In Part I of this blog series, we examined some of the practical issues with the use of standard normative data for providing context for advertising research. While having the global databases and expertise to provide traditional database averages as the situation warrants, MSW●ARS also offers approaches to the question of context that avoid the issues which plague traditional norms, providing more meaningful points of comparison that decision-makers can rely on with confidence.
The Fair Share Benchmark
A vital consideration in applying norms to communications research metrics is that the metrics themselves should be meaningful, allowing for comparisons to benchmarks to yield actionable insight. One way a metric can be considered meaningful is if it is predictive enough of in-market sales effectiveness to be useful as an overall success criterion. While most commonly used metrics fall short of this standard, the MSW●ARS CCPersuasion® metric (a behavioral measure of the change in percent of brand preference taken before and after incidental advertising exposure) was described by Quirk’s Magazine as having “been validated to actual business results more than any other advertising measurement in the business.” As an example of its utility, its track record in predicting matched-market advertising weight and copy tests far outstrips other metrics:
It has also been shown to predict actual sales volume impacted by an ad, as determined through marketing mix modeling:
The CCPersuasion score is not compared to a category average. Instead, to provide meaningful context to the question of whether an advertisement has attained an acceptable CCPersuasion level, it is compared to the Fair Share benchmark. This benchmark represents an estimate of the sales effectiveness, in terms of CCPersuasion level, for a typical ad for the advertised brand, given the category environment and the brand’s position in that environment. It utilizes a model which was derived from the results of tens of thousands of advertising tests and which has been proven to work over the course of several decades. Essentially, it utilizes brand and category market structure factors that have been shown to be related to higher or lower CCPersuasion levels. These factors include:
- Loyalty: In any given category, some consumers are, to varying degrees, susceptible to switching brands. In general, the more consumers susceptible to switching brands, the higher the sales effectiveness of advertising in that category.
- Number of Brands: Categories differ in terms of the number of competing brands. More brands mean more competition for non-loyal consumers, which results in a lower expected level of advertising’s sales effectiveness.
- Brand Strength: The larger a brand’s share, the smaller the pool of non-loyal consumers available to switch their preference to that brand and the more difficult it is to achieve a given increase in brand preference.
Vitally, these brand and category factors are collected as part of the MSW●ARS Touchpoint methodology which allows it to avoid the pitfalls inherent in category averages which were discussed in part I of this series:
- Availability – While category averages depend on the availability of sufficient relevant historical data, Fair Share is always available even for new or emerging categories since the inputs are a product of the testing system itself.
- Consistency – Category averages can be influenced by methodological differences between the current test and historical testing. Fair Share always reflects the brand’s specific test situation by only using information from that brand’s testing as inputs to the model.
- Representation – Category averages can vary greatly depending on what brands are included in or missing from the normative data set. On the other hand, Fair Share is stable since its inputs have been proven to be reliable in their collection. Plus, the model has been derived on and refined from tens of thousands of cases for brands in nearly every conceivable situation and so can be applied to any brand with confidence that the benchmark is appropriate.
- Brand Development – Category averages provide a single normative level for all brands, despite the fact that different brands can and do have very different situations that affect the potential strength of their advertising. In contrast, Fair Share is always based on current marketplace conditions and the brand’s specific position in the category. So each brand has its own unique benchmark level commensurate with realistic expectations for its advertising’s sales effectiveness.
How do we know that a brand’s Fair Share level is truly “fair”? Fair Share levels are closely monitored over time to ensure that average levels closely match average CCPersuasion level.
The average Fair Share level doesn’t just match average CCPersuasion overall, but also at different Fair Share levels as illustrated in the following chart. This shows that Fair Share effectively captures the factors that tend to result in higher or lower CCPersuasion results and that the benchmark is “fair” in a wide range of brand circumstances:
Furthermore, Fair Share explains 64% of the variance in CCPersuasion results across brands and categories, indicating that it effectively reflects each brand’s unique situation.
MSW●ARS pioneered this modeled normative approach and has unsurpassed expertise and systems in place to assure that the Fair Share benchmark continues to be the gold standard in the communications research industry.
When it comes to looking for insight into what is driving an ad’s performance, it is typical to look at diagnostic metrics in relation to historical normative averages and assume that those elements eclipsing normative levels must be driving an ad’s success. However given the issues with category averages, these assumptions can be erroneous.
This leads us back to the second way a metric can be considered meaningful – that being, it is specifically related to the brand or category in such a way as to guide revisions or future developmental work. However, most common metrics for which normative data is typically available are too general to provide specific guidance to the brand, while those attributes and equities directly relevant to the brand or category often lack robust normative data sets.
A contextualization approach which would provide meaningful feedback needs to be inclusive of all diagnostic elements which a brand considers important enough to include in its communications testing research initiatives. As with Fair Share, the MSW●ARS approach is to assess attitudinal metrics using context derived from the testing methodology itself, allowing for application to all diagnostic elements included in the survey.
This approach is possible within the MSW●ARS Touchpoint methodology since both CCPersuasion and attitudinal metrics are collected from the same sample. This allows us to analyze attitudinal measure performance between those study participants who changed brand preferences and those who did not change their preference after exposure to advertising. This makes it possible to derive the importance of each attitudinal factor in the actual performance of the piece of copy or campaign.
As with Fair Share, this Derived Importance approach obviates the availability, consistency, representation and brand development issues associated with traditional norms due to the assessment of importance being internal to the methodology of a single communications research survey for that specific brand. Furthermore, the results are both complete in scope and meaningful since all metrics are covered and the assessment of importance is based on preference change from the CCPersuasion metric which we have seen is strongly tied to in-market sales performance.
Furthermore, the importance of each attribute can be crossed with attribute performance levels. Such a plot, as in the example below, can reveal areas of important strengths as well as, most vitally, perspective on potential areas for improvement that a brand can use to guide revisions to copy or as input to future initiative development.
As parents, we need to know how are children are doing as we attempt to help them develop and fulfill their potential. In doing so, we depend on benchmarking to academic, developmental and societal norms to help us understand how they are doing. Similarly, as marketers we are concerned with developing our brand’s potential and need appropriate context to ensure our communications initiatives are delivering all the support our brands deserve. In each case, it is imperative that context is meaningful, relevant and unbiased to avoid taking misguided or even detrimental actions.
To learn more about the MSW●ARS approach to providing appropriate context to your brand’s communications research results, please contact your MSW●ARS representative.