Internet Ads

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Internet advertising effectivenessThe effect of design on click-through rates

for banner ads

Helen Robinson, Anna Wysocka and Chris HandKingston University

Online advertising has experienced phenomenal growth since its inception in 1994. This

empirical study investigates the impact of seven creative characteristics of banner ads on

the effectiveness of online advertising using a multiple regression model. A random sam-

ple of 209 banner ads was drawn from a sampling frame of advertisers, provided by anadvertising agency specialising in internet advertising for the gaming industry. The find-

ings of this study are broadly consistent with past research into online advertising effi-

ciency, indicating that the creative characteristics of effective banner ads in the online

gaming arena include: a larger size, absence of promotional incentives and the presence

of information about casino games. In contrast, banner features such as animation, action

phrase and presence of company brand or logo were ineffective in generating click-

throughs. Contrary to expectations, long messages on banners were associated with

higher click-through rates.

Introduction

Since the first banner ads appeared in 1994, the internet advertising indus-

try has experienced exceptional growth. The Interactive Advertising

Bureau (IAB) heralded 2003 as the most successful year in association his-

tory, recording full-year revenue as almost US$7.3 billion (IAB 2003).Existing academic research encompasses a broad spectrum of studies on

internet advertising effectiveness measured by direct response and brand-

ing metrics. The few studies that have included click-through rate (CTR)

as a measure of online advertising effectiveness have examined the rela-

tionships between CTR and a series of factors such as web user motives,

audience targeting, exposure frequency, copy content and a limited num-

ber of design elements of banner ads. Advances in technology have made

International Journal of Advertising, 26(4), pp. 527541


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INTERNATIONAL JOURNAL OF ADVERTISING, 2007, 26(4)

online gambling one of the fastest-growing industries in recent years.

While in 1999 there were only 300 online casinos worldwide, this number

had grown to 1800 by 2004 (Constable 2003; Catty 2004).

This empirical investigation broadens the existing academic knowledge

of online advertising efficiency and, more specifically, contributes to the

understanding of which creative elements of banner ads impact click-

throughs. In addition, this study is the first to examine internet advertis-

ing effectiveness in the context of the online gaming industry. It is not the

intention of this study to enhance the effectiveness of the advertising of

online casinos. Rather, it centres on whether the effects of banner design

on click-through differ between this market and others. Online gambling

is an example of an industry where those who participate online may not

have considered participating offline. This contrasts with most otherexamples of e-commerce (e.g. purchasing groceries, clothes, books).

Hence, an online casino has to build credibility from its banner ads and

website alone. Consequently, there might be reason to believe that differ-

ent banner characteristics would increase effectiveness more than those

previously found in other industries.

Nevertheless, given ongoing concerns and much recent debate over the

growth of the gambling sector and relaxed restrictions on broadcast adver-

tising (from September 2007 in the UK, for example), an understanding ofthe effectiveness of online advertising is of interest to both the casinos and

their regulators.

Previous research

Internet advertising, and in particular banner effectiveness, in this rela-

tively new medium has received considerable attention from academics

and practitioners. The study of online advertising effectiveness has beenconducted through two alternative paradigms. The first, widely used in

academic research, argues that banner adverts should be considered as a

form of marketing communication used to raise brand awareness. The sec-

ond, predominantly used in empirical research, contends that the internet

is a direct marketing medium, and hence a banner ad is likened to a

coupon in print media. Accordingly, the success of the banner ad should

be measured through the return rate or CTR for the internet (Chandon

et al. 2003).


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INTERNET ADVERTISING EFFECTIVENESS

Dreze and Hussherr (2003) found that internet users avoided looking at

ads while online, and hypothesised that internet users might perceive ban-

ner ads in their peripheral vision. Similarly, Janiszewski (1998) claimed

that peripheral vision allows individuals to recognise objects that are

located outside their focal point of attention.

Several studies suggest that click-through effectiveness may depend on

web user motives. Briggs and Hollis (1997) argue that the primary factor

in generating click-throughs is the nature of the audience and what the

inherent interest in the product category may hold for them. Further

studies have concluded that banner ads that complement the users web

motives may be more effective (Raman & Leckenby 1998; Rodgers &

Thorson 2000; Rodgers 2002; Danaher & Mullarkey 2003).

