What's a Quantitative User Experience (UX) Researcher, 2025 edition

Wow it's 2025! Well, within 24 hours of this publishing of this it'll be. So you can think of this as both the final post of 2024 and the first of 2025 because time and clocks are confusing.

It's been a long while since I've written anything about the job title that I've had for the past * checks calendar * seven years, 2022, and 2020 being the last times I wrote about it. Since then, there have been several "Quant UX Con" conferences, and steady growing interest in the field as well as adoption of the job title itself in companies. I think it's about time I updated my thoughts on the role since, as with all things, the collective understanding of the role continues to evolve as all the rest of the world itself evolves. The overall gist hasn't really changed, but the edges shifted a bit.

What's a Quant UX Researcher?

The easiest way to understand the role of qUXR is that, as the abbreviation I used suggests, it is a branch of the UX Researcher (UXR) job family. The primary reason that UXR roles exist is to do research on users of a product or service. The knowledge and insights that are created by the work of UXRs is then typically used to help people design and improve products and services. If you buy the assumption that any product is purchased and consumed by a user to fulfill one or more needs, then having an understanding of what those needs are is a fundamental requirement in creating successful products.

Traditionally, user research methodologies drew heavily upon social science. A lot of methods and practitioners would come out of places like sociology, anthropology, human factors, HCI, as well as places like industrial design. While quantitative methods like surveys, experiments, and statistical analyses were always used in both user research and the social science fields it drew methodologies from, the focus always remained on the humans. That natural compatibility is why many current UXRs have degrees in those fields.

The "quantitative" modifier was added to the base UXR role in the 2010s with the advent of big data and it became possible to aggregate and analyze information about users at unimagined scales. The problem was that the new methods of analysis required a significantly different skill set than the traditional UXR had, especially in the early/mid 2010s when even the term "data scientist" hadn't been invented and popularized yet. So in order to hire for people who had the programming and statistical skills to do these analyses while still being user-focused enough to care about user behavior, a new job title was created.

Even now after over a decade of people taking on the title, qUXRs are still UXRs. They're people who care deeply about who and what users are, and they care about making user's experience with products better. Their methods and tools just significantly differ enough that they needed a separate hiring process to find the right people.

What's the work entail?

"Understanding users" is a task with no end. There's always new users and the world is always changing. But most user researchers that I know aren't just given carte blanche to do whatever study they want while still pulling a paycheck. So what are they usually doing?

At the core, they're usually trying to understand the people that their employer wants to make money off of. Usually for people who are not current users, that's considered the domain of the folks in marketing because non-users don't have as much data about them. But qUXRs can call anyone who DOES leave data within internal systems fair game for analysis.

The specifics of what a qUXR will be studying will depend on what their employer wants or needs. Sometimes it will be analyzing survey data because whatever it is that's being measured can only be measured with one, like customer satisfaction. Other times, it'll be looking at data logs to understand user behavior, like whether users get lost in navigation or have trouble with an operation. Sometimes it'll be designing experiments. Sometimes it's figuring out if a weird behavior uncovered by qualitative UXR work is happening at scale. Other times it's data-mining logs for unusual behavior patterns and figuring out why users are even doing that with the help of qualitative methods. Yet other times, it's more fundamental questions like "what kinds of people make up our customers? Who are these people and can we find more?"

You'll note that all these kinds of work has overlaps with responsibilities that are often found in other job titles, for example data science or market research. This is not a mistake. What I've learned over the past couple of years is that organizations that adopt the qUXR job title actually have different interpretations of what that job.

But the work is still focused on the user

The end of the day, a UX Researcher role is focused on understanding the humans, the users, of a system. We might know the techniques for maximizing profit through the use of dark patterns and experiments, but that's NOT supposed to be our job. We're here provide insight into what users want and need, under the assumption that customers who actively choose to use our product over the competition because it does what they need better, is good for the long term health of a system. Many good UX organizations out there have similar viewpoints – it's why they spend lots of energy designing good interfaces and service flows. There are plenty of other people in an organization who has the explicit charge to maximize revenue and profit, and so we don't need to put energy into furthering that particular viewpoint.

The extent with which UX teams have the power to speak up and push back on pure profit-seeking behavior is of course dependent on a lot of external factors, but that's the ideal.

A question of path dependency

The past few years, I've had more opportunities to speak to Quant UXRs from other companies and organizations more, and started seeing interesting organizational patterns.

A very common question people ask is "what is the difference between a qUXR, a data scientist, market analyst, product analyst, or whatever?" And the answer is very often "very little aside from a focus on users". In terms of technical skills, they are very similar. We do data modeling, engineering, survey analysis, data collection, mess with ML, build dashboards and make recommendations. There is often greater variation within individuals of the group than between the different groups.

And so, given that people in all these data roles are maybe 80% skill interchangeable, how are their job roles and titles differentiated? The answer is, according to historic need. Every organization has a list of data needs they have to take care of, from production data engineering and ML systems to back office analysis for product work. People are hired under whatever data job title to knock items off that list and those become their work domain. As more and more data handling folk get hired on to take up tasks that no one has had time to take before, domains of expertise get marked.

So what happens with qUXR being a relatively new job title that gets adopted into an organization is that the niches of data work left to do are usually some neglected subset at the time the role was created. At one place, qUXRs are primarily focused on doing survey related work, and getting access to logs data for analysis is hard because that is the domain of the data scientists who had been around much longer. Meanwhile at where I work in the Cloud, we do a bit of everything because qUXR joined the organizational structure early on, to the point where we cover some things that other analyst or DS teams would have traditionally taken. Other places have their own unique historical quirks.

These weird turf situations can be just org chart distinctions where everyone is friends, or they can be jealously guarded domains of responsibility. I've seen plenty of examples of both and mixes in between. That, again, depends entirely on the nature of the workplace and people involved.

So, it's more job description reading

One of the things I've seen people express dislike for is how you now have to read Data Science job descriptions extremely carefully to try to figure out just what sort of data science you'd actually be doing. Maybe it's a job involving predictive analytics, or it might be dashboard hell. It might be product-focused, or it might be 100% building models.

A similar, but less extreme, sort of situation exists for Quantitative UX Researcher positions. Since there's so many different problem areas we could be asked to engage with, there's a decent chance that your particular strengths might not be completely aligned with what a given company is looking for, and you need to pay attention and ask detailed questions during interviews to check for fit. For example, I might know how to run a decent survey and analyze the results, but I'm not very excited to work with surveys all the time. Would I be able to do the job if I really needed the paycheck? Sure. But someone else would be far happier and productive in that position.

Go check out things like Quant UX Con if you're curious

With all the AI Hype going around the past few years, the oxygen has largely been sucked out of the room for talk about other aspects of data work. So if you're like many of my colleagues and are more interested in thinking hard about the complex, inconsistent creatures known as "human users", consider looking at the job title. There's still not very many companies that specifically use the title, but pay attention to data science roles that specifically aim to help product managers and engineers make decisions about new features, that's just qUXR under a different name.

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About this newsletter

I’m Randy Au, Quantitative UX researcher, former data analyst, and general-purpose data and tech nerd. Counting Stuff is a weekly newsletter about the less-than-sexy aspects of data science, UX research and tech. With some excursions into other fun topics.

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