I started by presenting the Liquality team with a quick review of research methods that would work best for getting us data for our hypotheses. We were calling this a "concept validation" project, but specifically we were looking for in-depth qualitative research that could reveal some product design issues to address. And then pair that info with something more quantitative to help with making a decision about how much effort to invest in further developing the cryptocurrency exchange product.

We decided to do a round of 30 minute interviews with retail crypto investors to get the in-depth qualitative data wanted, and then follow that with a small survey of email subscribers for quantitative data.

The slides below are from a planning doc I put together for the team.

The first round of research was the qualitative study where I developed the question/moderator guide, moderated all of the sessions, compiled and analyzed the respondent data, and created the presentation of findings.

One slightly difficult task with qualitative research is how to correctly identify patterns in the deluge of interview notes collected. Most of the research software solutions I looked into for this problem didn't really address it well, so I went back to my days as a business analyst and opened up Microsoft Excel to hack together a solution. And it worked: I came up with a way to quickly view all responses for a single question or topic, tag and categorize specific phrases, and search and filter that work when I was ready to summarize findings.

Another highlight of this research was a short portion of the interview where quantitative data was incorporated. At the end, I asked respondents to compare and rank a list of generic crypto-exchange product benefits, and that data ended-up carrying a lot of weight in our product validation decision and even Liquality's marketing strategy for months after this research project.

I followed-up the qualitative study with a survey of users from one of Liquality's email lists. I compiled the survey questions, crunched the numbers, and presented the findings for this project.

The slides below are from my presentation deck, which helped Liquality make a decision about further investment in that product.

This was an interesting outcome in the sense that it did not give a strong indication of product "validation" in this case and actually scored lower than another product concept that we tested (also listed in the work section of my home page).

But the findings from this research were still put into practice for strategy, marketing, and product design going forward. It helped a great deal for the team to understand how frequently crypto investors swap currencies, what product values or benefits are likely to get users' attention, and their perceptions and behaviors around an emerging collection of DeFi services.

For example, some of the first lines of copy that you see on the Liquality website were written and placed in that prominent area because I was able to refer back to our research that said our target user wants see those attributes in any new crypto service that they're trying for the first time.