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Banner Advertisements through a New Lens.

This article examines the impact of brand familiarity and internet user experience on banner-ad effectiveness. The results from a large empirical study show that there are major differences between the performances of banner ads for familiar and unfamiliar brands. Advertisements for familiar brands tend to wear out quickly, whereas banner ads for unfamiliar brands need multiple exposures to wear in. Major differences are also found between novice and expert internet users regarding their susceptibility to web advertising. Novice users are more affected by banner ads than are expert users. Implications based on the findings are discussed.

THE INTERNET IS THE FASTEST-GROWING medium of all time, and electronic marketing posits the biggest threat and opportunity to almost every industry in the 21st century (Eighmey and McCord, 1998; Achrol and Kotler, 1999). As consumers move online, so do advertisers. Advertising expenditures on the net increased by 121 percent to a total of 4 billion dollars in 1999 and are on a pace to increase more than twenty-fold over the next few years (IAB, 1999a; 1999b). As the internet continues to take an ever larger share of the marketing budget, the question of how to design and evaluate web advertising becomes crucial (Ducoffe, 1996; Hoffman and Novak, 1997; Dreze and Zufryden, 1998). The predominant form of web advertising is banner ads (IAB, 1999b). In this article we present the results from a large empirical study answering questions of how banner ads work--over time, for different brands, and for different internet user groups.

Empirical studies have investigated the communication effects of banner-ad impressions (Briggs, 1996; 1997; Briggs and Hollis, 1997) and what affects clickthrough (Doubleclick, 1996; Chatterjee et al., 1988 Hofacker and Murphy, 1998). With the exception of Dahlen et al (2000), which examined differences between high- and low-involvement products, no reported study has investigated how banner ads work for different products. In this article, we compare high- and low-familiarity brands (cf. Tellis, 1988; 1997). We will also make comparisons between high- and low-experience internet users (cf. Daheln 1997; Ward and Lee, 2000), which has important implications for the future as the web population grows more experienced.

Many authors have discussed what should be the optimal frequency of ad impressions in traditional media (cf. Krugman, 1972; Ephron, 1995; Naples, 1997). With the exception of Doubleclick (1996), which reported diminished clickthrough rates, and Chatterjee et al. (1998), which reported a U-shaped clickthrough rate with multiple impressions, little research has been done on banner ads in this respect. In this article we will investigate both the clickthrough rates and communication effects from mere ad impressions for multiple banner exposures.

BRAND FAMILIARITY AND ADVERTISING RESPONSE

Brand familiarity is an important factor with regard to advertising response. Familiar brands have high advertising leverage (Rossiter and Percy, 1997; Ehrenberg et al., 1997). They can thus be advertised at a low frequency and receive immediate response (Ephron, 1995). Tells (1988) presents evidence that consumer response to repetition of an advertisement differs substantially depending on the familiarity of the brand. This can be explained by the fact that consumers get used to the advertisement quicker (habituation) and tire sooner (tedium) of the advertising for a familiar brand (Tellis, 1997; Sawyer, 1981). The opposite may be true for unfamiliar brands. This means that familiar brand advertising rather quickly wears out, whereas unfamiliar brand advertising may need repetition to wear-in. This could have very important implications for web marketing, as banner ads should work differently for familiar and unfamiliar brands.

On the internet, brands may be even more important than in the physical world (Alba et al., 1997). With the risk of information overload, familiar brands work as shortcuts. In a study by Ward and Lee (2000), internet users are found to react more favorably to familiar brands when shopping on the web, as they prefer the sites of well-known brands. We can expect the same for banner ads.

As the internet is an information-rich medium, we would expect web users to react more immediately to banner ads for familiar brands, and these ads should thus receive higher initial clickthrough rates than unfamiliar brands. This leads us to our first hypothesis:

H1: Familiar brands will receive higher initial clickthrough rates than unfamiliar brands.

As banner ads for familiar brands receive more initial attention, they should also wear-out quickly with repetition. We can thus expect a quick drop in clickthrough rates with repeated banner-ad exposures. Furthermore, the communication effects of additional exposures should show decreasing returns and maybe even be negative due to tedium. This concerns brand attitude but not brand awareness (due to the high familiarity of the brands, awareness cannot be

expected to change much). This leads to our second and third hypotheses:

H2: For familiar brands, clickthrough rates will decrease with repeated banner-ad exposures.

H3: For familiar brands, brand attitude will not increase (or might even decrease) with repeated banner-ad exposures.

