Download repeat exposure effects of internet advertising

REPEAT EXPOSURE EFFECTS OF INTERNET ADVERTISING
Janghyuk Lee
HEC School of Management, Paris
and
Donnel A. Briley*
Hong Kong University of Science and Technology
* Janghyuk Lee is an Assistant Professor at HEC School of Management, Paris, 1 rue de la
Libération, 78351 Jouy-en-Josas, France ([email protected]). Donnel A. Briely is an Asssitant
Professor at the School of Business and Management, Hong Kong University of Science and
Technology, Clear Water Bay, Kwoloon, Hong Kong (SAR) ([email protected]).
The authors would like to thank Nick Nyhan, CEO, DynamicLogic, for providing data.
REPEAT EXPOSURE EFFECTS OF INTERNET ADVERTISING
ABSTRACT
In this paper, we exposure the repeat exposure effect of Internet advertising. By using
a field data set of 34 advertising campaigns, we analyze functional forms of the repeat
exposure effect of Internet advertising. Among four ad effectiveness measures including
aided brand awareness, message recall, brand opinion (favorability), and purchase intent, only
message recall shows substantial differences between control and exposures groups. Two
patterns of repeating exposure effect on message recall are found: the one in monotonically
increasing with a decreasing rate and the other in a quadratic form of inverted 'U'-shape with
'wearout' effect.
Key Words: Internet, Advertising, Repeat exposure, Message recall
Repeat exposure is one of the key phenomena in advertising as the most of consumers
have a chance to be exposed more than once. It has attracted the attention of many
researchers to assess the return on investment of advertising that turns out to be diminishing.
The nature of advertising whose performance is not increasing linearly to the number
exposure frequency combined with the pricing practice of advertising whose rate is linear to
the number of exposure generates the diminishing return on investment of advertising where
the necessity arises to analyze the repeat exposure phenomenon to enhance the efficiency of
advertising campaign. Especially on the Internet, repeat exposure phenomenon requires
particular attention as there is no upper limit of repeat exposures to a posted advertising and
the number of repeat exposures can be easily counted. Unlike TV and radio on which the
maximum number of repeat exposures is limited by the number of insertions, on the Internet,
one can be exposed as many times as possible and the exact number of his/her repeat
exposures can be tracked unlike magazine in which tracking the number of exposures is
unrealistic. In this paper, we explore possible functional forms of repeat exposure effect
based on field study data.
RESEARCH ON ADVERTISING REPETITION
The main question of advertising repetition is whether repeat exposure to an ad can
continue to provide positive effects on consumers and if not what the effective exposure
frequency would be. An ad is said to have worn in from a particular level of repetition, for
instance, the first exposure, if it has a significant positive effect and it has worn out at a
particular level of repetition if it has either any significant positive effect or a significant
negative effect. Although 'wearin' and 'wearout' happen sequentially in advertising repetition,
they are distinct phenomena per se. This complex process embedded in advertising repetition
may have forced many academics and practitioners who have provided results with a certain
degree of ambiguity to interpret their findings.
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According to Pechmann and Stewart (1988), this ambiguity springs up mainly from
fundamental differences among studies in terms of the methodologies and measures that are
used. Two distinct types of research, laboratory and field studies, have been conducted with
varying dependent variables measuring the ad effect: attention, immediate recall, delayed
recall, cognitive responses, immediate brand attitudes, delayed brand attitudes or sales. In
laboratory studies, research participants were required to view ads or other types of persuasive
messages. Repeated exposures were massed in that they occurred within the course of a few
minutes, or an hour. A written questionnaire was used to measure the effectiveness of the
messages immediately after exposure. In field studies: research participants were not required
to pay attention to the test ads. Repeated exposures to the ads are distributed over the course
of several days or weeks rather than massed. The dependent variables typically were
measured after a delay rather than immediately after exposure. Following models explain the
attitude modification process of two different types of studies.
Laboratory Studies: The Two-Stage Cognitive Response Model
This model has its origin in the 'two-factor' theory of Berlyne (1970) who explains that
the impact of exposure frequency is mediated by two factors: habituation (learning) and
tedium. Habituation can improve an ad’s effectiveness, whereas tedium deteriorates it. If the
tedium factor overwhelms its counterpart after the number of exposures passes a threshold,
repeat exposures may take the form of inverted-U curves, in which two opposing
psychological processes operate simultaneously: positive habituation and negative tedium.
Based on 'two-factor' theory, this model incorporates the change of people's thoughts: positive
and negative ones and explains the ad performance in two stages of 'wearin' and 'wearout'.
