’ inquarter 2 while 52% thought Brand X was ‘good value for money’ inquarter 3).

However, there are factors, aside from advertising activity, that caninfluence a respondent’s propensity to give a response linking a brand toan attribute. Two of these are (1) if the respondent uses the brand and(2) the degree to which the attribute is considered to contribute to categorymembership, or its ‘prototypicality’ (Bird et al. 1970; Romaniuk & Sharp2000). In this research, an approach controlling for these two influences iscompared to the more typical comparison of percentage changes. Thepurpose is to determine which approach provides greater insight as to thereal effect that the advertising has had on marketplace brand perceptions.

Background

The measurement of advertising effectiveness has been evolving over manyyears. Competition for marketing budgets from more easily measuredactivities, such as price promotions and direct marketing campaigns, hasraised issues of accountability for the money spent on advertising, as wellas assessing return on investment (Feldwick 1996). Thus, the expectationsof what advertising can achieve, and therefore the most appropriateadvertising objectives and how to measure them, have also been widely

Evaluating advertising effects on brand perceptions

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debated (e.g. Jones 1990; Ehrenberg et al. 2000). Despite this, the toolsused for advertising evaluation seem to have remained consistent. Thesemeasures include unprompted and prompted advertising awareness, recall,recognition and understanding of the advertising content or message(McDonald 2000). Essentially, these measures also look at the short-termeffects of advertising efforts, with a focus on customer memory structuresrather than directly on sales.

Marketing activities are undertaken with the goal of changing orreinforcing the consumer ‘mindset’ in some way. This includes thoughts,feelings, experiences, images, perceptions, beliefs and attitudes towards abrand. Keller and Lehmann (2003) describe five dimensions as beingimportant measures of the consumer mindset:

1. Brand Awareness (recall, recognition)2. Brand Associations (strength, favourability, uniqueness of perceived

benefits and attributes)3. Brand Attitude (perceived quality of, and satisfaction with, the brand)4. Attachment (or Loyalty), and5. Activity (how much consumers talk about, use, seek out information,

promotions, etc. regarding the brand).

The value of a brand, and the effectiveness of marketing activitiesundertaken to affect the consumer mindset about a brand, is thereforeoften measured by evaluating changes in perceptual responses onadvertised attributes.

Brand perceptions are attributes in consumer memory that are linked tothe brand name (Keller 1993). They have been the subject of research formany decades, particularly since the seminal article by Gardner and Levy(1955), which articulated that the brand was more than just the sum of thefunctional qualities it offered. Considered to be a key aspect of brandequity (Aaker 1992, 1996), developing, changing or reinforcing brandperceptions has long been considered an outcome of effective advertising,in that these perceptions and associations can influence the response tosubsequent marketing activity (Keller 2003).

Past research has identified two key influences on a person’s propensityto associate a particular brand with a particular attribute. The first is usageof the brand, which impacts the likelihood of a brand to be associated with(almost) any attribute. Customers are about three times more likely tomention a brand they use than a brand they don’t use (Bird et al. 1970).Thus, brands that have more users systematically gain more responses


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than brands that have fewer users. The slight exception to this pattern isfor highly descriptive attributes, which describe functional aspects of thebrand. Here, non-users are also highly likely to mention a brand; however,brand users will still have a higher propensity (Barwise & Ehrenberg 1985;Hoek et al. 2000). Changes in a brand’s usage levels – for example, due tosampling changes, a change in shelf space or distribution – will thus leadto complementary changes in brand response levels, regardless of anyadvertising activity. There needs to be control for this during advertisingeffectiveness analysis, to ensure that the impact of advertising is isolatedand correctly attributed.