Contrary to familiar brands, unfamiliar brands need more repetition to wear-in. Internet users may need more than one exposure to even notice the banner ad. Furthermore, brand awareness and brand attitude may have to be established before web users feel inclined to click on the advertisement. Building on the evidence of Briggs and Hollis (1997), we expect brand awareness and brand attitude to increase with repeated banner-ad exposure. With these communications effects we can also expect clickthrough rates to increase with repeated banner-ad exposure for unfamiliar brands. This leads to the following hypotheses:

H4: For unfamiliar brands, brand awareness and brand attitude will increase with repeated banner-ad exposures.

H5: For unfamiliar brands, clickthrough rates will increase with repeated banner-ad exposures.

INTERNET USER EXPERIENCE AND ADVERTISING RESPONSE

Internet user experience is important to consider, as a large part of the rapidly increasing web population consists of new users. There is a big spread among the novel users and experienced users who have several years of experience with the web. The inflow of new users will continue for a long time. At the same time, the existing web population is aging and becoming more experienced. Research shows that novel and experienced customers differ markedly in their behavior and response to marketing (Alba and Hutchinson, 1987; Maheswaran and Sternthal, 1990). The same thing can be expected for internet users.

Experience with the internet has been shown to influence user behavior. Dahlen (1999) found that internet experience was an important factor in explaining proneness to shop on the web. Internet users' browsing behavior depends on experience (Novak et al., 2000). More experienced users tend to search less and be more confident when online (Ward and Lee, 2000).

As internet users become more experienced they become more focused in their usage sessions (Hoffman and Novak, 1996). This makes them less inclined to react to unexpected stimuli and rush off somewhere they had not planned to go (Dahlen, 1997). Bruner and Kumar (2000) found that experienced users were less distracted by competing stimuli when on a website. This has important implications to marketers as experienced users should be harder to influence online. Some evidence for this was presented in Dahlen et al. (2000), which found a negative relationship between user experience and inclination to click on banner ads. Dahlen (1997) noted that there seems to be a threshold effect in that the greatest differences between internet users seem to be between those who have used the web less than five to seven months and those who have used it longer.

As experienced users are more focused and less willing to digress from their intended path, they should be hard to attract to other websites by way of banner ads. Less experienced users, on the other hand, should be easier to attract and pose a better target for banner ads. This leads to the following hypothesis:

H6: Less experienced users will click more on banner ads than more experienced users.

Experienced users are more focused, often experiencing "flow," which tends to block out everything else (Hoffman and Novak, 1996; Novak et al., 2000). They should thus be less impressionable by banner ads than less experienced users. Less experienced users, on the other hand, can be expected to be influenced by banner ads even without clickthrough. This leads to the following hypothesis:

H7: Less experienced users will exhibit a greater change in brand awareness and brand attitude from banner-ad impressions than more experienced users.

METHOD

In order to test our hypotheses, we must be able to measure how many times internet users have been exposed to certain banner ads and connect these ad impressions on an individual level with click-through behaviors and the internet users' levels of brand awareness and brand attitude. This way we can measure the effects of banner-ad impressions on click-through and communication effects. Furthermore, we need banner ads for familiar brands and unfamiliar brands, respectively, in order to make comparisons between the two product types.

Laboratory experiments are less suited for our purpose, as we want to measure internet users' actual behaviors (cf. Burke et al., 1992; Campo et al., 1999). In our design, we have unobtrusively observed internet users' clickthrough behaviors in response to a selection of banner ads for familiar and unfamiliar brands. Afterwards, we have surveyed the internet users and matched the responses on an individual user level with the banner-ad impressions and clickthrough behaviors.

The design

The study design was based on the design reported in Briggs and Hollis (1997). Banner ads for both familiar and unfamiliar brands were placed in the available advertising spaces on Sweden's most visited website, the Passagen portal. The design is illustrated in Figure 1.

The banner ads were exposed during a period of one week. Upon entering the site, randomly selected visitors were intercepted by a pop-up dialogue box, asking for their participation in a study about marketing on the internet. If the visitor accepted the invitation, they typed their e-mail address and sent the reply. Those who did not consent answered "no" and were not asked again. We employed systematic sampling without replacement, meaning that the same visitor could not be selected more than once (Newbold, 1991; Malhotra, 1993). This was achieved by using-[n.sup.th] Visitor intercept method with cookie files registering each visitor.