The first stage 'wearin' occurs during approximately the first three exposures as
consumers generate counter-arguments at the first exposure. They either do not fully
appreciate the ad's message (Cacioppo and Petty 1979) or take defensive position vis-a-vis the
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ad (Sawyer 1981). Therefore negative thoughts dominate positive ones that overtake negative
ones as the number of exposures increase up to three. When consumers are exposed to the
same ad three times in a row, they have more time to reflect upon its logic and merits then
start to generate support arguments (Cacioppo and Petty 1979) and their distrust may dissipate
(Sawyer 1981). The second stage 'wearout' starts approximately at the fourth exposure as
consumers start to get bored and irritated with the ad message because it becomes tedious
(Schumann, Petty and Clemons 1990). Consumers generate negative thoughts as the own
thoughts that are less positive than message related one overwhelm (Belch 1982) and these
negative thoughts undermine the ad's persuasive impact. In this process three major
performance indicators show different trajectories. Recall of brand related information
increases monotonically until fifth or six exposures then gets plateau. Cognitive responses
such as brand attitudes and purchase intent show the inverted 'U'-shape as they reach the peak
at the third exposure when positive thoughts dominate negative ones then decline.
Field Studies: The Two-Stage Learning Model
In field studies, the horizon of dependent variables is extended to cover the ad effect
on sales later it is developed to advertising/sales response function. As exposures are
voluntary and distributed instead of required and massed, the learning of positive information
about the advertised brand progress slowly and the measurement after a delay captures the
longitudinal effect of repeated advertising better than that of immediately after exposure in
laboratory studies does.
The first stage 'wearin' starts slower than laboratory studies because consumers do not
pay full attention to ads in natural environment (Greenberg and Suttoni 1973). Consumers
monitor ads and pay attention or withdraw their attention sporadically depending on their
motivation and the level of distraction of external environment (Wright 1981). Unless an ad
is avoided by consumers, it is beneficial to insure exposures repeatedly as repetition increases
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the likelihood that consumers will appreciate the message (Cacioppo and Petty 1979) and
their defensiveness will dissipate (Sawyer 1981). Although the attitude towards the ad
deteriorate or wear out even during the first stage according to the cognitive response model,
positive attitudes towards the brand are retained and thereby have a much greater influence on
purchase decisions, which is called as 'sleeper effect' (Mazursky and Schul 1988, Ronis 1980,
Ray and Sawyer 1971).
The second stage starts as consumers no longer pay attention to the ad and they
generate fewer message-relevant thoughts or cognitive responses due to irritation and/or
satiation (Calder and Sternthal 1980). Recall of brand related information increases
monotonically but the brand sales can not increase indefinitely because the longer the ad runs,
the fewer consumers remain to be reached and persuaded (Blair 1987). In general the second
stage shows two patterns. As advertising is repeated, brand sales improve at a decreasing rate
and finally reach an asymptotic level without a decline as repetition reinforces existing habits
(Ehrenberg 1983). On the other hand, brand sales get plateau at a point which is lower than
peak of brand sales but higher than brand sales would have been without continued
advertising (Little 1979). According to Little (1979), sales increase may not be fully
maintained because only a fraction of those who try a brand may end up becoming repeat
purchasers and competing firms are likely to retaliate in order to win back sales.
Repeat Exposure on the Internet
On the Internet, Drèze and Hussherr (2003) find first significant banner exposure
effects on brand awareness, unaided and aided advertising recall, and aided brand recognition
in a study where a sample of 807 respondents who are surveyed both before and after their
exposure with a 24 hour interval to the ads of ten brands. The effect of repetition is found for
unaided and aided advertising recall and aided brand recognition by using a simple logit
model with a dummy variable indicating one or two exposures. However, the inversed effect
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is found in the case of aided advertising recall where the second exposure decreases the level
of aided ad recall. As for brand awareness, the repeat exposure effect is significant with a
continuous variable for the exposure frequency. Despite useful findings, this study has a
limitation to fully assess the form of repeat exposure effect as the maximum number of
exposures is limited to nine due to the study design. Chatterjee, Hoffman and Novak (2003)
explore direct behavioral effects of repeat exposure on the Internet by using clickstream data
which track browsing behavior of site visitors. The negative and nonlinear effect of repeated
exposures on click-through rate is found as visitors are exposed repeatedly to banner ads. The
click-through rate decreases from the first exposure without having the increasing 'wearin'
effect.
METHODOLOGY
Data
We use data collected by Dynamic Logic, New York based marketing research
company, by using a patent-pending AdIndex® methodology. Our data set contains 26,258
subjects from 34 campaigns conducted in the U.S. and Europe for companies in consumer
electronics industry. 41.7% of respondents are female and the respondents are 32.8 years old
on average. Tables 1 shows major descriptive statistics of 34 campaigns which were executed
between March 2001 and December 2002. Some of campaigns with the grouping sign (G1,
G2, and G3) were conducted for the same company with similar creative in various countries.
