Improving Ad Research Insights
If we start with the assumption that all of the techniques listed in the matrix, when used by a reputable research supplier, have a legitimate claim to a piece of the truth, the real question becomes, What new insights or predictive power can be gained by using different techniques in combination?

To begin to sort this out, it is useful to first look at what might be gained by combining techniques falling within a specific quadrant, and second, to look at what might be learned from putting together combinations across different quadrants.

Within Quadrant Analysis

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To understand the value of combining techniques within a quadrant, it is easiest to start with the most familiar case, the metrics provided by the big, mainstream pre-testing systems falling in the lower right-hand quadrant.

Over time, these systems have evolved based on learning that different questions can provide equally important, but complementary measures of advertising quality. For example, it is now generally accepted that attention-getting power, brand linkage, motivation, and communication are all important predictors of in-market performance.  As a result, these metrics have become widely adopted as “report card” measures of performance that advertisers use to make go/no go decisions.

But another thing mainstream systems have learned is that other questions, while not as important as the primary, report card metrics just mentioned, can provide very useful diagnostic insights into the reasons why a particular ad is performing the way it is. For example, a high entertainment rating score is not important in and of itself, but only insofar as it is a useful explanatory variable correlated with the attention-getting power of an ad—and more recently, as one of several indicators of an ad’s likelihood of going viral on the Internet.

Moving to the upper right-hand quadrant, one of the things we quickly learned at Ameritest is that, similar to verbal questioning, one picture sort was not enough. To provide a complete set of diagnostics for the visual effectiveness of an ad, we need to look at a more complete picture. We ultimately developed three: to measure memory, feelings, and the meaning of visual imagery.  And to complement that, we developed two copy sorts, measuring recall and relevance, to measure the verbal component of an ad.

Looking to the upper left-hand quadrant, the new, interesting biometric techniques are still at the stage of experimentation. For example, much remains to be learned about whether or not brain wave measurement is more or less predictive of ad effectiveness than measuring heart rates or coding the emotions on respondents’ faces. Whether some of these techniques should be viewed as primary “report card” measures and others as secondary “diagnostics” remains to be sorted out.

Moreover, because many of these different techniques are being promoted by different small technology startups, it is still early days for learning the benefits to be gained by putting these techniques together in different combinations. It is encouraging to see the results obtained by companies like Sand’s Research, which combines brain wave measurement with eye tracking, and as a result is emerging as an insightful researcher of in-store advertising.

For more information or for a copy of the full article, please contact Sonya Duran (sonya@ameritest.net).