The second influence is the degree to which the attribute defines thecategory, or its prototypicality (Nedungadi & Hutchinson 1985; Wardet al. 1992). The more often an attribute is mentioned across all brands,the more prototypical it is considered to be (Rosch & Mervis 1975;Romaniuk & Sharp 2000). For example, the attribute of ‘quick service’would be more prototypical in the fast-food market than, say, ‘healthy’.Empirically, all brands would gain more responses for ‘quick service’ thanthey would for ‘healthy’. It would be expected that prototypicality levelswould change over time, as particular attributes become ‘standard’ in anindustry. For example, ‘has low carbohydrates’ in a food market wouldhave gained only a few responses for any food brand three years ago. Now,however, the attribute would gain more responses across all brands asconsumers have become more aware of this feature within the foodmarket, and marketers focus on this attribute in their communications andpackaging. Likewise, prototypicality levels can decrease as attributesbecome less relevant. For example, in the banking industry, it would beexpected that the prototypicality levels of ‘having convenient branches’would have declined as other non-branch methods of doing banking haveincreased.

Romaniuk and Sharp (2000) demonstrated a technique whereby anexpected response level for each brand on each attribute can beestablished, by drawing on these two patterns of usage and prototypicality,and utilising a chi-squared-type calculation. This expected value can thenbe used to identify deviations, which can highlight each brand’s strengthsor weaknesses, relative to competitors. Given that the purpose ofadvertising is to create these strengths, or reduce these weaknesses, wesuggest that this technique can be used to identify the impact ofadvertising, by controlling for changes that are due to variations in usageor prototypicality levels.


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Research method

This study is an evaluation of brand image studies from two differentmarkets: fast food and financial services. In both studies, brandperceptions were measured using a free response attribute to brandassociation method, which is commonly used in market research (Brown1985). A number of brand names were read out to respondents, who weretold to record the brands. Then a randomised set of attributes wasprovided and respondents were asked which of the brands, if any, theyassociated with each of the attributes. The result is then the percentage ofthe sample that associated each brand with each attribute, with rawpercentage changes determined by comparing the figures over time. Anexample of this is shown in Table 1.

An alternative approach to determining the change in perceptualresponses over time is to calculate an ‘expected’ value. The approach weuse in this paper is based on the method demonstrated in Romaniuk andSharp (2000), whereby all of the brand and attribute data are recorded ina contingency table. The row and column totals are then used in thefollowing manner to establish expected values:

Expected value (B1, A1) = Row total (A1) *Column total (B1)

Table total

This provides a figure for each brand, on every attribute, that can becompared with the observed figures to produce the ‘deviation fromexpected’ value. The differences in these deviations can then be comparedover time. So, rather than 56% followed by 50% of respondentsassociating a brand with an attribute (a change of –6% over time), thedeviations might be +13 and +10, which would be a difference of3 percentage points over time.

This study analyses the two approaches, comparing changes in both rawpercentages and deviations from expected figures for the samebrand/attribute combination – for example, Brand X for ‘good value’ raw


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Table 1 Results for Brand X: raw percentage and deviations from expected changes over

time

Raw percentage changes Deviations from expected changes

Attribute Wave 1 Wave 2 Change (W2 – W1) Wave 1 Wave 2 Change (W2 – W1)

Good value 56 50 –6 +13 +10 –3pp

Trustworthy 45 50 +5 +6 +7 +1pp

percentage change (–6 percentage points) would be compared withdeviation from expected change (–3 percentage points). Based on the priordiscussion our hypotheses are:

H1: Controlling for usage and prototypicality patterns willresult in fewer changes over time being evident.

H2: The changes that are apparent when usage andprototypicality are taken into account will better reflect themessages of the advertising in the market.

Results

Study 1: fast-food market

In the case of the fast-food study, two waves of cross-sectional researchwere conducted (during August 2001 and February 2002). A telephonesurvey was conducted each time with a random sample of over 600respondents. For the brand perception measurement questions,respondents were asked to record a given set of five brands in the market.A randomised set of 20 attributes was read out and respondents wereasked which (if any) of the five brands they associated with each attribute.The brands included in the list were a mix of companies offering ‘dairytreats’ (ice cream, milkshakes, etc.). The included attributes thus featuredproduct-related – ‘sells ice cream cakes’ and ‘has a wide range of ice creamflavours’ – as well as general attributes such as ‘offers value for money’and ‘has something for all the family’. The attribute list came from pastqualitative research, with input from the marketing department of thecompany sponsoring the research.