Practitioners and academics agree that while repetition reduces click-throughs, it builds brands (Broussard 2000; Dynamic Logic, 2000; Gugel

2001; Chatterjee et al. 2003; Danaher & Mullarkey 2003; Dreze &

Hussherr 2003). A considerable body of research has demonstrated that

successful targeting of online ads improves CTR (Briggs & Hollis 1997;

Sherman & Deighton 2001; Chandon et al. 2003; Chatterjee et al. 2003).

Relevance of the advertisement to the site may also be a determinant of

click-through rates. Chang-Hoan (2003) found that those more involved in

a product were more likely to click through, but also that users who wereinterested in the site and shown advertisements for products and services

related to those on the site also achieved a higher click-through rate.

The relationship between the banner size and CTR is conflicting.

Baltas (2003) found that bigger ads are more effective in attracting atten-

tion and (hence more likely to) trigger response. Chandon et al. (2003)

found positive interactions on five banner sizes, although no significant

difference between the two bigger sizes. In contrast, Dreze and Hussherr

(2003) tested the standard banner against two other banner sizes andfound the smaller ads performed just as well as the large ones, in accor-

dance with Cho (2003) who found no significant relationship between

banner size and clicking. Comparably, Rettie et al. (2004) established

that banner size impacted click-through and post-impression measures

differently.

Research on the impact of price and promotions on click-throughs has

revealed that none of these various stimuli (gift, rebate or free offers) has

a direct effect on CTR (Chtourou et al. 2002; Baltas 2003; Rettie et al.


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2004). Chtourou et al. (2002) found that the mention of price reduced

direct response. Rettie et al. (2004) discovered that banners which men-

tioned neither price nor promotional offers had the most effect on click-

through and post-impression rates.

Conflicting evidence exists on the impact of branded banners on click-

through rates. Research conducted by Baltas (2003) and Chandon et al.

(2003) revealed a negative impact and suggests that unbranded banners

might stimulate greater curiosity, leading to click-through. Similarly,

Dahlen (2001) found that familiar (as opposed to unfamiliar) brands

received double the click-through. In contrast, Briggs and Hollis (1997)

argued that the practice of running unbranded banners ... surely runs

counter to the concept of brand building through ad banner exposure, a

view supported by Dynamic Logic (2002).Dreze and Hussherr (2003) concluded that, in terms of artistic influ-

ences, audiences were most affected by the banner message rather than

how the message was conveyed. They concluded that artistic execution

overall had little effect on both click-throughs and traditional memory-

based effectiveness measures, with the exception of animation, which

influenced aided brand recall. Baltas (2003) found that banners with

lengthy messages and multiple frames (animation) received fewer clicks.

He reasoned that these two factors increase the complexity of an ad andhence have a negative effect on the viewers attitude towards and response

to the banner. It has also been suggested that animated banners may be

more difficult to remember than static ones (Burke et al. 2005). On the

other hand, Chandon et al. (2003) and Lothia et al. (2003) concluded that

animation improved click-through rates.

Overall, existing research on the relationship between creative charac-

teristics of banners and CTR reveals conflicting results. An early study by

Hofacker and Murphy (1998) suggests that creative factors tend toincrease response rates. The inclusion of clichd messages such as click

here or click now, along with animations and cryptic messages, was sug-

gested as methods of increasing response rates. More recently, evidence

has shown little support for a positive relationship between artistic vari-

ables and direct response (Dreze & Hussherr 2003). Moore et al. (2005)

indicate that there may be a conflict between getting a user to attend to a

banner while maintaining a favourable attitude towards it. Their research

suggests that the use of incongruent colour schemes in banners compared


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to the rest of the website results in more attention being given to that ban-

ner, but that the attitude towards the banner was more negative than

towards a banner more congruent with its website.

Research design

The aim of this empirical study is to investigate the impact of seven

design characteristics of banner ads on the click-through rate using data

from 209 real banner advertisements hosted on a single website in the con-

text of online gaming. The research builds upon past research in the area

of online advertising efficiency and, in particular, a study conducted by

Baltas (2003). The study investigates relationships between the CTR and

the seven design elements of banner ads discussed earlier: banner size,message length, promotional incentive, animation, action phrase, com-

pany brand/logo and casino games. The design element games is also

included to reflect the focus on the online gaming industry.