The duration of campaign varies from 1 to 88 days with the average of 29 days. The most of
brands record a high level of aided awareness and only four brands had the level below 35%.
AdIndex® is run in conjunction with an online ad campaign and is designed to provide
traditional brand metrics to gauge the impact of online advertising campaign. Our data are
collected through three stages: tagging of banner ad, online recruiting of subjects and online
surveying in two groups of control and exposed. At the first stage of tagging, the advertiser
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informs Dynamic Logic the Web site(s) where it placed its banner ads. An HTML code is
implemented to the pages of the Web site for the site visitors to be identified by tagging.
Every time a website visitor fully loads the pages where the banner ad is posted, an exposure
is recorded in the AdIndex® exposure database during the campaign. The second stage of
recruiting starts while the campaign is running. Two groups of visitors are sampled over the
same period of time and from the same Web site(s). Visitors of the Web site(s) are randomly
intercepted to take a brief online survey and, upon their consent to participate in a prize of
$150 or an equivalent amount of local currency, are asked a series of questions designed to
measure the ad effectiveness. At the last stage, intercepted visitors may answer a
questionnaire including questions about the subject's profile and ad effectiveness related
questions. The response rate is not reported. A sample of questions asked in the survey is
presented in annex.
AdIndex® methodology provides at two advantages to minimize the sampling errors
compared to the pre/post measurement methodology often used in advertising research (e.g.,
Drèze and Hussherr 2003). It can intercepts relatively homogeneous visitors to control and
exposed groups as both of them are visitors of the same site. The only difference is that the
subjects in control group are not exposed to banner ads posted in specific pages of the site.
As for the timing of survey, AdIndex® offers concurrent survey timing for control and
exposed groups in stead of two different points in time – typically before the campaign starts
and again after it is completed.
Ad Effectiveness Measures
Four main questions were asked to measure the effectiveness of ads in the order of
aided brand awareness, brand attitude, purchase intent, and message recall. For each
question, five companies in the same industry (direct competitors in most cases) are listed
either for selection (e.g., message recall) or for assessing cognitive responses. The mean of
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these measures are compared by t-test with independent sample. The difference is noted with
the number of positive or negative signs. The sign of double positive (negative) '+ +' ('- -')
stands for the mean of exposed group is larger (smaller) than that of control group at α=5%.
The sign of single positive (negative) '+' ('-') does at α=10%.
As for the question of aided brand awareness, three possible answers are listed: 'I have
heard of', 'I'm not sure', and 'I have not heard of'. As the second answer contains a high level
of uncertainty about brand awareness, we consider only the first answer to be effective for
aided brand awareness question. In Table 1, the level of aided brand awareness among
subjects of control group (no exposure) is reported in the column of 'awareness (control gr)'.
The most of brands tested in 34 campaigns showed a high level exceeding 80% except four
brands whose level of awareness was less than 35%. Due to the high level of brand
awareness, its repeat effect is revealed not to be substantial enough. Only 6 out of 26
campaigns (except 8 campaigns in which brand awareness measure is not available) turn out
to have the statistically significant difference between control and exposed groups.
[Insert Table1: Campaign Descriptive Statistics about here]
Favorable attitude towards brands and purchase intent are measured in Likert 5-point
scale which are considered as metric scale in our analysis varying from 1 to 5. Surprisingly
some campaigns show negative effects and the positive effect is almost insignificant across
campaigns. Only 3 campaigns for favorable brand attitude and 6 campaigns for purchase
intent record a significant improvement at the exposed group. To verify whether this
insignificant effect is due to the deterioration after the peak (Cacioppo and Petty 1979, Calder
and Sternthal 1980, Ronis 1980), the same mean comparison is conducted by sampling only
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subjects with the maximum of three exposures, we obtain almost the same result except
campaign 29 that records minor improvement.
The survey question about 'message recall' is organized by proposing the ad message
then asking the subject to choose the right brand or no right response option. In terms of
effectiveness, 'message recall' show the most significant and substantial difference across
campaign by recording 13 campaigns with significant improvement out of 33 ones. We look
for possible causes such as the level of aided brand awareness or the average number of
exposures for the variation of the effectiveness of 'message recall'. But no difference of these
two potential causes is found among successful 13 campaigns and the rest of them in terms of
'message recall'.