Changes over timeThere were a total of 16 statistically significant changes in raw percentagesover time at the p < 0.05 level. The majority of changes were decreases (14compared with 2 increases). There were fewer changes evident in thedeviations analysis (only 7; 6 decreases, 1 increase). For example, the rawpercentage change in responses on the attribute ‘something for all thefamily’ for Brand 5 was –6 percentage points, compared with the changein deviations from expected values, which was zero. The removal of ‘noise’due to external influences means the focus is now on far fewer deviations.This supports H1, that controlling for these known patterns reduces thenumber of changes of interest across waves. Percentage changes over time for


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a subset of brand/attribute combinations are illustrated for both approachesin Table 2. Shaded in light grey are the changes that moved from being signifi-cant to being insignificant over time (a total of 12 were evident across allbrands and attributes). Shaded in dark grey are the two that changed frombeing insignificant to being significant when deviations were involved.

The difference in the number of perceptual response changes identifiedas significant was particularly noticeable for Brand 5. An examination ofthe raw percentage changes alone may have led to the interpretation thatthere was a problem with the advertising for the brand. There was adecrease over time of perceptual responses linking Brand 5 with ‘wideflavour range’ (–5%), ‘value for money’ (–6%) and ‘has something for allthe family’ (–6%). However, by looking at changes in deviation fromexpected figures (and therefore controlling for the effect that the change inusage has had on attribute responses), it is obvious that, for theseattributes, there has been little or no change in association with the brandover time. The reason for this decrease becomes evident when we comparethe brand usage figures over time. Brand 5 had 14% fewer users in thesecond wave. This reduction in usage may be of concern (indicating thebrand has a distribution problem or there was a sampling issue with oneof the waves), but this change should not affect the evaluation of theadvertising.

For the attribute ‘has unique ice cream flavours’, Brand 5 actuallyincreased in brand/attribute association over time (4 percentage points),


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Table 2 Fast-food market: raw percentage changes over time compared with deviations from

expected changes over time

Brand 1 Brand 2 Brand 3 Brand 4 Brand 5

Dev Dev Dev Dev Dev

from from from from from

Attribute Raw exp Raw exp Raw exp Raw exp Raw exp

Smoothies –1 0 –3 –1 –3 –3 –2 0 1 4

Wide flavour range –3 –1 –1 2 –1 0 –6* –2 –5 1

Always new products –1 –2 1 1 14* 12* 0 –2 –7* –8*

Premium quality –3 –2 0 1 0 0 0 2 –4 –1

Unique flavours –2 0 0 2 1 2 –11* –8* –1 4

Value for money 1 2 –7* –5* 1 2 1 3 –6* –2

Something for all the family –2 –1 –5 –2 –1 0 –1 3 –6* 0

Sells ice cream cakes 2 1 2 2 –3 –5 –1 –2 5* 4*

which was not picked up in the raw percentage figures (which indicated adecrease of 1%). Similarly, for Brand 3, for ‘sells ice cream cakes’, aninsignificant change of –3% becomes a more significant –5%.

To test H2, we compared the remaining significant attribute changeswith the messages of campaigns run by these five brands. Only two brands(3 and 5) ran campaigns with sufficient mass-media weight to expectchange. Therefore we would expect no major positive changes fromBrands 1, 2 or 4, but possibly negative changes if Brands 3 or 5 weresuccessful in building stronger links to specific attributes. During the timebetween data collection for the two waves, Brand 3 ran a campaign thatemphasised a ‘new menu’ with regularly changing products. The focus ofthe campaign was continuously on a ‘changing menu’ with specific sub-campaigns focusing on individual product sets at any one point in time.This campaign was run extensively and the advertising spend was at least20 times that of the nearest competitor. This activity can be linked to thepositive increase in deviation for the attribute ‘always introducing newproducts’, which is the only significant positive change for this brand.