This study examines the impact of creative elements of banner ads on

CTR, the direct response metric used for measuring internet advertising

effectiveness. Data were provided by an advertising agency (specialising

in internet advertising for the gaming industry) that employs the Adtrack

system, which records impressions, clicks and CTR for each banner on adaily basis. The database comprised an inventory of 70 advertisers and

approximately 1500 banners that had appeared between 2001 and 2004.

A sample of 14 advertisers and their 209 banners was extracted. The

original inventory of 70 advertisers was filtered to 58 advertisers who ran

campaigns for at least a three-month period. In order to reduce the sample

to a manageable size, 14 advertisers were selected randomly from the list

of 58 remaining advertisers and, finally, the CTR data for the resulting

209 banners from the 14 advertisers for a three-month period wereextracted, forming the data set. The three-month period was chosen to

ensure a substantial database for the ensuing statistical analysis and also to

control for the moderating impact of the length of an ad campaign on the

CTR.

Click-through rates provide the marketer with considerable advantages,

as click-throughs record voluntary behaviour in the actual medium envi-

ronment. Furthermore, the data are collected unobtrusively; are based on

observed behaviour rather than self-reporting; and are free from researcher


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bias and recorded on a census of consumers rather than a sample

(Chatterjee et al. 2003).

A standard multiple regression model was used to examine the rela-

tionship between the dependent variable (CTR) and the seven creative

banner characteristics to determine which had a significant effect on CTR.

Research findings

Prior to running the regression, the CTR variable was found to be highly

skewed, with few banners obtaining high rates of click-through (the his-

togram is shown in Figure 1). An initial run of the regression model pro-

duced non-normal errors. In order to overcome this problem, CTR was

transformed by taking logarithms (a common remedial measure for non-normal errors in econometrics, for example see Gujarati (1995)).

The source website employs a total of seven distinct sizes of banner that

were included in the original model as six dummy variables (the standard

Frequency

0.00% 5.00% 10.00%

CTR

15.00%

200

150

100

50

0

Figure 1: Histogram of click-through rates


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banner size, 468 60 pixels, being the base category). One dummy was

dropped due to a collinearity problem. A more conventional approach

might be to include banner height and width in the model (as one

reviewer suggested). However, given the design of the source website, it

was impossible to separate the effects of banner size and placement. A

banner of a particular size can appear only in a given place on the web page

(e.g. long, thin banners appear only at the top and the bottom of the page).

Consequently, it was not possible to separate the effect of placement and

size; therefore generalising the results to other banner sizes would be

potentially misleading. The effect of message length was also captured by

a series of dummy variables. Again, at first glance, the number of words

would be the most obvious measure to use. It was decided to code mes-

sage length as a series of dummies for two reasons. First, the messagelengths were clustered around a few lengths. Second, the effect of adding

a word or two to the message on click-through is likely to be small; larger

differences are likely to be found between messages of different lengths.

These differences would be more apparent from a comparison of the

dummy variable coefficients. An alternative approach would be to use the

total number of words on the banner, which would permit testing for poly-

nomial relationships, as long as there was sufficient variation in message

length across banners.The effects of the presence of a logo, a promotional offer, the use of a

clichd phrase and whether the games offered were listed on the banner

were captured by a series of dummy variables. The study also investigated

whether animation on banners attracts greater click-through. Rather than

use another dummy variable, the number of transitions between frames

was used (i.e. 0 = static, 1 = two frames or 1 transition, and so on).

