In this project, delivered by a multidisciplinary team and using mixed methods, we set out to look beyond the easy stereotypes and engage with the diversity of different people who use powered two-wheelers on the roads.
There’s a link to the full report at the bottom of this page. Here, I’d like to say a few words about the topic of segmentation.
Are there really just seven types of motorcyclist?
Are there really just seven types of motorcyclist? Not six, or eight? Is everyone just one type, or can you be a mix? Can you be one type on weekdays and another at the weekend?
May answers to these questions are: No. Who knows? Not sure. Maybe.
The important point to grasp is that segmentations are NOT descriptions of reality. What a segmentation study like this is saying is: “For certain defined purposes, pretending that there are just these seven types of motorcyclist, in these proportions, will lead you to make better decisions than, for instance, treating all motorcylists as if they were the same.”
The reality, of course, is that every individual is different. Human beings are complex beings, not reducible to a few dimensions or a two-by-two. And human practices are intricate and nuanced cultural entities. There is, we might say, no such thing as a rider, and no such thing as riding: there are a lot of people who engage in activities which happen to have in common the use of a powered two-wheeler.
Decision-making and the value of segmentation
Researchers like me delight in this kind of complexity. A heart, I’m a naturalist: I love nothing more than to wander out into the world human experience and do what I can to record and catalogue its inspiring diversity.
But, unlike me, my clients have to make decisions. How to encourage the use of protective clothing, for example; or how to promote safer behaviours. And from this perspective, diversity is not inspiring: it’s baffling.
Every individual is different: but it is not practical to have a policy, a strategy, a proposition tailored to every individual.
One solution, of course, is to target the average. But in a diverse population, that can do more harm than good. There’s an old joke about a physicist, a chemist and a statistician hunting: the physicist shoots and misses the stag by one metre to the left; the chemist misses by one meter to the right; and the statistician shouts: “We got it!”
Segmentations are a pragmatic response to this challenge. You can’t tailor to every individual. But you can’t pretend everyone is average either. Instead you use the evidence to create a model which will help you get a handle on the complexity.
Segmentations and purpose
A crucial rider (pardon the pun) to all of the above. Segmentations are designed for a particular purpose. They won’t necessarily work for another. Because – remember – they are NOT descriptions of the reality. They are pragmatic and purposive
If you’re manufacturing T-shirts, for example, it makes sense to segment the incredible diversity of human shapes and sizes into, say, S, M, L and XL.
But that segmentation is not going to be so useful if you manufacture off-the-peg suits. And it’s even less helpful if you make shoes.