Independent Variables
We focus mainly the impact of ad exposure on effectiveness measures. The average
number of exposures among subjects of the exposed group varies heavily from 1.37 to 15.63
times with the average of 5.05 times. The pattern of repeat exposures varies substantially
even among campaigns organized by the same company by using similar creative. Figure 1
illustrates the proportion of visitors of repeat exposures. These three campaigns conducted in
different countries but similar banners were used (only with some minor modifications for the
local market) during the period of similar length (campaign 18 for 88 days, campaign 17 for
84 days and campaign 85 days. Consecutively each campaign recorded 1.37, 6.38, and 15.63
average exposures with different patterns. In campaign 18, the most exposures (97.5%) were
concentrated in the range of 1 to 3 exposures. It dissipates gradually to 56.6% for campaign
17 and 46.1% to campaign 22 as the average number of exposures increases. In total, 72.7%
of exposures were in the range of 1 to 3. Figure 1 represents well the varying nature of repeat
exposure in the real world of advertising.
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[Insert Figure 1: Pattern of Repeat Exposure about here]
In addition, the time lag between the moment of last seen of the ad and the timing of
survey is computed in the variable 'time difference'. This is measured by system generated
variables which captures the exact time of last seen and that of survey. The average time lag
is around 16 hours but the most of survey was conducted right after the last exposure. 80% of
survey was conducted less than 21 minutes after the last exposure. This time lag variable is
applied for assessing the forgetting effect of advertising.
Model
A typical binary logistic regression is applied with a dependent variable of message
recall probability, Prob(MessageRecall) =
exp(β 0 + β 1 * ExposureFrequency + β 2 * Age + β 3 * Gender)
.
1 + exp(β 0 + β 1 * ExposureFrequency + β 2 * Age + β 3 * Gender)
Exposure frequency is used as the main independent variable. Age and gender are included as
covariates. To assess 'wearout' effect, a squared term of exposure frequency will be added
and for forgetting effect, a variable measuring the time lag between the moment of last seen
and survey timing. The likelihood principle is applied to find a set of coefficients that can
maximize the likelihood of the model.
RESULTS
Out of four measures of ad effectiveness, only 'message recall' rate turns out to have
substantial differences between control and exposed groups across 34 campaign data.
Therefore we focus mainly on the pattern of repeat exposure effect on 'message recall' rate
which is coded in two values: subjects who recalled the message and chose the right brand
(coded as '1' in the logistic regression) and others who opted a wrong one or the answer 'none
of above brands' (coded as '0'). As the dependent variable is binary, we use the binary logistic
regression to assess the impact of the exposure frequency with age and gender as covariates.
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We proceed the analysis by assessing the monotonically increasing simple effect of repeat
exposure with scale transformation then assess a quadratic from to check the existence of
'wearout' effect. Dummy variables are used to represent the message recall rate of the control
group across 33 campaigns (except campaign 34 in which 'message recall' was not measured).
In all of following analyses, two covariates, age and gender, are found significant. The
younger the subject, the better to recall the ad message and women turn out to recall better the
ad message than men as the number of exposures increases.
Repeat Exposure Effect
Based on the result of previous findings (Drèze and Hushherr 2003) in which a
significant repeat exposure effect is reported on brand awareness, unaided advertising recall
and aided brand recognition, we proceed the logistic regression of 'exposure frequency' on
'message recall' rate with dummy variables for each campaign and two covariates of age and
gender.
Our first finding is that there is no significant repeat exposure effect on message recall.
The sign (0.0012) of the variable 'exposure frequency' is right but its p-value (0.256) is not
small enough to justify the existence of repeat exposure effect. The result of the logistic
regression of 'exposure frequency' on 'message recall' is presented in Table 2. Dummy
variables represent the difference of constant compare to that of the whole model representing
the constant of campaign 33 which is served as the baseline. Campaign 3, 25, 30 and 31 share
the constant with campaign 33 at α=5%. All the rests have their own constant different from
that of campaign 33 used as the baseline. 'Age' turns out to have a negative effect on message
recall and women excel in message recall compared to men.
[Insert Table 2: Simple Repeat Exposure Effect about here]
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As the reason of no significant repeat exposure effect may be due to the scale of
independent variable, 'exposure frequency', we proceed the same logistic regression by
modifying the scale of 'exposure frequency' variable. It is the first time to proceed the scale
transformation of 'exposure frequency' variable as we look for a functional form of repeat
exposure effect. We think that no previous research has pursued the scale transformation of
exposure frequency because of two possible reasons. First, previous studies do not intend to
find a functional form of repeat exposure and second, most of previous studies do not have
enough data points to conduct the scale transformation. As our measure of 'exposure
frequency' variable has continuous data points in enough number, it is plausible for us to
pursue the scale modification.
Two different functions are applied: a log and a square root of exposure frequency.
For the log transformation, we add 1 to exposure frequency that has many data point of zero
exposure. Taking a log after adding 1 to exposure frequency allows us to have log of zero
whose value is negative infinite. The scale transformation allows us to look for the repeat
exposure effect that could be in a different form rather than a linear relationship through logit
transformation of exposure frequency. With the scale transformation, we find the significant
repeat exposure effect in both cases. Table 3 explains the significant repeat exposure effect
after both cases of scale transformation. The effect is more significant in the case of square
root transformation with a smaller p-value than that of log transformation. However the
model with log transformation (LL = -13954.23) provides a little bit better fit than that with
square root transformation (LL = -13955.55) in terms of log-likelihood at the model level. As
it provides a better fit even it's minor, the model with log transformation will be used in
following analyses.