The multiple regression model takes the form of the following

equation:

log CTR = b0

+ b1

Bsize 120 120 + b2

Bsize 120 240 + b3

Bsize 175

60 + b4Bsize 234 60 + b

5Bsize 360 240 + b

6Smsg

+ b7

Mmsg + b8Lmsg + b

9Frames + b

10Promotion

+ b11

Clich + b12

Logo + b13

Game + e


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where:

log CTR is the dependent variable,

b0

is the constant,

b1

to b13

are the regression coefficients for the corresponding independent

variables as follows:

Bsize 120 60 Bsize 360 240 = banner size dummies (base

category = standard banner size

468 60)

Smsg = short message (15 words) dummy

Mmsg = medium-length message

(610 words) dummyLmsg = long message (1115 words)

dummy (base category = very long

messages, 16+ words)

Frames = number of transitions if animated

(= 0 if static)

Promotion = promotional incentive dummy

Clich = stereotypical action phrase dummy

Logo = company brand/logo dummyGame = casino games offered at an online

casino dummy

e = residual.

Inspection of a histogram of the residuals, a plot of the residuals against

predicted CTR (shown in Figures 2 and 3 respectively) and collinearity

diagnostics (shown in Table 1) suggested that the assumptions of normal-

ity, homoscedasticity and no multicollinearity were met. The modelexplains 68% of the variation in log CTR. As the dependent variable was

transformed, the regression coefficients can not be directly interpreted as

the effect of a unit change in the independent variable on CTR. Instead,

the effect of each variable is calculated using (antilog b) 1, which gives a

measure of the magnitude of the variables effect on CTR, shown in the

magnitude column of Table 1 (Gujarati 1995).


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Frequency

Regression standardised residual

40

30

20

10

0

3 2 1 0 1 2 3

Figure 2: Residuals histogram

3 2

Regression

standardised

predicted

value

1 0

Regression studentised deleted (press) residual

1 2 3 4

3

2

1

0

1

2

Figure 3: Scatterplot of dependent variable against residuals (heteroscedasticity test)


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Compared to the standard banner size (468 60 pixels) only two ban-ner sizes have a significant impact on CTR (at the 5% level). Small ban-

ners (175 60 pixels) receive 33.9% fewer clicks than standard banners. In

contrast, large banners (360 240 pixels) receive 495.4% more clicks than

standard banners. However, it should be remembered that the size meas-

ure used for this study also captures a banners position on the webpage,

hence not all of the increased click-through can be attributed to the size of

the banner. Describing or listing the games available on the site also sig-

nificantly increases CTR by 43.5%. It allows the website user to find a sitethat appeals to them more quickly as it reduces the time spent searching

for a particular game.

The regression model also produces some surprising results. Banners

with short, medium and long messages receive significantly fewer clicks

than very long messages. This result may reflect the finding that larger

banners obtain a higher CTR; long messages tend to appear on larger ban-

ners (if only because they fit). Promotional incentives generate signifi-

cantly fewer clicks than banners without such incentives. Apparently, the

Table 1: Regression results

Description b Std error t Sig. Magnitude (%) Tol. VIF

(Constant) 0.111 0.293 0.381 0.704 10.506Banner 120 120 0.296 0.168 1.762 0.080 25.621 0.828 1.208

Banner 120 240 0.339 0.199 1.701 0.091 28.752 0.813 1.230

Banner 175 60 0.414 0.198 2.089 0.038 33.900 0.823 1.215

Banner 234 60 0.289 0.165 1.753 0.081 25.099 0.830 1.215

Banner 360 240 1.784 0.181 9.861 0.000 495.362 0.584 1.713

Short Mgs (15W) 0.526 0.252 2.084 0.038 40.904 0.441 2.268

Med Mgs (610W) 0.438 0.178 2.454 0.015 35.467 0.341 2.933

Long Mgs (1115W) 0.635 0.169 3.748 0.000 47.006 0.434 2.306

Animation 0.061 0.036 1.698 0.091 5.918 0.858 1.166

Promo incentive 0.792 0.113 7.012 0.000 54.706 0.824 1.218

Clich 0.016 0.114 0.141 0.888 1.613 0.864 1.157

Logo/brand 0.148 0.199 0.740 0.460 13.757 0.773 1.294

Games 0.361 0.123 2.924 0.004 43.476 0.674 1.485

R2 0.681

Adj. R2 0.659

F 31.668 (sig. = 0.000)

Note: Tol. = tolerance; VIF = variance inflation factor


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offer of free bets is not enough to entice online gamblers to try a particu-

lar online casino; indeed, such offers would seem to have the opposite

effect than was intended.