As the visitor sent his or her consent to our database, a cookie file was placed in the visitor's web browser. The cookie file contained information about exposures to banner ads, if the visitor clicked on a banner ad, and the visitor's e-mail address. This was done in order to be able to match the respondent's answers in the following questionnaire with the respondent's behavior.

Six days after the banners were first exposed on the website, an e-mail was sent to the visitor containing the address and hyperlink to a web questionnaire. The information in the previously stored cookie file was sent along with the respondent's answers to a database.

The banner ads

Seven banner ads were carefully chosen for the study. Three were classified as unfamiliar brands and four were classified as familiar brands. The classification was based on the responses from a control group sample taken on the Passagen web-site the day before the banner ads were exposed. There were 285 randomly selected site visitors who answered a questionnaire that measured aided recall and experience with the seven brands. (These measures will be described in more detail in the measures section.) Brands with high levels of aided recall (mean values over 90 percent) and consumer experience (high frequency relative to product category) were deemed as familiar. Brands with low levels of aided recall (mean values below 20 percent) and consumer experience (low frequency relative to product category) were deemed as unfamiliar. The banner ads were:

* Familiar Brands

Skandia--insurance company

Atlas--travel agency

Via-detergent

Sia--ice cream

* Unfamiliar Brands

EU-bildelar--automobile parts

Zoo Village-clothing

Zoegas--coffee

The banner ads were designed so that creative differences would not confound the results. For example, they did not include non-product-related appeals (such as contests, etc.). A manipulation check was conducted to ascertain that advertising copy quality did not interfere with the results. A control group sample of 48 business students rated the copy of each of the banner ads (brand names were removed) on the 5-point likability scale reported in Haley and Baldinger (1991). This measure is often cited as the best discriminator between more and less effective advertisements (cf. Brown and Stayman, 1992). The mean values were 2.58 for familiar brand advertising copy texts and 2.61 for unfamiliar brand advertising copy texts, indicating that there are no significant differences (p [greater than] 0.8) between the advertisements.

The sample

The response rate in the first recruitment step (where visitors were asked to leave their e-mail address) was 29 percent. In the second step, 75 percent completed the questionnaire. The demographic and internet usage profiles of the sample closely resembled the profiles of the website visitor population (see Table 1).

Due to problems of reading cookie files from some respondents, a number of responses were disregarded. There are inherent problems with web browser-based sampling, e.g., an individual may use several web browsers, or a web browser may not accept cookie files (for a review, see Dahlen, 1997). A total of 14,600 responses were collected together with information on respondents' behaviors.

Measures

Two measures were used to assess the familiarity of the brand. Brand awareness was measured as aided recall (cf. Briggs and Hollis, 1997). Experience with the brand was measured with a scale where respondents were asked to indicate how many times they had been in contact with the brand (cf. Alba and Hutchinson, 1987). The scale was tailored to each product class.

Brand attitude was measured with the question "What do you think about this brand compared to other brands in the same product category?"; this was answered on a 7-point scale (1 = brand is the best brand in the category, 7 = brand is the worst brand in the category). For each product, the specific brand and product category were substituted in the questions. The measure was based on the recommendations of Gardner (1985) and Rossiter and Percy (1997).

Internet experience was measured with a 5-point scale (1 = [less than]6 months, 2 = 6-12 months, 3= 1-3 years, 4=4-6 years, 5 = [greater than]6 years). The measure was taken from the GVU questionnaires used in Ward and Lee (2000) and Novak et al. (2000).

RESULTS

Brand familiarity

In order to test the effects of brand familiarity on response to banner ads, crosstabulations and mean comparisons were performed. First, the clickthrough rates for familiar and unfamiliar brands were compared.

Familiar brands had a total clickthrough rate of 0.5 percent, whereas unfamiliar brands have a mere clickthrough rate of 0.2 percent. Overall, familiar brands receive more than double the clickthrough rate of unfamiliar brands. The difference is statistically significant (p [less than]0.01).

As can be seen in Table 2, familiar brands had an initial clickthrough rate of 1.0 percent, which is 10 times the initial clickthrough rate for unfamiliar brands.

The difference is statistically significant (p [less than]0.01). Hypothesis 1 is thus supported: familiar brands receive higher initial clickthrough rates than unfamiliar brands.

