Building entirely off assumptions, Pluralsight's newly acquired Code School was about to be placed awkwardly in the company's broader ecosystem of products. But their high turnover rate and poor performance against new competitors showed me something wasn't right.
For me, it started with a session with the marketing director that pointed to a regular monthly drop-off rate of users. As an education platform, drop-off shouldn't happen until a user is adequately educated or most likely, longer than a month. With marketing unable to explain the drop and the new courses & features built only by sense of smell, I wanted to get us real data & direction. We needed personas based on real evidence.
I started by capturing all assumptions across the company. This also built trust from coworkers to have their steadfast views heard and addressed. I then worked with both my intern and data analyst to turn assumption and question alike into survey questions. We wanted demographics, motivations and all our questions turned into something that could yield us some good quantitative data.
We wanted the ability to cross-segment against education level, occupation, sex, age, number of children, location and even financial standing
One of the main assumptions was that our users were all beginners. Since Pluralsight also acquired Smarterer, we worked with their product team to create a mini skill assessment that could generate concrete data for us.
Without leading, we wanted users to either validate or debunk all company-wide assumptions. Working with Tommy and Chris made sure we could do this while still pulling back clean quantitative data.
Being an education platform, it was important our product was attracting motivated users. We wanted to get high-level insights that could help inform what questions to then ask in our qualitative interviews.
Based on our survey, I worked with our team to build out a script we could use in tandem with our survey to fill in gaps and color our quantitative data. When possible, we tried interviewing in their typical work environments to capture what we could of environmental challenges as well. We also drilled into user's motivations specifically since this could really best be gathered through qualitative means. We knew we could then cross-reference them against our survey findings to color their corresponding personas.
Without any quantitative for direction, we started seeing if assumptions were correct. Incentives ranged from free subscriptions to me having to give a talk to students at my alma mater to get an afternoon with students 😂
Though we did identify a beginner persona, it became evident our vernacular was keeping them far from being our main user. Junior to more advanced developers seemed to be our main demo, joining only to learn the latest language and jumping ship afterwords.
We had an emotional session ❤️with one user that said our platform had changed his family's life for the better. He had changed careers successfully from shop worker to full-stack developer at a reputable company.
The largest segment was our advanced developer who only used Code School as their crash course through whatever latest language was out they wanted to learn. They all understandably left as soon as they finished the course & wished we had an al a carte pricing structure. Interestingly, when told about their junior counterparts, they wished for a way to see examples of their work for hiring potential.
Once we knew our personas like family, what to develop next came second nature. More beginners were struggling with GIT while more advanced personas wished to share or view other's work. An ecosystem wanted to emerge! Or at the very least, a way to start incorporating GIT. We ended up following-through with a feature called "Projects", still feature on Pluralsight today.
One night after a long session of parsing through data, I realized 2 things.
I started by putting parsed out quotes from our interviews on colored index cards. Each color represented one of our four personas. Each persona was then placed with a packet of quantitative findings on a table.
I had everyone that joined the company-wide invite split across the four tables of data. I had Chris our data-analyst ready with his visualizer of the data. Throughout the exercise, anyone could use this to segment quantitative data to get questions answered.
After a briefing of findings & instructions, everyone needed to decide on a photo, demographics, characteristics, occupation, a day in life & age. They also had to chose from the most important quote from the pile I parsed from actual recorded interviews. Persona's also needed names and some even got zodiac signs! ♋
Very rewarding and rather hilarious data-backed persona presentations ensued. Each group explained their reasoning for each detail of their personas. It became evident the new personas had gained support & respect.