The Advertising Research Foundation’s 54th Annual Meeting and Convention just concluded in New York City. This was a watershed event, with over 700 paid attendees sharing presentations over three days. The organization is made up of advertisers, their agencies and media planning partners, the media, and market research companies. At the meeting, companies demonstrated their latest, most innovative media offerings, creative product, use of media and sharing their research tools and studies advancing the measurement of ad creative, media, advertising effectiveness, and customer feedback.

I call it a watershed event because so much of the focus of the event was on digital advertising and social communication on the web. A year ago, digital media much less on everyone’s tongues. comScore, as a platinum sponsor of the event, presented a special session called “Online is the New Primetime,” documenting the immense size of the online audience and its development as an advertising medium. Despite the fact that consumers spend at least 17% of their media time online, online advertising only accounts for 7% of advertising spending. We shared work we have done with our clients to demonstrate the results they are getting from online display and search advertising—some uniformly impressive results.

We posed the question that given the huge audience and proven effectiveness of online marketing efforts, “Why are some advertisers so slow to adopt online ads?” We turned to a panel of advertising experts from Yahoo!, Google, MSN, P&G, CBS Interactive, and Mediaedge:cia to discuss the reasons. Those comments will be the subject of future blogposts. Here I would like to share some of the most interesting data on the audience size and invite you to a replay of the webcast that was broadcast on April 16. Click here for the replay.

Television is still the pervasive medium and the bedrock of most major brand advertising plans. Network television generates large reach numbers (not as large as a few years ago, but the most of any medium per exposure) and high engagement. However, the Internet has risen to be the second or third highest cumulative reach medium, depending on the source of the numbers.

One striking finding presented in our session was that during most waking hours, more people (age 15+) are using the Internet than are watching television. It is only for the last two hours of primetime and into late night, when most people seem to wind down their Internet usage, that TV consistently surpasses Internet usage. We presented the following chart which shows the hour-by-hour comparison of Internet and television usage.

Sources: National People Meter Sample, comScore

Another analysis that was part of the presentation was one prepared by our CEO, Magid Abraham that quantifies the impact of the Internet as an advertising medium compared to television. Based on our tracking of online ads compared to an estimate of the number of ads run on television and average TV ratings, it appears that the Internet delivers forty percent more gross rating points than TV in the course of a month and 120 percent more impressions. It was surprising, even to us, the heavier advertising delivery of the Internet versus TV.

Source: comScore estimates

If you would like to get a copy of the slide deck from the “Online is the New Primetime” presentation, please click here. For a replay of the webcast, please click here.

Kevin Mannion’s January 25 blog about engagement metrics on MediaPost’s Online Metrics Insider raises some interesting issues about online measurement, but fails to address the inability of site server data to provide any of the key people-based metrics that are needed for online media planning and analysis. In the world of online media, I never cease to be amazed at how easy it is for some to forget or ignore that basic reality of advertising. Earlier in my career, I spent 14 years at Leo Burnett and can assure you that we didn’t plan our media efforts around advertising to TV sets. No, we advertised to the people watching the TVs. And so it is with the Internet. Online media planners, just like their offline counterparts, fundamentally need to know how many unique people are visiting a particular site, with what frequency per person, and they need to know this by demographic segment. I have yet to see server log data that comes anywhere close to providing this people-based information.

It’s left up to the comment by John Grono of GAP Research in Australia posted on Mannion’s blog to set the record straight by highlighting some of the key problems with site server data and revealing the ugly reality of what happened in Australia, where the use of server data resulted in an estimate of that country’s online population that was more than twice the size of the entire Australian population! As John notes, the Australian experience has unequivocally shown that site server data grossly overstate the true number of site visitors.

The most deleterious problem with site server data is caused by cookie deletion. An important comScore study published last year showed -- beyond a shadow of a doubt -- that 31% of Internet users delete their cookies in a month and that the average cookie deleter does so four times per month, resulting in the placing of five different cookies for a single site on a deleter’s computer in the course of a month. Every time an Internet user returns to the same site after deleting their cookies, they will be counted as a new user. This can result in a dramatic exaggeration of the size of a site’s audience by as much as 2.5 times when using site server data. To make the problem even more acute, there is such a large variation across sites in the rate of cookie deletion among their visitors and their frequency of visitation to the sites that it is essentially impossible to build a model to predict the degree of exaggeration for individual sites based on site server data.

Beyond the cookie deletion problem, there are other problems with server data:

1. Cookies (or page tagging / beacon approaches) are incapable of accurately counting the true number of individual users:
   1. The same person may use different computers (e.g. a work and home machine) to visit the same site in a day and will be counted as two individual visitors
   2. Different people using the same computer and visiting the same site will be counted as one visitor
2. Server data cannot reliably determine if the visitor is a real person or a computer. In fact, some industry observers have estimated that “bot” traffic at a wide variety of sites now accounts for more than 30% of all traffic to those sites.
3. Using server data, there is often no way to reliably identify the geographical location of a visitor. For may U.S. sites with large numbers of International visitors, this can create massive exaggeration in server-based estimates of U.S. audiences by as much as 4.5X, which is incremental to the overstatement caused by cookie deletion.
4. Last, but by no means least, server data provide no information on the demographic characteristics of site visitors.

It is challenging for publishers to reconcile the conflicting data they get from multiple metrics sources, and understandable in today’s competitive environment that there is a desire to tout the highest audience count possible to both advertisers and investors. However, as comScore and other industry bodies have been working to educate users on the fundamental differences between server data from web analytics providers, and panel data from audience measurement companies, it is encouraging that publishers are increasingly relying on the audience measurement data to tout audience size. This is as it should be.

Intriguing though the concept may appear, the claim that server data can be integrated with panel data in order to obtain accurate online audience data is fundamentally flawed. No amount of “black box” manipulation can correct for the fact that server data are incapable of knowing who is on the computer visiting the site. You simply can’t make a silk purse out of a sow’s ear.

I fail to see how Quantcast can make the claim that panels aren’t capable of measuring the mid to long tail of the Internet (“the fragmentation of the Web kills the utility of the panel”) while at the same time saying that they start with panel data to adjust the server data. You can’t have it both ways. If the panel data’s sample sizes are too small to be able to measure these sites, how on earth can one possibly say it can be used to adjust for all the errors inherent in server data when it comes to counting people. No, the simple fact is that this methodology will end up relying on the server data and exaggerating the size of the sites’ visitor base.

As a final point, I think it’s important to reiterate the observation made by John Grono in his comment that some of the largest and most important sites in Australia decided to not provide their server data to third parties. This would appear to be a “deal killer” for any integration initiative before it even gets started. If many of the most popular Web sites in Australia decided to “not play ball”, I think we can take it for granted that the U.S. market will see many more sites deciding that their competitive position will NOT be enhanced by sharing their data with third parties.