There are many different methods you can potentially use to undertake a quantitative driver analysis, and they fall broadly under two categories – let’s call them explicit and implicit. In this post, we explore explicit methods and evaluate their merits and demerits.
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.
Stated importance
The most common of all driver analysis techniques is what we call the stated importance measure. As the name suggests, it’s simply asking how important each of the attributes is, and expressed as something like, “How important are each of these to you personally when considering product X?”
These are usually collected with a simple rating scale such as a 10-point scale (1 meaning “not at all important” to 10 meaning “extremely important”) and are simple to collect and analyse using nets – e.g. top 2 box (9-10) or top 3 box (8-10) – means and medians.
The problem with this method, though, is that there can be a tendency for everything to become important, especially if you have undertaken qualitative research to determine your driver list. Once presented with these as options, the consumer says, “yes, that’s important too,” and gives all of them a high score.
The method will generally enable you to create a hierarchy of drivers but can fall down because it can miss hidden relationships between drivers, may not pick up less articulated drivers (drivers consumers associate with brands they like but don’t overtly state as important – more on this in th next blog post), and can lead to less discrimination of drivers (for example, where everything is seen as “very important”).
We generally don’t recommend using stated importance on its own for these reasons. It can be difficult to know where to put your precious marketing and product spend when many drivers are all rated of equal importance, when in reality, if there was a forced choice, they might not all be equal.
Stated reduction method
Another stated method that partially gets around this is to first ask a respondent to select everything that is important from a multi-choice list and then serve only those selected again and ask the respondent to select the most important (see below). You do get the trade-off of three tiers of drivers (“most important”, “important” and “not important”) but, again, you can end up with indiscriminate driver lists at the top (e.g. if three are chosen as most important).
This is where ranking might be useful.
Ranked importance
Ranking is another popular stated method of importance. This technique simply asks respondents to rank the order of importance of a number of driver attributes, say, from 1 to 10, where 1 is the most important and 10 the least (see below for an example). This is a very popular approach amongst novices, and although at face value it seems a valid and useful approach, it suffers from a single but potentially very misleading flaw.
The flaw is that we have no idea about the distance between the ranked items in terms of importance. Just because items are ranked 1st, 2nd, 3rd, etc., it doesn’t mean that they are equally spaced in terms of importance. In the example above, the respondent has selected “low emissions” as the most important driver – and that’s pretty clearly the number-one attribute. Then they have selected “high safety rating” and “extended warranty period” as 2nd and 3rd most important respectively.
But how do we know how much more important each of these is from the one before? The assumption is that they are of equal distance but, in reality, they may not be and most likely are not. It may be that “low emissions” is in a top tier of its own and three times more important than “high safety rating”, which itself might be just a little bit more important than “extended warranty period” – see the problem? Although rankings can be useful to create some sort of hierarchy of drivers, they are flawed in terms of magnitude, and for this reason are generally advised against by most professional researchers.
In our next blog post, we’ll have a look at some more complex implicit methods of driver analysis, which tap into and bring to light the more automatic – or heuristic and passive – ways that we make decisions about things. Stay tuned!
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!