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 (lee@hec.fr). 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) (mkbriley@ust.hk). 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. 1
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 2
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 3
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 4
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 5
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 6
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 7
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. 8
[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, exp(β 0 + β 1 * ExposureFrequency + β 2 *Age + β 3 *Gender) Prob(MessageRecall) =. 1+ exp(β + β * ExposureFrequency + β *Age + β *Gender) 0 1 2 3 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. 9
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] 10
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] 11
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. 12
[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 13
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 14
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 7 th exposure with all campaigns. 15
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 16
and the optimum duration of campaign which can maximize the objective performance by minimizing the budget. 17
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TABLES AND FIGURES Table 1: Campaign Descriptive Statistics Observations Duration Awareness Country Group Total Exposed Control (days) (control gr) Average exposures Message Recall Aided Awareness Favorability Purchase Intent 1 US 606 306 300 22 96,7% 1,41 -- -- 2 US 1549 1201 348 44 23,6% 7,82 + -- -- 3 US 791 479 312 4 25,0% 1,93 4 US 857 401 456 35 95,2% 11,86 5 US 750 450 300 27 21,0% 2,70 + N/A 6 UK G1 757 375 382 6 99,7% 4,18 7 FR G1 751 376 375 4 96,3% 4,95 + 8 DE G1 751 376 375 5 94,4% 8,93 -- 9 I G1 619 300 319 12 98,1% 6,01 -- -- 10 ES G1 733 375 358 15 95,8% 4,14 ++ 11 NL G1 751 376 375 13 99,2% 2,51 ++ -- 12 SE G1 722 347 375 19 98,4% 3,83 ++ 13 H G1 751 373 378 11 99,2% 2,43 ++ 14 PL G1 703 328 375 12 97,1% 2,32 15 ES 934 299 635 17 3,96 N/A ++ N/A N/A 16 US G2 805 400 405 49 2,40 N/A ++ 17 DN G2 802 401 401 84 6,38 N/A + ++ 18 NL G2 803 403 400 88 1,37 N/A 19 DE G2 801 401 400 52 1,54 N/A ++ 20 NO G2 800 400 400 86 2,71 N/A ++ 21 SE G2 800 400 400 56 4,05 N/A ++ + 22 FN G2 801 400 401 85 15,63 N/A ++ + 23 UK G3 604 301 303 20 96,7% 3,61 ++ ++ 24 FR G3 603 300 303 18 80,9% 2,58 25 DE G3 799 400 399 15 93,7% 4,56 + ++ 26 I G3 605 302 303 16 97,0% 5,99 27 ES G3 602 300 302 19 92,1% 5,24-28 SE G3 757 400 357 18 98,9% 15,35 ++ 29 NL G3 812 410 402 16 93,8% 1,92 ++ + 30 US 938 513 425 1 81,6% 4,29 31 US 759 382 377 67 63,9% 11,63 32 US 716 401 315 25 34,3% 1,87 + ++ 33 US 626 400 226 9 25,7% 2,40 + ++ 34 US 800 400 400 13 99,8% 9,08 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 Coefficient Standard Errors Wald D.F. p-value. Exposure 0,001 0,001 1,292 1 0,256 Age -0,022 0,001 237,012 1 0,000 Women (1) 0,314 0,033 92,033 1 0,000 Campaign 1449,396 32 0,000 Campaign 1 1,462 0,161 82,041 1 0,000 Campaign 2 0,506 0,150 11,358 1 0,001 Campaign 3 0,125 0,172 0,526 1 0,468 Campaign 4 1,141 0,156 53,461 1 0,000 Campaign 5 1,314 0,157 69,925 1 0,000 Campaign 6 0,991 0,157 39,775 1 0,000 Campaign 7 0,475 0,163 8,448 1 0,004 Campaign 8 0,907 0,158 32,918 1 0,000 Campaign 9 0,748 0,165 20,604 1 0,000 Campaign 10 1,059 0,158 45,184 1 0,000 Campaign 11 0,930 0,158 34,547 1 0,000 Campaign 12 0,807 0,161 25,038 1 0,000 Campaign 13 0,900 0,158 32,387 1 0,000 Campaign 14 1,535 0,156 97,315 1 0,000 Campaign 15 2,775 0,152 332,754 1 0,000 Campaign 16 1,481 0,153 93,691 1 0,000 Campaign 17 1,356 0,154 77,828 1 0,000 Campaign 18 1,370 0,154 79,203 1 0,000 Campaign 19 1,341 0,154 76,095 1 0,000 Campaign 20 1,218 0,154 62,204 1 0,000 Campaign 21 1,558 0,153 104,095 1 0,000 Campaign 22 1,766 0,153 133,984 1 0,000 Campaign 23 0,415 0,173 5,756 1 0,016 Campaign 24 0,779 0,165 22,197 1 0,000 Campaign 25-0,001 0,173 0,000 1 0,995 Campaign 26 1,094 0,162 45,825 1 0,000 Campaign 27 0,941 0,163 33,192 1 0,000 Campaign 28 1,081 0,157 47,290 1 0,000 Campaign 29 0,934 0,157 35,552 1 0,000 Campaign 30 0,188 0,165 1,291 1 0,256 Campaign 31 0,286 0,170 2,819 1 0,093 Campaign 32 0,453 0,170 7,135 1 0,008 Constant -1,188 0,146 65,985 1 0,000 21