Investigating the clickthrough patterns further, we find that the clickthrough rates decrease with multiple exposures of familiar-brand banner ads (p [less than] 0.01). The opposite pattern is found for unfamiliar brands. Here, clickthrough rates increase with multiple banner-ad exposures (p [less than] 0.01). At three and four exposures, there is no statistically significant difference between familiar and unfamiliar brands. At five or more exposures, unfamiliar brands have a higher clickthrough rate than familiar brands (p [less than] 0.01). H2 and H5 are thus supported: for familiar brands, clickthrough rates decrease with repeated banner-ad exposures, and for unfamiliar brands, clickthrough rates increase with repeated banner-ad exposures.

Next, we turn to the communication effects of banner-ad impressions. Surprisingly, comparisons between exposed and nonexposed internet users show no differences in brand awareness and brand attitude. Cross-tabulations and mean comparisons yield nonsignificant results (p [greater than] 0.3) for comparisons between different numbers of exposures and for comparisons between nonexposed and exposed respondents. Mere banner-ad impressions do not seem to have an effect on brand attitude and brand awareness. Thus, we cannot find any support for wear-in and wear-out effects of banner-ad impressions. Hypotheses H3 and H4 are then not supported.

Internet experience

In order to investigate the effects of internet user experience on advertising response, the different user groups were compared on clickthrough rates and communication effects. First, a crosstabulation of clickthrough rates for the different user groups was performed. The result indicates a statistically significant relationship between user experience and inclination to click on banners (see Table 3).

As can be seen, less experienced users are significantly more inclined to click on banners than more experienced users (p [less than] 0.01). The clickthrough rate decreases as the internet users become more experienced. Hypothesis H6 is supported: less experienced users click more on banner ads than more experienced users.

Next, we compare the internet user groups with respect to communication effects from banner-ad exposures. Differences between nonexposed and exposed respondents are calculated for each group. The results indicate that there seems to be a threshold effect (as suggested in Dahlen, 1997). The least experienced users exhibit an increase in both brand awareness and brand attitude, whereas there are no significant increases in the other groups (Table 4). Hypothesis H7 is thus supported: less experienced users exhibit a greater change in brand awareness and brand attitude from banner-ad impressions than more experienced users.

The change in brand awareness and brand attitude in the least experienced user group was examined further. Specifically, it was interesting to see if the hypothesized wear-in and wear-out patterns over multiple exposures would surface. Respondents exposed to banner ads one, two, three, four, and five or more times were compared. Differences from the nonexposed respondents were calculated. The following patterns appear (see Table 5).

As can be seen, too many exposures of familiar brand banner ads have a negative impact on brand attitude. One and two banner ad impressions clearly increase positive brand attitude. From three impressions onwards, however, brand attitude falls below the base level. This gives some support for our previous hypothesis about ad wearout for familiar brands. Not surprisingly, there are no differences in brand awareness.

A slightly different pattern appears for the change in brand attitude for unfamiliar brands. Here, multiple ad impressions result in a U-shaped pattern. The greatest positive changes in brand attitude appear at one and five or more exposures. At three exposures, brand attitude is at a minimum, even lower than the base level. There are no significant differences in brand awareness.

IMPLICATIONS

The present study shows that the question of how banner ads work is too simple. There is not one general answer. They work differently for different products, and hence the goals should not be the same for all banner ads. We have looked at two inherent factors, brand familiarity and internet user experience, that affect banner-ad performance both short-term and long-term. This is also an important lesson--there is a short term and a long term to consider in banner advertising.

Banner ads for familiar brands work best short-term. The initial clickthrough rates are relatively good. However, with repeated exposures clickthrough decreases rapidly. The opposite is true for unfamiliar brands. Observed only short-term, i.e., over the first couple of exposures, unfamiliar brands perform badly with very low clickthrough rates. With repeated exposures the trend is positive, resulting in a dramatic increase in clickthrough. Based on this, we would recommend that unfamiliar brand banner ads should have long-term goals allowing multiple advertising exposures. For familiar brands, on the other hand, there should not be a long-term goal as the advertisements quickly wear out.

A surprising result is the fact that mere banner-ad impressions did not affect brand awareness and brand attitude. This gives another answer to the question "is there response before clickthrough?" than was found in Briggs and Hollis (1997). A plausible reason for this discrepancy is that the share of experienced users has increased. Our study shows that experienced users are harder to affect on the net. One important implication based on the result is that more focus should be given to cickthrough and less focus should be on mere advertising impressions.

