Jessica Davis
Amazon
Jessica Davis is a UX researcher for Amazon Business (which includes the procurement and shopping space), responsible for a customer experience tracking survey and some of the team’s research op responsibilities.
Research ops must also keep you pretty busy, lots to manage. Could you take us through different types of research (primary quant, primary qual, secondary, or any of the different types of sources) that you're trying to manage for your team?
We are comprised of both user researchers and market researchers. So our user researchers have skill sets across both methods.
Our user researchers are primarily working with product teams, working on usability testing interviews with end users to measure ease of use and create better designs. Our market researchers are supporting the marketing org and doing a lot of quant research, like brand tracking and concept testing with advertising, that sort of thing.
So market research in the sense of literally helping marketing more than broader market – interesting!
Curious how this lands. We've heard from a few different people we've spoken with today that there are a couple of different buckets of research:
Big projects: You know what you have to tackle. You kick things off, you get going and then you know how you're going to use it.
Ad hoc: Somebody asked you something you need to prep quickly before a meeting.
Always-on: Something that you're constantly monitoring.
Curious if those resonate? Does your team tend to lean towards one versus the other?
Yes, it does. Each researcher is embedded in their own team and they're responsible for their own road map, so to what degree they do more strategic work versus more iterative work is of native to the team and the researcher.
But we try to do a pretty healthy balance of both. We try to plan out our research agenda in advance, but obviously there's always kinds of high-level requests that come in last minute.
A couple of last questions, some quick ones for you!
When you think about AI and research, what word comes to mind?
New. We’ve heard here [at IIEX] there's a lot of interest and a lot of conversation around it. And I think everybody has a different feeling about it, but it's exciting and I’m just interested in seeing what it can do.
When you think about one concern or fear you might have when it comes to AI and research, what comes to mind?
For me personally, just kind of lack of knowledge on how to use it. Where it can come in, where it's best used, and where to steer clear.
Last question!
When you think about all of the ways you can collect information from what's publicly available, you can Google, you can use LLMs…, I'm curious for the word that comes to mind around how well those different solutions do the job for you.
I would say pretty well, as a place to gather information as a starting point. Google obviously generates a lot of information at your fingertips. The AI models that I've tried put it in a more cohesive narrative. It's a great place to start and get ideas.
Super helpful, thank you for your time!