Journal of Marketing Thought Publication details, including instructions for authors and subscription information:

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1 On: 15 May 2015 Access details: Access Details: [Online: , Subscription: ] Publisher SS Foundation Online International Standard Serial Number: Editorial office: School of Business, Sungkyunkwan University, Jongno-gu, Seoul, South Korea Journal of Marketing Thought Publication details, including instructions for authors and subscription information: Ideal Frequency Exposure to Web Banner Advertising for Online Shopping Malls Yunyoung Hur a* a. Master in School of Business, Seoul University To cite this Article Yunyoung Hur (2015) Ideal Frequency Exposure to Web Banner Advertising for Online Shopping Malls, Journal of Marketing Thought, 2(1): To link to this Article: /jmt PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sublicensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

2 Journal of Marketing Thought, 2015 Vol. 2, No. 1, 52-58, DOI: /jmt Yunyoung Hur * Ideal Frequency Exposure to Web Banner Advertising for Online Shopping Malls Web banner advertising is a type of display advertising, which can reach a wide, yet targeted audience in the whole globe. However, despite the burdensome cost of displaying banner ads on websites, the effectiveness of such banners is in question. The purpose of this paper is to illustrate how we can improve the performance of online banner advertising, particularly, the ones about online shopping websites, and suggest an algorithm for inspecting the ideal frequency of banner advertisement, based on the forgetting theory and the Ostrow Model. The forgetting curve was first hypothesized by H. Ebbinghaus in 1885, and shows how rapidly people forget information when no attempt is made to reinforce it. To test the algorithm, a set of data needs to be collected from websites that provide ad publishing services for online shopping malls. Both the websites with high traffic volume and ones with lower traffic volume should be taken into account for the data collection. he many forms of online advertising include display advertising, mobile advertising, adver- T tising, and many more. This paper focuses on web banner advertising. Web banner advertising is a type of display advertising. Display advertising delivers its advertising message visually. Most of the time, text, animations, logos, photographs, or other graphics are used as the carrier of messages. An appealing trait of display advertisement is that advertisers can target users with particular characteristics to increase the effect of the advertisement. Cookies are the most commonly used tool in delivering advertisements to a particular user. Cookies are unique identifiers of computers and they can track if a user left a page without purchasing anything, so the advertisers can later target the user again with ads from the site the user previously visited(heft and Vega, 2010) Unlike other online advertising that are less targeting and not personalized much, display advertisements can deliver online ads that consumers are actually interested to click. Among them, web banner advertising is generally graphical ads displayed on a web page. Most of banner ads, or web banners are delivered by a central ad server. Web banner advertising is considerably a good practice, as it can reach a wide, yet targeted audience in the whole globe. However, the problem is that it might actually not be as effective as one would like to believe. This is Yunyoung Hur*, Corresponding Author Master in School of Business, Seoul University skb02014@gmail.com mainly due to the high cost it requires and low clickthrough-rate of the banners. The cost of web banner advertisements are determined by a revenue model the advertiser decides to adopt. The widely used kinds are CPM-cost per impression, CPC-cost per click and CPA-cost per action-revenue models. The price of advertising banners tends to vary based on the selected revenue model. Yet what stays the same despite the chosen revenue model is that the costs of advertisements tend to be burdensome. For example, The CPM can differ from $10 to $100 per impression based on how targeted the website is and the traffic amount the website receives. Calculate in the cost of producing the advertisement, and the total cost of placing an advertising banner on a single website can require as less as a few thousand to 450,000 dollars to produce, a cost that is actually paid to Yahoo for one-day homepage takeover(marshall, 2013). If the advertising company wants to place banner ads on multiple websites, or even a single, yet popular one with heavy traffic, the price the firm has to pay can go as high as no limit. Considering that most of the firms believe that displaying an advertisement for a single day has little effect and decides to display advertising banners for a certain long, consecutive period of subscription, the total costs of web banner advertising will be even higher. Moreover, despite the burdensome cost of displaying banner ads on websites, the effectiveness of such banners is in question. According to a research done by Smart Insights, the average click through rate (CTR) of advertising banners is 0.11% (Chaffey, 2013) CTR stands for click through rates. It is the ratio of clicks over the amount of impressions viewed. Therefore, high CTR generally means that advertising banner is optimized correctly and it is interesting enough to the publish-

