Predicting Attention Is Worth More Than Measuring It

The outcome dimension of Advertising Attentiveness has been rising in importance year after year as more techniques become available for measuring it. Sorin Patilinet, Global Marketing Insights Director at Mars, has been a leader in bringing forth the importance of Attention.

Attention did not even appear as an official step in the advertising process until 2003. The original Advertising Research Foundation (ARF) model of the advertising process was developed by the brightest minds in the business between 1955 and 1961. In that six-step ARF model the next step after Advertising Exposure was Advertising Perception, and the next step after that was Advertising Communication. One of the greats in marketing research, Alfred Politz, pointed out that the only way that was available for measuring advertising perception known at the time was by using a proxy, advertising recall, which was defined by the model as part of Advertising Communication. Alfred therefore considered the Advertising Perception level a waste.

When then-ARF President Jim Spaeth asked me to chair a committee to update the ARF model around the turn of the 21^st century I offered co-chairmanship to Erwin Ephron, who became the de facto leader of the project. (I wrote the first draft and he brilliantly improved every word and phrase.) He was also passionate about adding one specific level to the model: Advertising Attentiveness.

Why is Attentiveness so important? Because it is the beginning of the success journey of the ad exposure. It is not a sufficient condition the way incremental sales effect is, but it is a necessary precondition if the effect is ever to reach all the way to incremental sales.

The creative and the media both make contributions to every single level in the ARF model. How could it be otherwise? Creative and media are abstractions in our minds. The consumer sees one thing together which is the combination of those aspects.

However, in the business framework one must draw a bright line somewhere indicating that the media has done its job and ought to get the money it expects. Historically this has been thought of as occurring at Advertising Exposure, the last stage at which media might be penalized for non-performance. (Note that as of today in 2020, Nielsen is still reporting Advertising Exposure based on the average ad in a program, rather than the more desired individual ad.) In our 2003 version of the ARF model (which is the latest version available today) we made this more of a grey scale with these words: “As we move to later stages, the role of the advertising creative begins to dominate.”

Many of the ideas in Marc Guldimann’s excellent Measuring Attention Pathway paper are solidly in line with the 2003 ARF model, such as his fine distinctions of the degree to which duration of exposure is and is not a fair measure of attention. This echoes and adds to our 2003 ARF model remark that duration and loyalty need to be validated as indicators of attentiveness. This is highly relevant nowadays as the Media Rating Council (MRC) is considering increasing the importance of duration and needs to finely hone the way duration should and should not be used.

Marc's Adelaide system is not only a method for measuring attention, it is a method that can be used predictively during media selection, e.g. programmatic Real Time Buying (RTB). Adelaide is constantly learning from available signals what placement variables to look for as the best predictors of the highest attention. Considerable learning has already been amassed in Adelaide and more gets added every time it is used with new creative. The placement variables relate in large part to the world of digital in which where you are on the page, the size and shape of the ad, pictures vs. text and what the page itself is, are the types of factors Adelaide considers and learns from so as to maximize attention to the ad. Because of the amount of ad clutter in most of digital, someone one day would have thought of Adelaide, and Marc and his colleagues did it first and best.

Marc and I are discussing how RMT technology and Adelaide technology might work together to maximize ad attentiveness. RMT tech is focused on decoding the memes in the ad and then placing the ad in contexts whose memes are at maximum overlap with the memes in the ad. This has been shown to increase incremental sales and branding metrics. Logically the two approaches add to one another with minimum overlap, and so the combination should provide even better results than using either one alone. Stay tuned for findings from the field as they are accumulated or get in touch with one of us if you’d like to take the lead in such experimentation.

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