Novice internet users are clearly the ones that are most susceptible to banner advertising. Web users with less than six months' experience are a target for advertisers. The clickthrough rate is almost four times greater than among other users. This means that banner ads can continue to be considered as rather attractive traffic generators as long as there are novice internet users that can be targeted. The web population is increasing rapidly, thus ensuring that the share of novice internet users will be substantial. However, in the long run, banner ads may lose their traffic-generating abilities as the share of experienced internet users increases.

Banner ads were shown to, in fact, have an effect through mere impressions, when we looked at novice internet users specifically. Both brand awareness and brand attitude were increased without clickthrough. Internet users with little experience are less focused on their task and are thus more impressionable. Obviously, response before clickthrough is an important goal when these users are targeted.

Looking further into the communication effects from advertising impressions among novice users, we find that familiar brands and unfamiliar brands behave differently once again. Familiar brand banner ads have a positive effect on brand attitude when exposed once or twice. After that, they quickly overstay their welcome. Clearly, familiar brand advertisements wear out with multiple exposures. The response to unfamiliar brand advertisements is U-shaped. The greatest increases in brand attitude appear at either one or at five or more exposures, indicating that the banner ads work best either immediately or with a rather large number of exposures.

Taking both clickthrough and communication effects into account, the conclusion is that familiar brands should focus on immediate reactions as they quickly wear out. Unfamiliar brands need many exposures to increase clickthrough, whereas change in brand attitude is most positive at the initial and high-numbered exposures. Therefore, a high number of exposures should be optimal as unfamiliar brand advertisements need repetition to wear in.

Further effort need to be put into developing new advertising formats that can attract and influence experienced users, as more and more users leave the novice stage. For banner advertisers, ways of finding and exposing advertisements to new and novice internet users is critical and an important task in the future.

MICAEL DAHLEN is assistant professor at the Center for Consumer Marketing, Stockholm School of Economics. His research focuses on advertising and promotion effectiveness with an emphasis on new media. His work has been published in the International Journal of Advertising, Scandinavian Journal of Management, European Business Forum, and Consumption, Markets & Culture.

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TABLE 1

Sample and Population Profiles


                            Sample             Population

Mean weekly internet usage  1 hour 44 minutes  1 hour 40 minutes
Gender male/female          61/39              59/41
Mean age                    29.5 years         30.0 years
TABLE 2

Clickthrough Rates for Familiar and Unfamiliar Brands and Different
Numbers of Exposures


Number of Exposures  Total  1     2     3     4     5

Familiar brands      0.5%   1.0%  0.6%  0.4%  0.4%  0.3%
                                                    p [less than] 0.01
                                                    (n = 11390)

Unfamiliar brands    0.2%   0.1%  0.1%  0.2%  0.3%  0.8%
                                                    p [less than] 0.01
                                                    (n = 14448)
TABLE 3

Clickthrough Rates in the Different Internet Experience Groups


User          [less than]6 Months  6-12 Months  1-3 Years   4-6 Years
Experience    Experience           Experience   Experience  Experience

Clickthrough
 rate         2.3%                 0.6%         0.4%        0.3%




User          [greather than]6 Years
Experience    Experience

Clickthrough
 rate         0.3%



n = 5916, p [less than] 0.01.
TABLE 4

Brand Attitude and Brand Awareness Change Related to User Experience


User        [less than]6 Months   6-12 Months   1-3 Years
Experience  Experience            Experience    Experience

Brand       +3.49%                Not           Not
 awareness  (p [less than] 0.01)   significant   significant

Brand       +16.9%                Not           Not
 attitude   (p [less than] 0.01)   significant   significant




User        4-6 Years     [greater than]6 Years
Experience  Experience    Experience

Brand       Not           Not
 awareness   significant   significant

Brand       Not           Not
 attitude    significant   significant



n = 3930.
TABLE 5

Brand Attitude Change with Repeated Exposures, Users with [less than]6
Months Experience (All Differences Compared to Nonexposed Respondents)


Number of Exposures     1       2       3      4       5

Familiar brands         +32.5%  +26.5%  -5.5%  -19.9%  -20.0%
n = 2011 p [less than]
0.01

Unfamiliar brands       +19.0%  +16.9%  -6.5%  +15.0%  +20.8%
n = 2114 p [less than]
0.01
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Comment:Banner Advertisements through a New Lens.
Author:DAHLEN, MICAEL
Publication:Journal of Advertising Research
Geographic Code:1USA
Date:Jul 1, 2001
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