[Insert Table 3: Simple Repeat Exposure Effect with Scale Transformation about here]
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As for the form, various patterns of repeat exposure can be expected technically as it is
the matter of the value of the slope and the constant for the logistic regression. In the case of
sales response function, the S-shape repeat exposure function is advocated by Little (1979),
Rao and Miller (1975). On the other hand, Krishnamurthi, Narayan, and Raj (1986) propose a
concave one. In our case, both models show a monotonically increasing pattern with a
decreasing rate which is in a concave form without having the phase of accelerating with an
inflection point like the S-shape curve. Figure 2 illustrates the pattern of repeat exposure
effect on message recall rate of both models. In this graph, we present the range of exposure
frequency up to 30 that covers 98.6% of total observations. The model with log
transformation projects with a steeper slope than that with square root transformation.
[Insert Figure 2: Repeat Exposure Effect on Message Recall about here]
In above analysis, we pool observations from all campaigns regardless of their
effectiveness between control and exposed group. As we noted earlier, there are 13
campaigns in which the exposed group performs statistically better than the control one does.
In these successful campaigns, the repeat exposure effect is amplified with substantial
differences compared to that of all campaigns in Table 4 and in Figure 3. To measure the lift
of message recall rate of repeat exposure, one can compute the proportion of improved
message recall rate (MRR) compared to that of baseline rate; (MRR at n exposures – MRR of
zero exposure) / MRR of zero exposure. For a campaign having the baseline message recall
rate is 16.4% (c.f., campaign 33), the cumulative improvement of repeat exposure can be
more than double if the campaign is successful in Table 5.
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[Insert Figure 3: Repeat Exposure Effect (All vs. Successful campaigns) about here]
[Insert Table 4: Simple Repeat Exposure Effect (13 successful campaigns only) about
here]
[Insert Table5: Cumulative Repeat Exposure Effect (All vs. Successful Campaigns)
about here]
Repeat Exposure Effect with Wearout
Cognitive response model and learning model presented in an earlier section explain
the repeat exposure effect in two phases: 'wearin' and 'wearout'. But the binary logistic
regression applied to assess the simple repeat exposure effect does not allow to capture the
'wearout' phases as the function has to increase monotonically. Therefore to capture the
functional form of the 'wearout' phase, we add a squared term of 'exposure frequency' variable
to the previous model. The addition of the squared term allows the model to be flexible
enough to capture various patterns of repeat exposure effects with or without inverted 'U'shaped effect. If there is no 'wearout', the coefficient of this squared term is expected to be
not significant. Then it becomes exactly the same model of the previous section. If 'wearout'
does exist, this coefficient can be negative and that of the normal term being positive which
allows to have the inverted 'U'-shaped form reported in laboratory studies with a limited
number of exposure frequency data points. Cacioppo and Petty (1979) show this inverted-U
shaped form with only three data points of exposure frequency (1, 3, and 5).
We proceed the same binary logistic regression with exposure frequency transformed
in a log form by adding one more independent variable with the value having the square of the
log of exposure frequency plus 1, [log (exsposure+1)]2. The introduction of the squared term
improves the model fit by increasing the log-likelihood that increases from –13954.23 to –
13947.83. The log-likelihood ratio test confirms its significant contribution as the difference
between the model with and without the quadratic term multiplied by 2 is 12.81 that is largely
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greater than the threshold, 6.63, of chi-square test with 1 degree of freedom at α=1%. The
results with all campaigns in Table 6 show the inverted 'U'-shaped form of repeat exposure
effect in Figure 4 with the peak of message recall reached at around 7 exposures (more
precisely 6.96). Compared to the results of the model with no squared term, the message
recall rate increases in a faster rhythm until the exposure frequency reaches the peak at around
7 then it deteriorates gradually. In our results, 'wearin' starts from the first exposure and
'wearout' starts around the peak of seven exposures. As our quadratic form does not allow to
have a plateau or a rebound, the message recall rate goes down even to a rate lower than that
of control group with no exposure. In this campaign 33, the message recall rate gets lower
than that of no exposure as the number of exposure passes around 63.
[Insert Table 6: Wearout Effect about here]
[Insert Figure 4: Wearout Effect about here]
[Insert Figure 5: Repeat Exposure Effect with Wearout about here]
The 'wearout' effect is also found among successful campaigns in Table 6. All
coefficients have the same sign even though the magnitude of repeat exposure effect is
substantially different. As shown in Figure 5, the peak is reached between 8 and 9 exposures
(more precisely 8.37) in the case of successful case only, which extends a little bit the 'wearin'
phase. However it maintains a similar form of inverted 'U'-shape like the model with all
campaigns.
