In previous posts, we’ve explored explicit and implicit methods of undertaking a driver analysis.
New to driver analysis? In short, driver analysis involves researching how people choose one brand/option in a category over another, by measuring the relative importance of brand/option attributes (drivers) in making choice. Learn more in our full introduction to driver analysis.
The final set of approaches we would like to discuss are hybrid approaches. These use a combination of stated (explicit) and derived (implicit) approaches to classify drivers rather than create hierarchies.
Why would you want to do this? Well, the answer is that a classification of drivers is, in many cases, actually more useful than a simple hierarchy because it potentially reveals how you should treat each attribute or driver and provides a lot more room to play with your product claims and marketing communications.
All of these methods attempt to tap into not only the importance of the attribute at a functional or rational level (explicit), but also at an emotive or subconscious (implicit) level by comparing stated importance scores to derived importance scores for each potential driver attribute. We refer to these techniques at D&M Research as “Im-Ex” as they represent both the implicit and the explicit.
Im-Ex Quadrant
Quadrant is the simplest of the hybrid approaches and is loosely related to the Kano model of understanding customer preferences. It uses a simple stated importance measure but then adds a derived measure of importance and crosses them to create four quadrants of drivers. Remember, the derived measure is created by linking driver attributes associated to the things being tested (“Which car brand is X, Y, Z?”), to a propensity measure (“How likely are you to buy car brand X in the future?”).
The process involves a number of steps:
- First, we standardise both metrics (stated and derived) so that they are expressed in terms of comparable units with a mean score of “0” – this then enables us to compare and cross each metric on the same scale.
- We then cross them on an x-y plot, noting that we now have four quadrants based on the intersection of the mean scores on the x and y axes.
- We can then classify the drivers into four groups.
This is a better and more discriminating technique than other methods as it provides not only a hierarchy of drivers, but also the following four classifications.
To see the Im-Ex Quadrant classifications, download our Driver Analysis Guide from our Resources page, and understand how you can answer the ultimate marketing question in any category – how do choosers choose one brand or option over another?
The downside of this method is that it tends not to work as well in markets where customers/users do not have a good knowledge and/or opinion of the competitive set.
That said, this is still a very useful method, particularly in markets where there is strong competition and customers/users are likely to have formed opinions and/or have experience with a number of competitors.
Im-Ex Polygraph
The Polygraph is the final hybrid method we want to discuss and uses similar data (implicit and explicit driver metrics) as the Quadrant method but now adds the concept of “attractors” and “detractors” to create a more sophisticated method of classification.
The Polygraph is a D&M Research proprietary method made popular following the multi-award-winning presentation of the case study “What Women Really Want” at the 2010 AMSRS Conference (AMSRS: Australian Market & Social Research Society, now The Research Society). (Check out the follow-up study, “What Men Really Want From Women“, on our blog.)
The method has now been widely adopted and used by D&M Research across many categories including the broadcasting, FMCG, construction and education sectors.
As mentioned, the real benefit of the Polygraph is it now adds the concept of attractors and detractors, as well as four new classifications based on understating and overstating. There are “areas of compromise” (attractors that consumers are willing to go without if other things are present), “areas of tolerance” (detractors that consumers are willing to put up with if other things are present), “areas of delight” (attractors that are more appealing than they say) and “areas of disgust” (detractors that are more off-putting than they say). This method, therefore, purports to more accurately reflect how we truly make category decisions, trading off pros and cons both explicitly and implicitly to get to a final decision.
The method is based on the following premise:
- It can be argued that we cannot always introspect and articulate what motivates us to think and behave in certain ways.
- It follows, therefore, that we may not be able to accurately articulate what it is that attracts or turns us off in a particular category.
- However, by comparing what we say we want (or don’t want) to what we are actually attracted to (or turned off by), we may be able to reveal areas of attraction (or detraction) that we cannot articulate, thus creating a more comprehensive picture of what we really want.
The Polygraph essentially compares what people say they are attracted to or impressed with (or not) – for example, in a car brand (stated importance) – to the features of car brands they are actually attracted to or impressed with (or not) (derived importance). In doing so, the analysis takes into consideration both cognitive biases and less spoken motivators.
To learn how we generate the Polygraph output, download our Driver Analysis Guide, and understand how you can answer the ultimate marketing question in any category – how do choosers choose one brand or option over another?
As it turns out, the Polygraph in practice is a research output with a lot of utility because it enables a brand to focus on the core attractors and “wow” factors it can deliver, while worrying less about other areas that it might not have, but which it now knows fall in the “nice-to-haves” bucket. Conversely, it can avoid the areas that really detract (core detractors and “whoa” factors) while worrying less about some negative areas which, although detractions, turn out to be not as bad as stated. We have found this to be true across numerous categories and among brand custodians such as CEOs, CMOs, COOs as well as insights professionals.
A final word on driver analysis
As we have seen, there are many ways and methods to do driver analysis from the most simple stated importance exercise where we get in-category respondents to rate the importance of certain attributes using a simple 10-point scale, to the more complex such as choice-based methods using maximum difference scaling and, of course, derived methods which link product attributes to a dependent variable such as purchase propensity or product satisfaction.
The chosen method will depend on many things and, of course, budget and timing contraints. In a perfect world, we would always move beyond just rated importance or rankings to ensure that we are tapping into consumers’ minds beyond just the rational thinking (System 2) into something that better approximates how choosers really do choose in the real world, incorporating our emotional and cognititive biases and use of shortcuts and hueristics (System 1 thinking).
We trust that this guide has been enlightening and useful in your quest to understand what we believe to be the most fundamental question in marketing and market research ― how do choosers choose?
For a detailed guide to the methods used in driver analysis – and the pros and cons of each – download our FREE printable Driver Analysis Guide by completing the form below!