Why does observational data also enrich post-tests?

Recall is still the basis of the 'post-test'. However, today we have the technology to identify actual exposure to campaigns.

Apr 27, 2022

Post-tests - in their various forms- are still one of the most solidly established tools in the advertising and media world for understanding how a campaign has worked in qualitative terms and what its brand impact has been.

"Do you recall seeing this ad? Where?" The reality is that when it comes to identifying the exposure that a campaign has had, all analysis of this type continues to resort to the user's recollection or memory. This approach is the one we’ve been using for over 30 years (in hall test, CATI, CAWI...) and, although the methodology used has changed over the years, the tests’ internal structure and operation have not.

However, today we have the technology (and the ability) to identify each panelist's actual exposure to a campaign with no need to ask what they recall seeing, when they think they saw it or on which medium or channel the impact took place. This, in addition to providing us with key information about the role of each media in generating incremental reach and frequency in our targets, can also greatly enrich these post-tests.

With just a quick analysis, we can identify several advantages of incorporating this observational measurement into post-tests. Here are a few:

  • Stop relying on consumer memory as the sole source of information on exposure.
  • Better understand how memory is constructed. How is it possible that some campaigns generate greater recall than actual exposure to the campaign? In conventional post-tests, some brands generate very high degrees of recall even among people who´ve never seen the campaign. Why? This is due to the strength of certain brand cues, which are very well-known and "damage" actual campaign recall. With our methodology, when asked whether they remember having seen a campaign, we reliably know whether they’ve been exposed to it or not, and can differentiate results between those actually exposed and those not.
  • Simplify and greatly reduce these questionnaires by eliminating everything related to where or when the impact occurred; this benefits the panelist who’s not overloaded with long, complex surveys that test their endurance.
  • Focus the questionnaire on aspects that truly enrich the information we obtain from it.

By themselves, these advantages represent a very significant difference with regard to traditional post-tests. But if we take into account that, thanks to our ACR technology, we have real data on impact and where it occurs, or to put it another way, we have all the “grays” on exposure (how many times, where or in what time slots it occurred), we venture into territory that was previously unexplored - except by performing complex mathematical functions and calculations -again based on recall-: and can now find out how many times a user has to be impacted to mobilize brand awareness, consideration, image or preference -in other words, the most important brand KPIs.

Not only that but we can also discover the optimum combination of impacts in different media (and moments) to generate this response and which channels are the most effective for generating one reaction or another.

What’s more, because these post-tests are performed on online panels (CAWI), they can be compared to Brand health/Tracking data to serve as a base level for brand KPIs.

As you can see, observational data represents a qualitative leap in post-test analysis of cross-media campaigns: it frees us from recall dependency, allows us to carry out better, shorter questionnaires, is useful for us to do stand alone post-tests or as a complement to the research portfolio carried out on brand health, and, above all, it allows us to do much more sophisticated planning analysis to find the most efficient formula.

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