Forgetting Effect
As time passes after being exposed to an ad, the effect of advertising starts to
dissipate. Zielske (1959) reports the forgetting effect in a field experiment on newspaper ads.
Repeat exposure with a shorter interval (e.g., 1 week) showed a faster forgetting rate than that
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with longer interval (e.g., 4 weeks). Unfortunately no statistical test was conducted at that
time. Later more attention to paid to repeat exposure as a way to reduce the forgetting rate.
Johnson and Watkins (1971) emphasize the learning effect of repeat exposure to reduce the
likelihood of forgetting.
In our date set, the time lag between the moment of last exposure to the ad and the
timing of survey is computed automatically. We can see how long time has passed and assess
its impact on message recall rate. Despite its average of 16 hours, around 80% of time lag is
less than 21 minutes, which means the most of online survey was conducted right after the last
exposure. The result of binary logistics with all possible combination of exposure frequency
and the choice of campaign set turns out to have no significant of time lag on the rate of
message recall.
CONCLUSION
In this research, we focus mainly on finding a generic functional form of repeat
exposure effect of Internet advertising. By using the binary logistic regression, we assess the
impact of exposure frequency on the probability of message recall. While Drèze and
Hussherr (2003) report the impact of repeat exposure on brand awareness from a data set
having 10 data points of exposure frequency, we are not able to find the similar impact on
message recall. Therefore a scale transformation taking the log value of exposure frequency
is proceeded. With the log scale transformation, the repeat exposure effect is found in both
models with and without the squared term of exposure frequency. The first model without the
squared term shows a monotonically increasing effect of repeat exposure in a decreasing rate.
In this model, the message recall rate approaches 100% without 'wearout' effect as the
exposure frequency goes to the positive infinite. On the other hand, the second model with
the squared term explains the repeat exposure effect in two phases of 'wearin' and 'wearout' in
the inverted 'U'-shape. The peak is attained at around the 7th exposure with all campaigns.
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Even though the coefficients of two exposure frequency terms (ordinary and squared) have
changes, this inverted 'U'-shape remains in the same form in the case of successful campaigns
only. However this inverted 'U'-shaped form needs to be analyzed with precision as our data
have a high concentration in the range of low exposure frequency. One common result from
two models show the concave form of repeat exposure effect reported by Krishnamurthi,
Narayan, and Raj (1986) that accommodates the immediate 'wearin' effect from the first
exposure instead of having a gradually improving introduction phase reported in a 'S'-shape.
Our research faces following limitations. First, the nature of our data collect lacks
control both on the level of exposure to other media of the same campaign and on the banner
ads themselves. Our results are based on 34 different campaigns which were conducted under
very different circumstances. With only two covariates of age and gender, our analyses
would be exposed too much noises. Second, as we focus narrowly only on repeat exposure
effect, we do not handle other related effects such as spacing one (Janiszewski, Noel, and
Sawyer 2003). Lacking of handling space effect between exposures is mainly due to the
nature of our data which does not possess the detailed information about the timing of each
exposure. Instead it records only three timing related information: the first and last exposure
timing and that of survey. Third, we test the forgetting effect with the variable measuring the
time lag between the last exposure and the moment of survey. But this variable is highly
concentrated as the most of survey have been conducted immediately after the last exposure.
Therefore the forgetting effect is not able to be fully assessed in our research.
Despite above limitations, our results provide useful information to media planning on
the Internet. The generic functional form of repeat exposure effect that we have found in two
models provide the key information about diminishing returns of Internet advertising. Still
many Web sites practice ad pricing based on the number of exposure. With our results, the
advertiser can refine its return on investment of advertising spending by selecting Web sites
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and the optimum duration of campaign which can maximize the objective performance by
minimizing the budget.
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Marketing, Jan, 239-43.
