Banner advertising: Measuring effectiveness ... - Wiley Online Library

MARKETPLACE BANNER ADVERTISING: MEASURING EFFECTIVENESS AND OPTIMIZING PLACEMENT Lee Sherman John Deighton

LEE SHERMAN is Director of Consulting and Analytics R&D at Avenue A Inc., Seattle, WA.

f

JOHN DEIGHTON is the Harold M. Brierley Professor of Business Administration at the Harvard Business School, Boston, MA.

ABSTRACT The article describes how one company improved banner advertising response rates by taking advantage of the medium’s rich data to optimize placement. The study identified Web surfers who were frequent visitors to the banner advertiser’s site. It then identified 100 Websites that these surfers tended also to visit. These sites were cluster analyzed to yield site genre definitions (affinities). In this manner a model was built to identify a group of affinities whose visitors were disproportionately likely to respond to banner advertising. These predictions were tested by placing banners on sites forecast to perform well. The average cost per response was nine times lower for sites predicted to belong to high affinity groups than low groups.

© 2001 John Wiley & Sons, Inc. and Direct Marketing Educational Foundation, Inc. f JOURNAL OF INTERACTIVE MARKETING VOLUME 15 / NUMBER 2 / SPRING 2001

60

BANNER ADVERTISING

to learning can be very short. This flexibility occurs because, as we shall describe, Internet advertising is “served” to surfers by companies like DoubleClick and Avenue A who are distinct from the publishers who serve all other content. In this article we describe work performed by Avenue A on behalf of its client, drugstore.com. Avenue A, the digital marketing and technology company, was founded in 1997 to help e-marketers acquire, retain, and grow customers across all digital media. drugstore.com, founded in 1998, is a licensed online pharmacy and leader in healthcare e-commerce, with 1999 sales of $34 million. Avenue A is a digital marketing services firm based in Seattle. In June 2000 drugstore.com cooperated in a field study of Web-surfing behavior with Avenue A to improve banner advertising’s ability to drive traffic to its online store before and during the 2000 Christmas period.

The economic value of online advertising is controversial. One report estimates the annual run rate of online advertising spending at the end of 2000 to be $8 to $9 billion (Internet Advertising Bureau, 2000), with the implication that a medium with zero share in 1992 now commands about 5% of all U.S. advertising spending. Despite this performance, a skepticism has begun to develop. Observers question whether it “works.” The investment rating of the ad-serving firms, Doubleclick, Engage Technologies, and 24/7 Media and Avenue A Inc., have declined substantially. Click-through rates from banners to websites, a measure of audience response to banner advertising, have declined steadily with growth in the volume of banner advertising, and now run at about 0.3% of impressions for Nielsen’s sample of Web users (Nielsen/Netratings, 2000). The very accountability for which online advertising was once praised is now employed to question its value. If a medium is accountable, however, it should be able to improve its own performance over time. This article describes one program to systematically enhance the performance of online advertising. The goal of this article is to show, by means of an example, how analysis was able to reduce the cost-per-action of banners for an advertiser. The improvement was achieved by sharpening the definition of the target market, and selecting websites that were attractive to the target. In this way the advertiser ultimately obtained banner advertising performance significantly better than the levels of performance reported in overviews of the industry. Three distinctive properties of the Internet advertising medium make possible this kind of improvement over time. First, unlike previous advertising media, Internet advertising accumulates a digital trail of audience exposures and responses that can be analyzed to measure how well the advertising is working. Second, the particular advertisement served to a website visitor can be made to depend on the characteristics of that audience member. Third, these advertisement content changes can occur very rapidly and without changing other website content, to take account of this analysis. Cycles of learning and response JOURNAL OF INTERACTIVE MARKETING

