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How it all fits together - what comes out of Ad-Audit

We move on to practical matters and implications, having already unpacked the fact that advertising should be evaluated in terms of predictive models of both strategic intent and executional efficiency; the need for a new, better way of measurement when dealing with affective issues; and the invention of the System 1 type MSc measurement process for each respondent.
In the last three articles, we covered the following:
  • Theoretical foundation of Ad-Audit:

    Bringing precision and elaboration into ad testing

    In the 40 years I have worked in marketing research, the elephant in the room has been using focus groups to ‘evaluate', ‘investigate', ‘dipstick', ‘research', ‘copy test' or any other of the politically correct terms for ad testing...

    By mike broom 16 Jun 2017

  • The argument for paired comparisons:

    A new measurement paradigm that is ideal for ad testing

    One of the difficulties of advertising research is to determine the dimensions to measure.

    By mike broom 23 Jun 2017

  • The invention of and implications of the System 1 type MSc measurement process for each respondent:

    Measuring the consumers of advertising

    In our previous two articles we referred to measurement, principally measurement of advertising. But, of course, we are talking about obtaining measures from consumers of advertising, namely human beings...

    By mike broom 7 Jul 2017


We now turn to more practical matters, such as why it is profoundly more useful; case studies; and a short discussion of implications.

When talking about the MSc measurement process, we emphasised that the paired comparisons were of a ‘full factorial’ design. This means that every item is compared with every other item. So, if you have three items (A, B and C) then there are three comparison tasks: A vs B, A vs C and B vs C. The order of each is randomly flipped to cancel out order effects, so B vs A is the same as A vs B. If you have four items then there will be six measurement tasks: A vs B, A vs C, A vs D, B vs C, B vs D and C vs D.

In effect, we are collecting more information than required if we assume that a person makes decisions according to a logical framework. Consider the case of three items. If a person judges A to be better than B and B better than C – then, according to the assumption of logical consistency, A IS better than C and we should not need to do the A vs C task. However, because we insist on the full factorial tasks being done, we have a measure of how consistent a person is. In the case of three items, one inconsistent rating equals 100% inconsistent.

© Sashkin7 123RF.com

In the course of an Ad-Audit study, each respondent will be doing an absolute minimum of 10 (If there are four attributes and three different ad executions, they would be doing 53) paired comparisons, all randomly presented. The probability of guessing the correct answers to all paired comparisons and therefore being ‘fooled’ that the person has a logical decision framework is infinitesimally small.

We use this information to remove from the analysis those who are very inconsistent. We analyse them separately to try and learn if there are patterns that explain why. So far, we have found different levels of inconsistency in different categories and different types of people. There are exciting new learnings with every project we undertake.

Case studies and learnings


In January and February 2017, we conducted two case studies in South Africa and eight in the UK with Fly Research, our co-developers of Ad-Audit.

Let your local research fly online

Online research isn't just the next thing, it's the only thing. And if you do it right, it's more than just fast and cheap - it gives you accurate, reliable data, even in Africa...

By Leigh Andrews 12 Jun 2015


Reliability


One client commissioned four replications of the same study – each N=300 – but with different executions. The hypothesis was that each study would give the same importance for strategy and executions components, with the following results:



As is evident, there is very little variation across four studies in the same market over a period of two months.

Category-specific segmentation

In calculating additional insights into the advertising tests, Fly Research developed three metrics that are also category specific.



Confused % is the percentage of inconsistent respondents.

Signal to Noise ratio is the ratio of those who were consistent compared to those who were inconsistent – we treat this as the ratio of people who ‘get the message’ and decode it against those who were simply inconsistent and therefore unreliable. We hypothesise that the ratio will be higher in high involvement categories and low in categories like condiments.

Cut-through % is the percentage of people who were consistent but also completed the comparison tasks quickly of all people who were consistent – so they decoded the message quickly.

Cross tabbing these segments by the advertisements tested enables a clearer understanding of the effectiveness of the different executions.

In conclusion, Ad-Audit brings a wide range of objective metrics to help teams create more effective understanding. These new metrics do not necessarily replace any other existing metrics as they can be included in the same questionnaire. Pricing is competitive and delivery is fast as the methodology is applied on the TellUsAboutIt consumer panel of PSA. Surveys can also be redirected to client-owned panels. For more information, please contact me at PSA on 083 255 2668 or send an email to .

About mike broom

I have been involved in marketing research for over 40 years, across all spheres. I started Marketing Science in 1992, Infosense (aka Infotools) in 1995 and Panel Services Africa in 2005. For more information on Ad-Audit, please contact me at PSA on 083 255 2668 or to send an email.
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