19
TABLES AND FIGURES
Table 1: Campaign Descriptive Statistics
Observations
Country Group
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
US
US
US
US
US
UK
FR
DE
I
ES
NL
SE
H
PL
ES
US
DN
NL
DE
NO
SE
FN
UK
FR
DE
I
ES
SE
NL
US
US
US
US
US
G1
G1
G1
G1
G1
G1
G1
G1
G1
G2
G2
G2
G2
G2
G2
G2
G3
G3
G3
G3
G3
G3
G3
Duration Awareness Average
Total Exposed Control (days) (control gr) exposures
606
1549
791
857
750
757
751
751
619
733
751
722
751
703
934
805
802
803
801
800
800
801
604
603
799
605
602
757
812
938
759
716
626
800
306
1201
479
401
450
375
376
376
300
375
376
347
373
328
299
400
401
403
401
400
400
400
301
300
400
302
300
400
410
513
382
401
400
400
300
348
312
456
300
382
375
375
319
358
375
375
378
375
635
405
401
400
400
400
400
401
303
303
399
303
302
357
402
425
377
315
226
400
22
44
4
35
27
6
4
5
12
15
13
19
11
12
17
49
84
88
52
86
56
85
20
18
15
16
19
18
16
1
67
25
9
13
96,7%
23,6%
25,0%
95,2%
21,0%
99,7%
96,3%
94,4%
98,1%
95,8%
99,2%
98,4%
99,2%
97,1%
96,7%
80,9%
93,7%
97,0%
92,1%
98,9%
93,8%
81,6%
63,9%
34,3%
25,7%
99,8%
1,41
7,82
1,93
11,86
2,70
4,18
4,95
8,93
6,01
4,14
2,51
3,83
2,43
2,32
3,96
2,40
6,38
1,37
1,54
2,71
4,05
15,63
3,61
2,58
4,56
5,99
5,24
15,35
1,92
4,29
11,63
1,87
2,40
9,08
Aided
Message Favora- Purchase
AwareRecall
bility
Intent
ness
--+
---
+
N/A
+
--++
++
++
++
--
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
++
++
N/A
+
++
+
++
++
++
++
N/A
++
++
++
+
+
++
++
+
+
+
++
N/A
DE (Germany), DN (Denmark), ES (Spain), FN (Finland), FR (France), NL (The Netherlands), NO
(Norway), PL (Poland), SE (Sweden)
20
--
++
Table 2: Simple Repeat Exposure Effect
Exposure
Age
Women (1)
Campaign
Campaign 1
Campaign 2
Campaign 3
Campaign 4
Campaign 5
Campaign 6
Campaign 7
Campaign 8
Campaign 9
Campaign 10
Campaign 11
Campaign 12
Campaign 13
Campaign 14
Campaign 15
Campaign 16
Campaign 17
Campaign 18
Campaign 19
Campaign 20
Campaign 21
Campaign 22
Campaign 23
Campaign 24
Campaign 25
Campaign 26
Campaign 27
Campaign 28
Campaign 29
Campaign 30
Campaign 31
Campaign 32
Constant
Coefficient Standard Errors
0,001
0,001
-0,022
0,001
0,314
0,033
1,462
0,506
0,125
1,141
1,314
0,991
0,475
0,907
0,748
1,059
0,930
0,807
0,900
1,535
2,775
1,481
1,356
1,370
1,341
1,218
1,558
1,766
0,415
0,779
-0,001
1,094
0,941
1,081
0,934
0,188
0,286
0,453
-1,188
0,161
0,150
0,172
0,156
0,157
0,157
0,163
0,158
0,165
0,158
0,158
0,161
0,158
0,156
0,152
0,153
0,154
0,154
0,154
0,154
0,153
0,153
0,173
0,165
0,173
0,162
0,163
0,157
0,157
0,165
0,170
0,170
0,146
Wald
1,292
237,012
92,033
1449,396
82,041
11,358
0,526
53,461
69,925
39,775
8,448
32,918
20,604
45,184
34,547
25,038
32,387
97,315
332,754
93,691
77,828
79,203
76,095
62,204
104,095
133,984
5,756
22,197
0,000
45,825
33,192
47,290
35,552
1,291
2,819
7,135
65,985
21
D.F.
1
1
1
32
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
p-value.
0,256
0,000
0,000
0,000
0,000
0,001
0,468
0,000
0,000
0,000
0,004
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,016
0,000
0,995
0,000
0,000
0,000
0,000
0,256
0,093
0,008
0,000
Table 3: Simple Repeat Exposure Effect with Scale Modification
Coefficient Standard Errors
Wald
Model 1
Exposure
Age
Women (1)
Constant
0,001
-0,022
0,314
-1,188
0,001
0,001
0,033
0,146
1,292
237,012
92,033
65,985
Log (Exposure+1)
Age
Women (1)
Constant
0,032
-0,022
0,307
-1,214
0,011
0,001
0,033
0,147
8,951
238,028
87,545
68,671
Square root (Exposure)
Age
Women (1)
Constant
0,060
-0,022
0,305
-1,226
0,018
0,001
0,033
0,147
11,565
238,314
86,163
69,830
Model 2
Model 3
p-value. Log Likelihood
-13959,33
0,256
0,000
0,000
0,000
-13954,23
0,003
0,000
0,000
0,000
-13955,55
0,001
0,000
0,000
0,000
Table 4: Simple Repeat Exposure Effect (13 successful campaigns only)
Coefficient
Log (Exposure+1)
0,140
Age
-0,024
Women (1)
0,417
Campaign
Campaign 10
1,066
Campaign 11
0,949
Campaign 12
0,826
Campaign 13
0,903
Campaign 15
2,789
Campaign 17
1,355
Campaign 21
1,565
Campaign 22
1,736
Campaign 23
0,426
Campaign 25
0,007
Campaign 28
1,062
Campaign 29
0,958
Constant
-1,174
Standard
Errors
0,027
0,002
0,051
0,160
0,160
0,162
0,160
0,153
0,155
0,154
0,154
0,174
0,175
0,158
0,159
0,165
Wald
27,603
110,381
67,394
859,021
44,609
35,109
25,882
31,775
330,447
76,247
103,647
126,482
5,972
0,002
44,922
36,464
50,615
22
D.F.