3 Yunyoung Hur* / 53 FIGURE 1 Ebbinghaus Classic Forgetting Curve Source: Herman Ebbinghaus, Memory, a Contribution to Experimental Psychology, ing website visitors for them to click the banner to learn more about the preached messages. For online banners, high CTR does not always mean high profit. What rather matters in optimizing advertisement profit is conversion rate, the rate of conversion among the website visitors to paying customers. Since high CTR means higher volume of the website visitors, one can assume that it would generally mean high conversion rate. Yet, a high CTR does not always promise a high conversion rate. It is even possible that these two have an inverse relationship, for an advertisement geared toward curiosity clicks will lead to fewer conversions in terms of overall percentage, than an advertisement geared toward qualified clicks. Still, low CTR is not a thing to welcome as focusing on high conversion rate and neglecting CTR will keep the advertiser from generating the actual profit through advertisement. Higher CTR means higher traffic volume for maximization of conversion opportunities. If CTR is neglected, the case is that high conversion rates will not do any good as the conversion volume will stay low. Also, if the revenue model in practice is CPC model (cost per click model), then maintaining high CTR becomes an important task because CTR is a key factor in deciding an ad s quality score. Quality score is Google s rating of the quality and relevance of an advertisement. Google awards advertisers with higher quality scores with higher advertisement rankings and lower costs. Therefore, the staggering average click-through-rates show that the effectiveness of online banners may not be as high as one hopes it to be in terms of collecting profits. Taking the high cost of web banners into calculation, the effectiveness of web banner advertising might a mere illusion. However, if we treat these problems right, it means to be taking out these problems - the low CPR and high cost. Advertisers, consumers, and even the market as a whole will benefit by it as the result. The primary question of this paper is how we can improve the performance of online banner advertising, particularly, the ones about online shopping websites. This paper plans to answer the question by suggesting the ways to solve for the problem of high cost and low CPR. This paper s contribution is that it can improve the effectiveness of web banner advertising. Background Literature In terms of pre-existing literature, there are many researches that aim to improve the cost system of online banners. Such researches seek to lessen the burden of advertisers by developing a predicting system that matches certain types of ads to the particular lists of websites of which its visitors are most likely to click that types of ads(sherman and

4 54 / Journal of Marketing Thought TABLE 1 Ebbinghaus Findings in His Classic Forgetting Curve. delay Savings Immediate 100% 20 minutes 60% 1 hr 45% 9 hr 35% 1 day 30% 2 days 25% 6 days 22% 30 days 20% Deighton 2011). Nevertheless, despite the fact that banner advertising is one of the dominant means of online advertising, most of the researches done on online banner advertising domain have been centered on scheduling of advertisements (Mahdian, Nazerzadeh and Saberi 2007) and bidding (Ghosh, Rubinstein, Vassilvitskii and Zinkevich 2009). It is well known that the web advertising system has a database, a controller, and an advertising serving operating as a part of a web server(alberts 1999: U.S. Patent No. US A). The database carries advertising campaign information, including frequency information for how often the ad is to be served and identification information. The advertising server uses the very campaign information to control the ratios of displaying ads, triggering mechanisms for controlling which ads to be served, and the distribution of ads during the day. Then it occurred to me that the frequency of the web banner exposure can be controlled. Surprisingly, none of the existing researches so far, directly deals with the problem of high cost and low CPR of online banners as by controlling the frequency exposure. The closest that comes discusses the relationship between advertising frequency exposure and the effectiveness of online advertising(broussard 2000). The paper brings in the idea of effective frequency the ideal number of banner exposures to a user that result in an actual action. Yet, the paper only briefly addresses the concept of efficient frequency, and though it is such a fascinating concept, the concept of efficient frequency is mostly applied to television and media advertising instead of web advertising. Therefore, in this paper, we plan to utilize the concept of efficient frequency in solving for the problems of web banner advertising. This differs from the existing researches in that it is one of the first that corporates the concept of efficient frequency in the field of banner advertising. Theoretical Background and Model This paper seeks to address each aspect of the problem (high cost, low CPR) with the concept of efficient frequency, in particular, with the forgetting curve hypothesis by H. Ebbinghaus and J.W. Ostrow s Effective Frequency model. The forgetting curve was first hypothesized by H. Ebbinghaus in The forgetting curve shows how rapidly people forget information when no attempt is made to reinforce it. FIGURE 1 depicts Ebbinghaus classic forgetting curve(ebbinghaus 1885) To test human memory and forgetting, Ebbinghaus s measure was to study his own memory for a list of words that do not make sense. The list includes nonsense words such as BEL, LIM, MUK, etc. Ebbinghaus exposed himself to the words until he memorized them perfectly and measured his pattern in mastering the words perfectly. FIGURE 1 shows the amount of memory that was saved as the time passed. Table-1 shows the data in greater detail. Both the graph and information in the table show that there is exponential loss of memory right after information is learned. Learned information stays 100% immediately after it was memorized. However, 40% of the information is already gone when 20 minutes are passed. By the time that 1 hour is passed, only 45% of the conceived information remains. After 9 hours are passed, 35% of the original memory remains. The remaining memory declines and when there is no attempt to reinforce the memory back, only 20% of information is left in a person s memory. The interesting part Ebbinghaus noticed was that if the information was reviewed over and over again, it would not only strengthen the remaining memory back to its original form, but also reduced the rate of memory decay. In other words, Ebbinghaus claims that the most efficient way to

