City of Ballarat

Data-driven research

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:

• Education
• Income
• Status
• Age
• Marital
• Job category
• Industry
• Gender
• Housing
• Language
• Parent

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 their 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 the council’s need to cater to all of the community warranted all 11 persona archetypes to fully capture the broad spectrum of people within the population.

Community Interviews

The next step was to add quantitive and qualitative data to the Census categories to give them more depth and detail. The method selected to gather this was a survey put out to the COB population. Its structure included qualitative questions such as what council services were used, what media they engaged with and what events they attended.

Quantitive question were things like “What do you enjoy most about living in Ballarat?”, “During your time in Ballarat, what positive changes have you seen in the city?” and “During your time in Ballarat, were there any negative changes that concerned you?”. The quantitive questions were designed to give the Council insights into the digital habits of its community and the qualitative question would be used to develop the personality of the personas.

The survey also included the same fields that were pulled from the census database, enabling us to match the work done by the data scientists with the survey result to create reliable persona groupings.

 

Persona Development

With the survey and census data together we were in a position to synthesise the two together to create final personas. We gathered all of the survey results and ordered them into their relevant archetypes. The final personas pulled data from the Census, quantitive feedback and aggregated quantitive data in order to create a well-rounded snapshot of the Ballarat community.

Information Architecture

After the personas were completed we moved onto creating the site new sitemap & IA. We ran a user story workshop with the new personas working through each one individually and pairing them to council relevant subject areas like payment, support, permission and participation.

Once we had a list of user stories we developed a new site IA making sure that each story was covered. Our final concept was named ‘wider circles’ with the idea being that the main navigation would start focused on the individual (Me), move further out (My Property & My Business) and finally end up holistically (My Community & My City). This was well received and complimented their new citizen-centric strategy.

User Validation

In order to ensure our new IA was highly intuitive and usable, we ran it through a series of tree-tests (facilitated through Treejack) to better refine it with real users. We went back to the original participants used in the survey stage and got them to complete specific tasks on the site, using a text-based IA structure.

We did three rounds of testing, each time refining flows based on our users’ results. By the end of the third round, we had an 80% pass rate for every question asked. At this point, we were positive our IA & sitemap would work for the community of Ballarat.

From here we were able to hand over the work to the agency in charge of the site’s delivery, confident that the navigation and finding have a solid and tested foundation.