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Moving Minds is a groundbreaking study that groups home movers into tribes according to their move motivations. It explores what these motivations tell us about each tribe’s mindset and investigates the behavioural psychology behind how this impacts decision making and purchasing behaviour. Undertaking the research behind Moving Minds was a vast job, so we engaged our research partner, Ragdoll. Ben Taylor, Research Director at Ragdoll, shares some detail on the methodology behind the Moving Minds data collection, the uniqueness of the study and what he found most surprising when analysing the results.
Tell us a little bit more about the methodology behind the Moving Minds.
We started the process by painstakingly reviewing data that already existed on home movers. This achieved two things: a) Ensuring that we didn’t cover ground that was already well explained. b) That we could use some of the government data to show us the official demographic landscape of moving home, as well as the proportions of those moving to rent vs buy. Once this step had been completed and the questionnaire developed (this covered all manner of questions from personality to the nuts and bolts of moving), we went into fieldwork using what we term as Nationally Representative (Nat Rep) Entry. This means that the sample we put into the survey is perfectly balanced in relation to the UK census – so it is the right number of males and females, the right spread of ages, across an appropriate spread of regions in the UK. Once we had the appropriate people in our survey, they went through a screener to understand whether they were in the process of moving or had very recently moved. Those that didn’t meet our requirement were screened out and what we were left with is a sample that gives the most honest view of the moving market – a snapshot of that particular moment (it also proportionally matched government spreads for moving and renting, which further validated our approach).
Once that data collection had finished, we moved on to the fascinating task of segmentation. We use a multi-modal segmentation technique. Eventually, we settled on an 8 cluster solution (at the time, giving it a catchy name of ‘R6-8CL’, meaning that this was the 6th iteration of the segmentation we had run and the 8 cluster solution within it). Selecting a segmentation is when science meets art. Statistically, we are presented with 3-12 cluster solutions each time we run the segmentation, and technically, each one is valid as a segmentation. But, we have to review the data each time and add a more ‘human’ lens – does it make sense? Is there too much crossover between some segments? Do the groupings make logical sense? And most pertinently, is there a commercial output to be had from the solution? It took us two weeks to find our winner, and another week to refine and understand it before making out the selection. Once the segmentation is chosen, it is about building the personalities in the simplest, most engaging manner, ready for their first share.
Have you ever done anything like this before in terms of looking into the why behind a home move?
This isn’t just a first for us in terms of looking at the home moving market in this manner, it is also the first of any project of this kind – one that looks at the home mover market more deeply than just renting vs buying, or adding a demographic layer. We instead showcase that the reason, the driving motivation behind why someone moves, is more important than their age, location, or whether it is a rental or purchase. This is key for any marketer or brand that can sell to this audience; the stimulus of moving is a great trigger for us to leverage. These are people who are about to make significant behavioural and purchase changes to their life, and if we can understand their mindset, our offering and approach to the market become all the stronger for it.
As someone involved from the very start of the data collection, what was the most interesting thing to come out of the research?
One of the most interesting things to come out of all segmentations is the depth of the data. In every segmentation we do, we move from basic terms and simplistic thinking to a much richer narrative. Everyone knows that ‘Millenials’ and young people (in general) don’t have as much money as the previous generation at this point in their life, and that their best chance to get on the ladder is through the ‘bank of mum and dad’ – this narrative is tired, as well as full of idiosyncrasies. This segmentation moves the conversation on and leaves behind the tired moniker of millennial, and instead looks at this key moment in their life. A segment that I found particularly interesting was those who simply hate the place they currently live in. We saw an over-index of young people in this segment, but to us, the more interesting point is what this move would represent – a fresh start. This is a marketer’s dream, as this segment is primed to start again, to question their existing habits and try new things. Tie this segment with the right message, and this becomes a huge opportunity for brands.
Now the research is complete, how quickly can a home mover be profiled into one of our Moving Minds tribes?
It only takes 30 seconds to get someone allocated to the segmentation via our algorithm. We have taken the statements and factors that were most divisive in the entire survey and used them to create a boiled-down version of the work – this is accurate to around 90% (on average). This means that you can roll this out across a database using a very quickly. For example, before we would have had a 32-year-old female, that we know has at least 1 child based on previous purchase habits. Once allocated, we will know what her psychological drivers and attitudes are. We will also know how she defines herself, as well as some of the things she is likely to want to purchase for her new home.
Ben Taylor is a Research Director at ragdoll research, where he specialises in quantitative research and matching data with commercially focused outputs. After nearly a decade in the industry, he has developed a reputation for taking the often complicated world of data and insight and boiling it down to simple, actionable, business-driven points. During his time in the industry, he has worked with numerous brands across the FTSE 250 and companies across the globe – with segmentation becoming a core specialism.