In order to understand the Ballarat community we first had to identify what a cross-section of the population looked like. Our solution was to look at the 2016 census data on Ballarat as a comprehensive starting point for our categorisations. Working with two data scientists, we used K-mode clustering algorithms to segment the population using the following data points:
- Job Catagory
The K-mode clustering converts the data into dimensions and attempts to find the centres of the regions of highest density. This means that we can find clusters that are large enough to be statistically relevant while also being different enough to warrant its own persona.
At the end of the exercise, we identified 11 persona archetypes that could be used as the foundation of our user research. Although 11 personas would usually be considered a lot we felt that, as the councils need to cater to all the community (which covers a broad spectrum of people), that the 11 final archetypes were needed to fully capture the populations’ variety.