Click-Through Rate, R.I.P.

Friends! Reps! Online media planners! Lend me your ears! We have come here to bury the click, not to praise it!

That’s right, faithful readers, you can say that you read it here first. And after you read the first formal eulogy written for the click-through rate, go forth as your agency’s town crier and shout the news from every rooftop, from every conference room, and at every client-input session.

‘Tis true, click-through is dead.

Well, it is as a metric for determining an online campaign’s success anyway, if that campaign is interested in anything other than traffic.

From time to time in this space I’ve discussed the varying metrics that can be applied for determining the success of varying aspects of an online ad campaign. They have been:

• Click-through rate (CTR): This metric is good only for determining the success of creative and maybe the appropriateness of the placement.
• Cost per click: This is the least-meaningful metric if you are interested in moving product, but it is meaningful if you are simply trying to drive traffic.
• Cost per pageview generated: This metric is meaningful only if the advertiser is a publisher selling impressions.
• Cost per order/action: This metric is necessary if you want to determine the efficacy of your efforts in getting customers to buy something, register for a newsletter, request more information, etc.
• Advertising-to-sales ratio: This describes how much income is yielded by every dollar spent on online advertising.

The further down the list you go, the weaker CTR’s role becomes. But none of the metrics listed here alone toll the death knell for the CTR. Instead, it is a new metric in town, one that is making the rounds at conferences, in industry conversation, and even a bit in the trades.

Unlike the metrics listed above, it is a correlative rather than causal metric, which makes it even more surprising. Rather than showing a cause-and-effect relationship, it demonstrates something reciprocally related without a direct A-to-B connection.

What I’m talking about is the relationship between those that have been exposed to an ad message, didn’t click on the ad unit, but went to the advertiser’s site and transacted anyway.

AdKnowledge’s “Online Advertising Report: First Quarter 2000” indicates that 34% of site transactions involved individuals who arrived at the site in just this way. That’s an extraordinary figure. And it suggests that though click-through rates are down, it doesn’t really matter because users are making it to advertisers’ sites regardless of direct response to the advertising.

I currently have only a few clients gathering this data, and we are still working on the various ways to use it. But what it means is that perhaps sites on which you or your client have run advertising may be performing on a correlative level that is not reflected on a causal one. Sites that are “stickier” than others tend to have lower CTRs but have higher levels of audience involvement. It is possible you will find that, though a sticky content site has a less-than-desirable click-through rate, it’s actually sending more transactional visitors to the advertiser’s site than those sites that have higher CTRs.

But how do you get at this data?

Well, as this kind of read is just becoming available, I know of only a few.

 

• Third-Party Ad Servers

 

 

• The more robust third-party ad servers are able to now report this kind of data. Doubleclick’s DART Analyzer, for one, is capable of gathering and reporting on this kind of information. So is AdKnowledge. The user is cookied at the impression level, i.e., at the time the banner is served. If that user then goes to the advertiser’s site without clicking on a banner, that cookie gathers that information as well as any activity committed on the advertiser’s site and then reports that back to the server.

• Site-Profiling Tools

 

 

• This solution is a little less elegant and a bit more manual, but some of the same information is yielded. Depending on the profiling tool employed, it may also require additional assumptions about the user’s activity. Site-profiling tools, like Cogit and Personify, can identify the referring URLs from which users are coming. If advertising is running on the site belonging to that referring URL, you can assume (though it is just an assumption) that the user saw an ad and came to the site. With some manual coding, you might be able to eliminate the assumption and get some tighter data.

• Profiling Plus?

 

 

• I’m actually not sure what you’d call this kind of application, but I’ll give you an example of what kind of tools I’m thinking of here. Primary Knowledge is an outsourced solution for data warehousing, data management, reporting, and data mining. The company aggregates customer data from in-house and offsite data silos and houses it in a single, secure data mart. The data is transformed daily into individual customer-interaction profiles that enable certain “decision-ready” business reports and analytical tools. Ad-serving data is married to site-side log files and story data, which allows for a correlative read of ad exposure and subsequent site activity.

So spend some time with your clicks. Visit, talk about old times, and even bring flowers because the click-through days are numbered.