The use of clichd phrases has no significant effect on click-through

rates; neither does the presence of logos or brand emblems, perhaps

reflecting the lack of brand awareness in this subset of the gambling

industry.

Conclusions and recommendations

This research indicates that the design elements of effective banner ads

include: a larger size, long message, absence of promotional incentives and

the presence of information about casino games. In contrast, creative char-acteristics established as being ineffective in attracting a direct response

include: stereotypical action phrases and the company brand/logo. These

findings are generally consistent with past research in the area of online

advertising effectiveness.

The largest banner ads are more effective in generating click-through

than the smaller-sized banners, replicating findings by Baltas (2003),

Chandon et al. (2003) and Chtourou et al. (2002), and supporting the indus-

try mantra that bigger is better (Briggs, 2001a; Dynamic Logic, 2004).However, this study contradicts some earlier findings that banner ads car-

rying a shorter message are more effective than those with a lengthy mes-

sage. Animation, on the other hand, demonstrated an insignificant effect

on users direct response (at the 5% level), a point of interest to media

agencies since animated banners are more costly to produce. Therefore,

Baltass (2003) contention that a wordy animated message increases the

complexity of the ad, and hence receives a negative response from the

audience, receives only partial support. Moreover, the discovery that ban-ners containing no promotional offer attract more attention than those with

such incentives, concurs with past studies (Chtourou et al. 2002; Baltas

2003; Rettie et al. 2004).

The traditional attention-capturing characteristics of banner ads (such

as action phrases and company brand/logo) play an insignificant role in

eliciting a direct response. These findings are consistent with previous

studies on the response to branded vs non-branded banners (Baltas 2003;

Chandon et al. 2003). While some commentators have suggested that


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unbranded banners stimulate greater curiosity, hence generating more

clicks, others have argued that unbranded banners contradict the concept

of brand building through banner exposure (Briggs & Hollis 1997). The

evidence from the current study indicates that the presence of games in

a banner ad positively impacts on CTR. Clearly, online gamblers find this

aspect of the communication important to their decision making, and thus

banner ads for the online gaming sector should comprise casino games in

order to increase direct response.

The findings of this study have strong implications for online commu-

nications; the evidence stresses the importance of fresh and innovative

message tactics, given the ineffectiveness of the more conventional design

tools employed to stimulate user interest. These results also suggest that

the effectiveness of banner ad campaigns for online gaming (as measuredby CTR) is governed by the same principles of artistic execution as those

found in other, non-gambling-related sectors of the internet.

This study used CTR to evaluate the effectiveness of banner ads. This

metric is one of the oldest methods of evaluating the success of online

advertising campaigns and is an appropriate tool for assessing direct mar-

keting objectives. However, it has been criticised for not including the

additional effects of online advertising such as branding (Briggs 2001b)

and may be considered by some as a limitation of this study. Furtherresearch could investigate the branding-building effects of banner ads.

Another limitation of the study is that it included a sample of banner ads

hosted on a single website, namely an online gaming portal. Future

research could extend the scope of the research to a number of websites.

Acknowledgements

The authors are grateful to participants at the 34th European MarketingAcademy Conference, Milan, the 2005 Academy of Marketing

Conference, Dublin, the editor and two anonymous referees for helpful

comments.

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About the authors

Helen Robinson is a principal lecturer in the School of Marketing at

Kingston University, UK. Previously, she worked in the advertising indus-

try, for JWT in London. Her current research interests include advertisingand media planning, marketing communications and internet marketing.

Her work has been published in a number of marketing journals.

Anna Wysocka graduated from Kingston Business School with a mas-

ters degree in marketing, moving on to follow a career as a practitioner in

the field.

Chris Hand is a post-doctoral researcher in the School of Marketing,

Kingston Business School. His research interests include the effects of

eCommerce on consumer behaviour, the entertainment industries and the


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application of quantitative methods in marketing. His research has

appeared in journals such as theJournal of Brand and Product Management,

Environment and Planning A and theJournal of Cultural Economics.

Address correspondence to: Helen Robinson, Kingston Business

School, Kingston Hill, Kingston upon Thames, Surrey, KT2 7LB, UK.

Email: [email protected]


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