5 Yunyoung Hur* / 55 FIGURE 2 Change in Forgetting Curve after The First Review FIGURE 2 Change in Forgetting Curve after The First Review prevent this kind of memory loss is repetition. His research shows that if the material is reviewed, the forgetting curve will change its format so that it takes longer in forgetting the material. This is displayed in FIGURE 2. As the material is repeated multiple times, the memory will become more permanent. When you look at FIGURE 3, the forgetting curve becomes flatter, and thus memory retention increases, as the material is repeatedly reviewed.

6 56 / Journal of Marketing Thought TABLE 2 Ostrow Model of Effective Frequency Low Required Frequency Frequency Adjustment High Required Frequency Market Factors Established brand New brand High brand share Low brand share High brand loyalty Low brand loyalty Long purchase cycle Short purchase cycle Less frequent usage Frequency usage Low share of voice High share of voice Target other group Target old people or children Message Factors Low message complexity High message complexity High message uniqueness Low message uniqueness Continuing campaign New campaign Product-focused message Image-focused message Low message variety High message variety High wearout Low wearout Large advertising units Small advertising units Media Factors Low clutter High clutter Favorable editorial setting Neutral editorial setting High audience attentiveness Low audience attentiveness Continuous scheduling Pulse or flight scheduling Few media vehicles More media vehicles High repeat exposure media Low repeat exposure media The material should not be repeated too much. According to the spacing effect, reinforcement of memory over a longer period is more effective than cramming information - reinforcing information over a short time period. Other researches confirm this by showing that repeated retrieval of information with long intervals between each reinforcement results in a 200% improvement in long-term memory relative to repetition with no spacing between tests. Ideally, reinforcement of information should come just before the information is forgotten, but not too early, to optimally promote (Karpicke and Bauernschmidt 2011) This is referred as spatial repetition. The researches show that certain number of repetition with spatial intervals in between work better than intensive repetition of the same message during a comparably short period of time. This notion will not only be limited to the learning domain, as most of the