Brand 5 ran campaigns focusing on two products – new favours ofsmoothies and ice cream cakes. This is apparent in a positive change indeviation from expected figures, for the attributes ‘sells ice cream cakes’,‘unique flavours’ and ‘sells smoothies’. Further, a greater negativedeviation is evident for Brand 3 on this attribute, suggesting thestrengthening of the link by Brand 5 has weakened the presence of Brand3. This is one example where controlling for known patterns influences theinterpretation of results by highlighting a change that would otherwisehave gone unnoticed.

Looking at the raw percentages in Table 2, it would have been assumedthat both Brand 2 and Brand 5 suffered decreases across many of thebrand/attribute combinations. But when the figures for Brand 5 areadjusted to control for known patterns, the decrease in brand/attributeassociation is reduced. While Brand 4 (and to a lesser extent Brand 5)suffered decreases for the attribute ‘wide flavour range’ in the rawpercentages, when usage and prototypicality is controlled for, thesedifferences are also reduced. Given there were no campaigns directlyfocused on this attribute, the latter result is more in line with what wouldbe expected from the campaigns actually run. Therefore the resultssupport H2. Incorporating prior knowledge and controlling for knownpatterns meant the changes that could be attributed to advertising activitywere more easily detected and interpreted, thus providing a better measureof advertising effectiveness.


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Study 2: financial services market

In Study 1 the advertising environment was quite easily defined, with twobrands conducting major campaigns and the others off air. Some marketshave much more complex environments where most brands are advertisingand often have several campaigns running at once. One such market isfinancial services. The data for Study 2 were collected as part of anongoing tracking project for a financial institution. The data were reportedin quarterly blocks, where 12 weeks of data constituted one quarter. Thisstudy focuses on quarters 3 and 4 of 2003, with a sample size ofapproximately 900 respondents each quarter.

Respondents were asked to record a given set of seven brands in thefinancial services market. Then a randomised set of 19 attributes were listedand respondents were asked which, if any, of the seven brands they associatedwith each attribute. Attributes were again product-related (for example, ‘offerscredit cards’, ‘has great home loans’, ‘offers investment products’) and generalattributes (for example, ‘is friendly and helpful’, ‘would have a lot of satis-fied customers’ and ‘would be a good partner for increasing your wealth’).

Changes over timeAcross the seven brands by 19 attributes (133 combinations), there was atotal of 39 (30%) statistically significant changes in raw percentages atthe p < 0.05 level. In this case, the majority of changes were increases (32,compared with 7 decreases). However, when we control for usage andprototypicality patterns, the number of significant changes reduces to onlyfour. In the financial services market, the brand/attribute associations weremore stable over the two quarters. This supports H1.

Table 3 shows the changes in deviation from expected values for a subsetof brand/attribute combinations. Here, a comparison of results can bemade between both methods. The shaded boxes highlight when there is adiscrepancy in the two approaches. Over all brand-attribute relationshipsthere were 36 discrepancies; 32 occurred when a significant change in theraw percentages became insignificant. Examples of where this occurs inthe table are shaded in light grey. For example, for Brand D, on the attri-bute ‘investment products’, the raw percentage change in respondents whomade the association over time increased by 13 percentage points, comparedwith the deviations from the expected increase of 1 percentage point. Fourdiscrepancies occurred when a previously insignificant change became moresignificant, illustrated by dark grey shading in the table. An example fromTable 3 is Brand B and has the ‘full range’, which goes from no change inthe raw percentages to decreasing by 5%.