Table 3: Simple Repeat Exposure Effect with Scale Modification Coefficient Standard Errors Wald p-value. Log Likelihood Model 1-13959,33 Exposure 0,001 0,001 1,292 0,256 Age -0,022 0,001 237,012 0,000 Women (1) 0,314 0,033 92,033 0,000 Constant -1,188 0,146 65,985 0,000 Model 2-13954,23 Log (Exposure+1) 0,032 0,011 8,951 0,003 Age -0,022 0,001 238,028 0,000 Women (1) 0,307 0,033 87,545 0,000 Constant -1,214 0,147 68,671 0,000 Model 3-13955,55 Square root (Exposure) 0,060 0,018 11,565 0,001 Age -0,022 0,001 238,314 0,000 Women (1) 0,305 0,033 86,163 0,000 Constant -1,226 0,147 69,830 0,000 Table 4: Simple Repeat Exposure Effect (13 successful campaigns only) Coefficient Standard Errors Wald D.F. p-value. Log (Exposure+1) 0,140 0,027 27,603 1 0,000 Age -0,024 0,002 110,381 1 0,000 Women (1) 0,417 0,051 67,394 1 0,000 Campaign 859,021 12 0,000 Campaign 10 1,066 0,160 44,609 1 0,000 Campaign 11 0,949 0,160 35,109 1 0,000 Campaign 12 0,826 0,162 25,882 1 0,000 Campaign 13 0,903 0,160 31,775 1 0,000 Campaign 15 2,789 0,153 330,447 1 0,000 Campaign 17 1,355 0,155 76,247 1 0,000 Campaign 21 1,565 0,154 103,647 1 0,000 Campaign 22 1,736 0,154 126,482 1 0,000 Campaign 23 0,426 0,174 5,972 1 0,015 Campaign 25 0,007 0,175 0,002 1 0,967 Campaign 28 1,062 0,158 44,922 1 0,000 Campaign 29 0,958 0,159 36,464 1 0,000 Constant -1,174 0,165 50,615 1 0,000 22
Table 5: Cumulative Lift of Repeat Exposure (All vs. Successful Campaigns) Exposure All Campaigns Successful Campaigns 1 3,5% 8,3% 2 5,6% 13,4% 3 7,2% 17,1% 4 8,3% 20,1% 5 9,3% 22,5% 6 10,2% 24,6% 7 10,9% 26,5% 8 11,5% 28,1% 9 12,1% 29,6% 10 12,6% 30,9% Table 6: Wearout Effect Coefficient Standard Errors Wald p-value. Log Likelihood All Campains -13947,83 Log (Exposure+1) 0,185 0,039 22,052 0,000 [Log (Exposure+1)]2-0,045 0,013 12,333 0,000 Age -0,022 0,001 240,223 0,000 Women 0,301 0,033 84,143 0,000 Constant -1,270 0,147 74,347 0,000 Successful Campaigns Only -5457,72 Log (Exposure+1) 0,407 0,061 44,131 0,000 [Log (Exposure+1)]2-0,091 0,019 22,730 0,000 Age -0,024 0,002 112,751 0,000 Women 0,409 0,051 64,590 0,000 Constant -1,273 0,167 58,428 0,000 23
Figure 1: Pattern of Repeat Exposure 90% 80% 70% Campaign 18 Campaign 17 Campaign 22 All Campaigns 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 2: Repeat Exposure Effect on Message Recall 20% 20% 19% Exposure Square root (Exposure) Log (Exposure+1) Message Recall Rate 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% 25% All Campaigns Successful Campaigns Message Recall Rate 23% 21% 19% 17% 15% 0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930 Number of Exposures Figure 4: Wearout Effect 20% 19% [Log (Exposure+1)]^2 Log (Exposure+1) Message Recall Rate 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% 24% 23% All Campaigns Successful Campaigns Only Message Recall Rate 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 I'm not sure I Have Not Heard of Sony ( ) ( ) ( ) Nokia ( ) ( ) ( ) Philips ( ) ( ) ( ) Samsung ( ) ( ) ( ) LG Electronics ( ) ( ) ( ) Brand opinion (favorability) question How would you describe your overall opinion about each of the following brands of consumer electronics? Very Favorable (5) Somewhat Favorable (4) Neutral (3) Somewhat Unfavorable (2) Very Unfavorable (1) Sony ( ) ( ) ( ) ( ) ( ) Nokia ( ) ( ) ( ) ( ) ( ) Philips ( ) ( ) ( ) ( ) ( ) Samsung ( ) ( ) ( ) ( ) ( ) LG Electronics ( ) ( ) ( ) ( ) ( ) Purchase intent question How likely are you to purchase each of the following brands of consumer electronics in the future? Very Likely (5) Somewhat Likely (4) Neutral (3) Somewhat Unlikely (2) Very Unlikely (1) Sony ( ) ( ) ( ) ( ) ( ) Nokia ( ) ( ) ( ) ( ) ( ) Philips ( ) ( ) ( ) ( ) ( ) Samsung ( ) ( ) ( ) ( ) ( ) LG Electronics ( ) ( ) ( ) ( ) ( ) 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