DESIGN OF THE STUDY The study had three goals and a validation phase: To identify the Web-surfing habits of a sample of customers of drugstore.com, as a basis for classifying Web users into high-value prospects and non-prospects by their surfing profiles. To classify a sample of sites into those likely to be visited by customers and high-value prospects, and those not likely to be visited. To classify sites into “affinities” so as to generalize from the sample of sites to the universe of sites. In the validation phase, advertising was placed on sites that the model predicted would generate high and low levels of traffic, and subsequent traffic to the drugstore.com site and sales on the site were measured. The data assembled for the study can be visualized as a matrix of visitors by sites, as depicted in Figure 1. The data were assembled as follows. First, a sample of customers was selected in collaboration with the drugstore.com marketing team. Avenue A’s Consulting Group and the ●

61

VOLUME 15 / NUMBER 2 / SPRING 2001

JOURNAL OF INTERACTIVE MARKETING

FIGURE 1

Data Matrix

THE ROLE OF THE AD SERVER IN DATA COLLECTION

team identified a segment of high-value drugstore.com customers based on actual visits and purchases at drugstore.com in the months prior to the study. Identification was by means of cookies as described below. Although for convenience we say that the study identified customers, it in fact merely identified the browsers of the customers so that the offline identity of customers was not recorded, and in that sense the anonymity of the customers was preserved. Second, a sample of sites was chosen for the study, comprising 800 of the 3,700 sites on which Avenue regularly purchases media and monitors traffic. These 800 sites were chosen because they had large volumes of traffic, of the order of hundreds of thousands of unique visitors each month. The third element of the data matrix was unique visitors to the sample sites during the months prior to the study. Entries in the matrix comprised visits to the sample sites and the drugstore.com site by the visitors, as well as transactions in the six-week study period at the drugstore.com site. JOURNAL OF INTERACTIVE MARKETING

Before the Web, ad serving did not exist as an activity separate from the serving of the content of a publication to its audience. Conventional advertising, whether television or print, is embedded into the editorial content by the magazine, newspaper, or television network affiliate who disseminates the programming. On the Web, banner advertisements are often served by a separate organization, such as DoubleClick, Engage Technologies, 24/7 Media, or Avenue A. Avenue A can be distinguished from the others by the fact that, while they contract with publishers of websites to sell advertising space and serve the banners of advertisers into that space, Avenue A contracts with advertisers to buy space. When a Web-surfer arrives at the site of one of the 3,700 publishers with whom Avenue A has contracts, the publisher’s server notifies the Avenue A server to supply a banner impression. This event occurs 6 to 8 billion times a month. Each time it occurs, Avenue A logs the request, ●

62


BANNER ADVERTISING

hold-out set comprising the last four weeks of surfing data. Each set had a binary dependent variable (was or was not a heavy drugstore.com customer), and 800 binary predictor variables (visited or did not visit each site). The predictive model was calibrated using CHAID, a technique that “explains” the binary dependent variable by means of the predictor variables. In this case, likelihood of visiting and spending at the drugstore.com site was explained by visiting a subset of the 800 other sites. The model was validated by showing that visits to those sites were also associated with above-average frequency of visits to drugstore.com in the holdout sample. The results of the model were applied to identify so-called high-affinity sites, or sites whose visitors have a disproportionate propensity to visit drugstore.com and therefore are good locations for drugstore.com banners. To do so, attributes of all sites were cluster analyzed to yield site genres. The expectation was that websites in high-affinity genres, whether or not they were among Avenue A’s 3,700 sites, would be better banner locations than run-of-network sites

then serves up the appropriate impression to the Web surfer’s machine. At that time it collect five pieces of information and adds them to a dataset: 1. The number on the cookie1 that identifies the computer used to view the impression (if the computer does not have an Avenue A cookie, one is assigned); 2. The website and ad placement to which the impression is being served. 3. The specific ad that is being served; 4. The time and date when the impression is served; 5. What action the Web surfer takes after receiving the impression. None of this information identifies the surfer personally, and none can be used to reach out to the surfer except via banners on subsequent visits. Thus the anonymity of the surfer is preserved in the database. In addition, Avenue A places what it calls an “action tag” on the websites of advertisers who are clients. These files are placed on several pages of a site, and record the movement of surfers across the site. They can be configured to show what pages they visit, whether they register, whether they make a purchase, what they buy, price paid, and similar information. In this way the matrix of sites by visitors described in the previous section was populated.