7 Yunyoung Hur* / 57 researches about the forgetting curve and the spatial repetition are, but also applicable to the online advertising banner domain. For the next part, one possible explanation for low CTR is effective frequency. The effective frequency in advertising is widely referred to as the number of times a man must be exposed to the delivered message before a response is created and before the advertising exposure is considered wasteful. Frequency is a well-established variable in online advertising effectiveness. It has been well documented that people tend to click on web banners during the first few opportunities of exposure and beyond that point, the click rates significantly diminish. (Broussard 2000) A possible reason for low CTR can be that maybe the banners are displayed at the frequency that is too high and thus, consumers lost their interest in the ad. The question for advertisers then becomes what is the effective frequency of exposure of web banners? The question can be reworded into what is the optimal number of banner exposures to average users that results in action? Joseph W. Ostrow s efficient frequency model answers the question. He created a decision model to help determine the optimal frequency level by analyzing marketing factors, media factors and copy factors. (Ostrow 1984) Beginning with a baseline effective frequency of 3, the model makes adjustments in frequency based on 20 factors in three categories. As shown in Table 2, each category is composed of several statements, upon which the model is built to help make judgments by circling an appropriate rating in that row. For example, let us look at a case where the first factor asks the media planner to rate if the product is a "New brand" or "Established brand." A new brand will require higher frequency exposure than an established brand, and therefore, the media planner would circle the "+.2" frequency adjustment in the model. After taking a look at the factors, the media planner adds all the adjustments to calculate the effective frequency recommended by the model. Media planners can modify Ostrow s model by adding or removing statements in order to get more appropriate estimates. According to the model, setting frequency level goals differ in that higher-level communications such as lead generation and persuasion tend to require higher frequency levels. An example of this is that brand awareness generally requires lower levels of frequency than advertising lead generation and persuasion. To reiterate, advertisement that intends to change the brand preference among the existing consumers of several competing brands would need a higher frequency of exposures than an advertisement that intends to introduce a new brand. Even though the model was originally developed with television advertisements in mind, it seems plausible that the Ostrow Model can be used in determining the effective frequency of online advertising banners as well. CONCLUSIONS Despite the popularity of the theories, the forgetting curve and spatial repetition are majorly studied in the learning domain, and the Ostrow Model was developed with television advertising in mind. The paper suggested an algorithm that inspects the ideal frequency of banner ads for online shopping websites. By constructing such algorithm, online banners will identify the optimal frequency in which they can be presented to meet the ideal interval in promoting memory retention, and optimally lure consumers to click the banners. The creation of the algorithm, therefore, will increase the overall effectiveness of web banner advertising greatly. To test the algorithm, we need to crawl data from websites that provide ad publishing services for online shopping malls. Both the websites with high traffic volume, such as facebook and Yahoo, and the websites with lower traffic volume, such as individual fashion blogs, will be taken into account for the data collection. This paper is unique for that it is utilizing the widely popular concepts in the online advertising domain, however, since both theories that construct the backbone of the paper s research are not fully related to the field of online advertising, it is possible that the forgetting theory and the Ostrow Model might have to go through a kind of modification during the research. REFERENCES Alberts, Charles D. (1999), "Banner advertising display system and method with frequency of advertisement control," U.S. Patent Number: US A. Washington, DC: U.S. Patent and Trademark Office. Bayles, Michelle E. (2002), Designing online banner advertisements: should we animate?, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, 2002 New York, NY, USA, Broussard, Gerard (2000), How advertising frequency can work to build online advertising effectiveness, International Journal of Market Research, 42 (4). Chaffey, Dave (2013), Display advertising click through rates, Smart Insights. Ebbinghaus, Hermann (1913), Memory: A contribution to experimental psychology, 3, Teachers college, Columbia university. Ghosh, Arpital, Benjamin I. P. Rubinstein, Sergei Vassilvitskii and Martin Zinkevich (2009), Adaptive bidding for display advertising, Proceedings of the 18th international conference on World wide web, ACM,

8 58 / Journal of Marketing Thought Helft, Miguel and Tanzina Vega, (2010), Seeing that ad on every site? You re right. It s tracking you. The New York Times. Retrieved June 8, 2014, from stalk.html?_r=0 Mahdian, Mohammad, Hamid Nazerzadeh and Amin Saberi(2007), Allocating online advertisement space with unreliable estimates, Proceedings of the 8th ACM conference on Electronic commerce, ACM, Marshall, Jack. (2013), What Online Ads Really Cost, Retrieved February 22, 2013, from Ostrow, Joseph W. (1984), Setting Frequency Levels: An Art or a Science?, Journal of Advertising Research, 24(4), I9-I11. Sherman, Lee and John Deighton (2001), Banner advertising: measuring effectiveness and optimizing placement, Journal of Interactive Marketing, 15 (2),