I was hired by Ink & Switch to conduct a study into the audience of generative art creators, particularly those who use Processing (generative art is, basically, making art through computer programs). The research was intended to be broad and exploratory, but a key aspect was identifying unmet needs, e.g. pain points with existing tools or user segments that were not well-served.
We defined the following research questions.
How do makers of generative art label themselves? What keywords do they use?
Is there a single audience of people known as "generative artists” or many subgroups?
Is it a hobby, a side gig, or a full-time job?
What is their skill level?
Do they see themselves as artists, programmers, or something else?
What is their motivation for learning Processing or other generative art tools?
What is their creative philosophy?
What are their long-term aspirations?
Experiences with generative art
What are the biggest barriers to new artists getting started with Processing?
What tools do they use today? What computing platform(s) do they use?
What tools have they previously tried?
What are their biggest pain points with their existing tools?
Where and how do they publish their work?
I first explored the domain online, looking at:
General information about generative art and its history
Communities where generative artists hang out (Conferences, message boards, etc.)
Tools currently in use, tutorials about them, and details about their creators
High-profile people in generative art
I also sought individuals who had written or talked about their experiences with generative art, and processed their information similar to user interviews.
Based on the goals of the project, we chose to recruit participants who had created generative art as a hobby, but were not experts or professionals. I spoke to five people about their experiences with generative art: two were friends of the client, two were recruited from design social networks (Dribbble and Behance), and one was recruited from the Processing.org forum.
I processed the collected data using an affinity diagram in an online tool. I broke down each set of interview notes into chunks of data, which I wrote on note cards in the tool. I then moved the note cards around the digital whiteboard, looking for patterns and similar sentiments. I gave labels to the groups of note cards that emerged. These labels and their supporting evidence (the note cards) eventually became the set of insights presented here.
I can't share the details of my findings here, but broadly they showed the key pros and cons of the most prominent generative tools (Processing, p5.js, and OpenFrameworks), the importance of publishing work online, the fact that many users are generative artists as a hobby and designers as a day job, and that their needs are not being fully met.