Does advertising decay exist?

“Though we’ve been doing advertising effectiveness analysis for over a year now, we’re continually learning new things about online advertising. The newest learning? The time-honored concept of “advertising decay” is a myth.”

The Myth of Advertising Decay

A really interesting case study from Compete for Banana Republic, and incidentally, for intrusive big-ass ad formats on major media properties such as NYT.com

Qualitative Value of Display Advertising

One thing that often comes up in my discussions with fellow geek marketers is the qualitative value of display advertising. More precisely, the idea of valuing advertising on metrics other than the direct clicks, interactions, acquisitions, links, and so on. These other metrics may include things like lift in brand awareness or appreciation, for example, and complement quantitative metrics such as the hard clicks mentioned earlier.

Survey-based measurement, while it has its own flaws, is one of the staples of advertising measurement, and one that is little known or understood outside of the agency/advertiser circles.

Lucky you, dear readers, for Chen Wang writes a long overview of the techniques and methodologies over at MediaPost which is well worth a read. Keep in mind that the magic really happens when you combine multiple measurement sources and methodologies. The insight of mashing up site analytics, ad server analytics and survey-based measurement is often overlooked but always appreciated when properly executed.

Q: When are people most likely to pay attention to online ads?

A: When they’re relevant and include freebies.

This is the result of IAB UK’s study conducted recently (PDF of the summary). The problem here is that only an alarmingly low percentage of users in any given age group claims not to notice ads. My theory has always been that people hate admitting to being influenced, but here 70% of people don’t have a problem with it at all. Which begs the question — are they really telling the truth, or is this completely theoretical?

If this was based on actual behavior data, I bet the first winning category would be “when ads are big enough to be noticed”, closely followed by the second — “when they’re relevant”.

(link via  @jonathanmendez)