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For the attribute ‘investment products’ we can see that all brands recordan increase in raw percentages, which disappear once usage andprototypicality are taken into account. In this instance, the wave 3 surveywas conducted post-end of financial year, where investments, as acategory, were less prevalent in the marketplace (as people were paying offtheir taxes and/or waiting for returns). However for wave 4 everythingwas back to its normal level, so what looks like an apparent rise is reallyjust a ‘seasonal’ effect. We see a similar pattern occurring across all brandsfor the attribute ‘confident’, which also showed a systematic increase inthe number of overall responses, or prototypicality, over the two timeperiods. This again reinforces the benefits of controlling for these externalpatterns.

To test H2, we examined the media schedule for each of the brands foreach quarter. While putting an advertisement to air does not guaranteeeffectiveness (some advertisements do not affect any brand perceptions) weshould be able to link positive changes back to specific campaigns. As afurther check we should not see major positive changes that cannot belinked to specific campaigns. Brand A has no deviations that remainsignificant. Brand B is a community-based bank and ran a corporatecampaign around this community-based positioning, and we see aconsistent positive change for a ‘bank for everyone’.

Brand C ran a credit card-based campaign in wave 4, which appears tobe linked to its consistent increase in that attribute. It also has a consistentdecrease in home loans, which is probably linked to the only majorincrease in that attribute for Brand E. Brand E ran a home loan campaignduring wave 4, and this appears linked to a positive change for that brandon that attribute. There were no significant changes for Brands D, F or G.Two of the three brands did run campaigns, with Brand D running one on‘guides to buying houses’, and Brand F running a corporate campaign andone about its home loan products. These campaigns appear not to havehad a noticeable effect on the buyer memory structures measured here.

Therefore while there was definitely advertising that did not affect thebrand perceptions included in this research, the changes we could identifyafter controlling for usage and prototypicality did seem to be able to beexplained through the advertising messages put forward. This providessupport for H2.


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Discussion

This paper illustrates how controlling for known patterns in brandattribute responses can lead to more clarity in interpreting the impact ofadvertising. The two patterns are the effect of usage and theprototypicality of the attributes within any particular survey. Whilechanges in these factors in themselves can provide insight, they cloud theability to detect advertising-related changes. Therefore we recommendcontrolling for, but not ignoring, these wider market changes.

We demonstrated, using data across two very different markets, how theanalysis of raw percentage changes in responses can lead managers todraw erroneous conclusions. The changes in perceptual responses fromone wave of the study to another were overstated, and some importantmessage-related changes were missed.

Controlling for these patterns reduces the number of major differencesto focus on, and is therefore easier to interpret. We were able to illustratethis by comparing the messages from specific campaigns with thesignificant differences over the two waves. While we recognise that thiscan be a subjective assessment, it does appear to have face validity.Importantly, the objective assessment of the reduction of the brand-attribute relationships that warrant attention, using an approach thatcontrols for known patterns, provided a clear benefit to those interpretingbrand tracking data.

Limitations and future research

A key limitation of this approach it that it is confined to a free-choice,‘pick any’ approach of brand-attribute relationship measurement. Furtherresearch should explore whether similar calculations are needed (andindeed possible) for other brand-attribute measures, such as ranking orrating.

Further replication is needed across markets and campaigns to furtherexpand the generalisability of this finding. Additionally, while it isencouraging that improvement in interpretation was evident with what arerelatively small changes over waves, validation should be undertakenwhen there have been major market shifts. Similarly, the stability of resultswhen brand or attribute lists change should also be explored. A usefuldirection for future research might also be to compare different techniquesfor obtaining expected results for brand perceptions, such as theRomaniuk and Sharp (2000) method with an additive logit modelapproach, to improve our understanding of perceptual data and what any


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brand ‘should’ score. The ability to derive meaningful expected values isan important area for the future of marketing metrics.

Future research could also examine the link between attribute responsechanges, when usage and prototypicality changes are taken into account,and future behaviour/brand performance. Quantifying the relationshipbetween the amount of TARPs spent and changes in deviations fromexpected would also be a promising step to quantifying return oninvestment from advertising spend.

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