RESULTS More than 100 sites were identified as having high affinity, that is, a higher-than-average probability of driving customers to the drugstore.com online store. The model predicted that visitors to these sites were 38% more likely to visit drugstore.com than visitors to the average website. Once they visited, they would be 24% more likely to purchase than the average Web surfer. To validate the findings from this analysis, drugstore.com committed funds to test and compare the performance of high-affinity and low-affinity sites. A test plan was designed to compare cost per action (CPA) across the sites, and to control for a variety of creative treatments. For this test, performance was defined as a purchase at drugstore.com. More than 25 million banner impressions were served on sites in several genres. The results were significant:

ANALYSIS The first goal of the analysis was to decide which of the 800 sites were most visited by surfers who also visited the drugstore.com site. Two data sets were formed: a model-calibration set and a

1

A cookie is a small text file, containing a unique tracing number that the server of a website places on a user’s hard drive when the user visits the site. The server (and only that server) can detect this file when the user visits the website on a subsequent occasion, and so can recognize that the user’s computer has visited before. Cookies identify a browser and the computer on which it resides, not a person. Unless a computer owner has registered at a website and volunteered personal information, the cookie does not map to the individual’s offline identity nor compromise the individual’s privacy.


●

63


JOURNAL OF INTERACTIVE MARKETING ●

● ●

●

●

●

of soliciting the response is much higher for direct mail than digital media, and perhaps banners can have persuasive value even if they do not elicit a click-through, as at least one study suggests (Milward Brown Interactive, 1997). But the decline in average click-through rates remains a phenomenon to be explained. This article points to one possible explanation. Just as a heat-seeking missile is off-target for most of its journey but on-target when it matters, so some part of the life of an Internet advertisement is spent gathering the information that it needs to assure that it is on-target for the rest of its life. Possessed of the distinctive properties of ad serving and the short learn-and-respond cycle illustrated in this article, Internet advertising is self-correcting. It might be better judged not by the standards of broadcast advertising, but by a standard appropriate to an interactive medium like direct mail or outbound telemarketing. The question to ask of banner advertising, it is suggested, is whether it succeeds over the life of a campaign to optimize its cost-per-action, and thereby its return on marketing investment.

The high-affinity sites delivered a conversion rate (purchases per impression) 10 times higher than low-affinity sites. The average CPA was 9 times lower for high- vs. low-affinity sites. The high-affinity sites delivered prospects that were 9 times more likely to purchase once they visited the home page. The high-affinity sites, with no optimization, delivered a CPA close to that achieved by drugstore.com’s long-term partner sites such as Yahoo! and AOL after a year of continual optimization. The top 5 high-affinity sites achieved a CPA 27% lower than these partnership deals had produced. The analysis identified one site genre that delivered 43% of all orders, using only 32% of the total budget.

For reasons of confidentiality, the client prefers not to report the absolute click-through rates and return on investment in banner advertising achieved as a result of this optimization. Nevertheless the continued use of banner advertising suggests that the medium is valuable to the client.

REFERENCES Internet Advertising Bureau. (2000, December 20). Internet Ad Revenue Report. [On-line]. Available: http://www.iab.net. Milward Brown Interactive. (1997). Interactive Advertising Bureau Advertising Effectiveness Study. [Online]. Available: http://www.mbinteractive.com/ resources/download.html. Nielsen/Netratings. (2000, December 1). Nielsen/ Netratings Reporter. [On-line]. Available: http:// 209.249.142.22/weekly.asp#usage.

CONCLUSION The average rate of click-through from a banner advertisement to advertiser sites is, in general, declining, and at about 0.3% is substantially lower than the average rate of response to direct mail advertising, about 1.5% to 2%. Perhaps the comparison is unfair because the cost


●

64
