Personas: A case study
New York Cares trains and distributes volunteers across New York City. It designs, hosts, and then tracks outcomes of thousands of volunteer-run programs. These programs happen in multiple cause spaces, including hunger, college access, and seniors.
New York Cares’ website acts as a marketplace in which volunteers match themselves with opportunities created by the organization.
The organization has a trove of data on its programs and its volunteers. This tells it how its volunteers behave, but not why.
The organization needed a better understanding of large-scale user patterns. An educated hypothesis, the personas could hopefully save development time across departments and allow teams to avoid returning to foundational research with each new initiative.
• Ran qualitative research: recruited participants, conducted and tracked research
• Combined the qualitative research with data to create and refine segmentation into personas.
• Introduced and supported adoption of personas across the organization.
The work of the user-centered design experts Alan Cooper and Indi Young provided solid guidance. Both prioritize qualitative research from the outset, blending it with data as the persona segments evolve. Both also sharply avoid relying on demographic clusters that are often oversimplified and introduce bias.
I threaded persona research through a variety of other design/development cycles, consistently including persona-specific questions across different research initiatives. The organization evokes lots of good will, which consistently makes for smooth recruiting of research participants.
This allowed me to build up a body of research to draw from. The original segments evolved as the research progressed.
In an earlier research project, I explored the different goals our users had for volunteering. This project revolved around matching the language used to describe volunteer opportunities with volunteer mindsets. This research revealed a hierarchy of volunteer motivations and provided a solid base for developing personas.
The next step was to overlay user’s unique behavioral patterns on top of this framework of goals. Indi Young recommends “Pay attention to the main ways in which people behave differently when engaged in what your product will solve”.
In the New York Cares’ ecosystem, time is everything. The organization promotes a veritable smorgasbord of volunteer experiences, all during the week and across all five boroughs.
Volunteers have wide agency, and their choices reflect a great deal about them. Some spend lots of time, some volunteer intermittently, or only once. Some volunteer at a wide variety of projects, while others graze lightly around, and still others only at one place
I used a series of spreadsheets to track individual patterns of time-spending behaviors. I looked at:
• Overall volume: how many projects had a volunteer completed?
• Cadence: Did they volunteer weekly, monthly, annually, episodically?
• Day of the week
• Time of day
• Speed of engagement: how long was the path from volunteer training and approval until engagement in volunteer projects?
• Types of projects (although I weighted this less, because often volunteers discover a project by chance while browsing, then stick with it out of habit)
•Locations of projects
The combination of this data, combined with some of my qualitative research, began to reveal clear patterns. As I worked, I developed nicknames for the patterns. Most of these nicknames represent traits that were eventually rolled into bigger patterns. For instance, the following were rolled into “This is the New Me”.
“I’ll go Anywhere, Anytime!”
“This is my New Job”
During the sorting and affinity process a cohort of 6 personas naturally suggested itself. In the end, each persona is differentiated enough from the others to make a useful comparison across multiple traits. At the same time, the entire cohort is both generalized and plastic enough to be useful in most use cases.
The tech group has used two contrasting personas to develop tasks in a card sort. We are using the results of the sort to refine information architecture on our website. As well, I used these contrasting personas to prioritize screen real estate for a user dashboard in a new product.
At this point most of the organization has been exposed to the personas through presentations, a staff-wide newsletter. A few departments are using them. The development team, for instance, has used them as a platform to refine email outreach segments.
These personas are a snapshot of where the organization was at a point in time. The organization has had a stable volunteer base for a long time. It is pushing into a different model of volunteer engagement, particularly with the outer boroughs of the city. These changes may influence the current range of personas.