1
1
1
12
1
1
1
1
1
1
1
1
1
1
1
1
1
p-value.
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,015
0,967
0,000
0,000
0,000
Table 5: Cumulative Lift of Repeat Exposure (All vs. Successful Campaigns)
Exposure
1
2
3
4
5
6
7
8
9
10
All
Successful
Campaigns Campaigns
3,5%
8,3%
5,6%
13,4%
7,2%
17,1%
8,3%
20,1%
9,3%
22,5%
10,2%
24,6%
10,9%
26,5%
11,5%
28,1%
12,1%
29,6%
12,6%
30,9%
Table 6: Wearout Effect
Coefficient
Standard
Errors
Wald
p-value.
0,185
-0,045
-0,022
0,301
-1,270
0,039
0,013
0,001
0,033
0,147
22,052
12,333
240,223
84,143
74,347
0,000
0,000
0,000
0,000
0,000
All Campains
Log (Exposure+1)
[Log (Exposure+1)]2
Age
Women
Constant
Successful Campaigns Only
Log (Exposure+1)
[Log (Exposure+1)]2
Age
Women
Constant
Log
Likelihood
-13947,83
-5457,72
0,407
-0,091
-0,024
0,409
-1,273
0,061
0,019
0,002
0,051
0,167
23
44,131
22,730
112,751
64,590
58,428
0,000
0,000
0,000
0,000
0,000
Figure 1: Pattern of Repeat Exposure
90%
Campaign 18
Campaign 17
Campaign 22
All Campaigns
80%
70%
60%
50%
40%
30%
20%
10%
0%
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19 20+
Number of Exposures
Figure
ure 2: Repeat Exposure Effect on Message Recall
20%
Exposure
Square root (Exposure)
Log (Exposure+1)
20%
Message Recall Rate
19%
19%
18%
18%
17%
17%
16%
16%
15%
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Number of Exposures
24
Figure 3: Repeat Exposure Effect (All vs. Successful Campaigns)
27%
All Campaigns
Successful Campaigns
Message Recall Rate
25%
23%
21%
19%
17%
15%
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Number of Exposures
Figure 4: Wearout Effect
20%
[Log (Exposure+1)]^2
Log (Exposure+1)
Message Recall Rate
19%
18%
17%
16%
15%
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Exposure Frequency
25
Figure 5: Repeat Exposure Effect with Wearout
25%
All Campaigns
24%
Successful Campaigns Only
Message Recall Rate
23%
22%
21%
20%
19%
18%
17%
16%
15%
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Exposure Frequency
26
Annex: Sample Questionnaire
Aided brand awareness question
Which of the following brands of consumer electronics have you heard of before?
I Have Heard of
()
()
()
()
()
Sony
Nokia
Philips
Samsung
LG Electronics
I'm not sure
()
()
()
()
()
I Have Not Heard of
()
()
()
()
()
Brand opinion (favorability) question
How would you describe your overall opinion about each of the following brands of
consumer electronics?
Sony
Nokia
Philips
Samsung
LG Electronics
Very
Favorable (5)
()
()
()
()
()
Somewhat
Favorable (4)
()
()
()
()
()
Neutral
(3)
()
()
()
()
()
Somewhat
Unfavorable (2)
()
()
()
()
()
Very
Unfavorable (1)
()
()
()
()
()
Purchase intent question
How likely are you to purchase each of the following brands of consumer electronics in the
future?
Sony
Nokia
Philips
Samsung
LG Electronics
Very
Likely (5)
()
()
()
()
()
Somewhat
Likely (4)
()
()
()
()
()
Neutral
(3)
()
()
()
()
()
Somewhat
Unlikely (2)
()
()
()
()
()
Very
Unlikely (1)
()
()
()
()
()
Message recall question
Which of the following companies, if any, uses these messages in its advertising?
DigitALLpassion
(1) Sony, (2) Nokia, (3) Philips, (4) Samsung, (5) LG Electronics, (6) None of the above
27