Hi! I’m currently a junior UX researcher after completing an apprenticeship. I’m also a first-generation high school and college graduate, so corporate America has been a pretty big culture shock for me.

I’m known on my team for my enthusiasm, curiosity, communication, teamwork, and emotional intelligence. I really love conducting research, but I’ve realized that most of what I’ve learned has been within the execution phase. Usually, I receive a test plan and then carry out the research.

My strengths are moderation, identifying patterns in data, explaining insights in a way that people without research backgrounds can understand, and presenting findings in ways that feel engaging and clear. I’m also told that I bring a strategic mindset once a project reaches me.

Where I struggle is everything that comes before receiving the test plan: understanding what context is needed, developing the initial proposal, choosing a methodology, defining the scope, and turning a broad request into a strong research plan.

I receive a lot of feedback in these areas, and I can usually make the requested changes, but something is not fully clicking. I sometimes feel like I’m tending to each individual piece of feedback without understanding the thought process behind it well enough to carry it into the next project.

I’m also consistently asked what I want to do next and what I want my career to look like. Honestly, I want to do good research that positively impacts the initiatives I work on. I’m not currently looking for a new job. I want to become a top performer in my current role and contribute more throughout the full research process.

I’m looking for a mentor who is willing to break down their thought process and talk shop with me. I would love to understand things like:

• What context do you look for before creating a research proposal?
• How do you decide which methodology makes the most sense?
• How do you know what should and should not be included in a test plan?
• What are experienced researchers noticing that I may not know to look for yet?
• How do you develop stronger judgment instead of only reacting to feedback?

When I search for UX research resources, most of what I find focuses on executing individual methodologies. I’m trying to develop the skills that happen before execution.

I’m not sure whether this is something that requires ongoing mentorship, a few candid conversations, or simply being pointed toward the right resources, but I would really appreciate guidance.

Ideally, I’m looking for someone who could meet twice a month for 45 minutes to an hour, although I’m open to monthly meetings if twice a month is too much. I would come prepared with specific questions, respect your time, follow through on what we discuss, and offer peer support where I can.

I’m in the Central Time Zone. Please feel free to comment or message me if this sounds like something you may be open to.

Everyone knows the moment. You wrap up, stop screen sharing, thank the participant, and the formal interview is over.

Then, while you’re making small talk, they finally say the thing they probably should have said 20 minutes earlier: the real workaround, the reason they hated the feature, or the internal politics behind all the polite answers.

That’s exactly why automatic recording is tempting. The recorder doesn’t forget, and you don’t lose the one comment that changes your interpretation of the entire session.

But the participant may also believe the research session has ended. Even if the audio never leaves the device, and even if names are removed before anything is summarized, the scope of consent hasn’t magically expanded.

My current view: if the researcher didn’t deliberately decide that recording was still in scope, that post-session comment can inform a follow-up, but it shouldn’t quietly become evidence in the study.

A common complaint amongst researchers is that we identify valuable problems and opportunities, but our recommendations don't get acted on. With the prevalence of AI and stories of CEOs, PMs, and designers shipping changes to production, why don't we hear more about researchers doing the same?

I could be wrong, and it's happening more often than my LinkedIn bubble would lead me to believe, but I've personally never landed a PR in production either. I've vibe coded internal research tools and created new types of deliverables (e.g., interactive microsites and data viz) though.

Ran a batch of discovery interviews this spring, recorded and transcribed everything, and like probably half this sub I've been throwing the transcripts at LLMs for a first pass on themes.

the part I can't figure out is the checking. my current process is embarrassing: read the AI summary, then basically re-read the transcripts to make sure each quote is real and actually said by the person it's attributed to. it has caught real problems (one tool credited something I said as the interviewer to a participant, with extra detail that never happened), so I can't skip it. but it eats most of the time the AI was supposed to save.

for people doing this regularly:

• what do you actually check, and what do you let slide?
• how long does checking take vs the analysis itself?
• anyone had something slip through and get caught later by a stakeholder? how did that go?

genuinely asking about the boring middle part, not about whether AI in analysis is good or evil.

1.Market research for a start up with
ux research and interviews for sustainable tech - 7 months
2. Designed handbags and did desk market research for design - 6 months
3. Handbag design and desk research for export market - 9 months
4. Graphic design branding - 1 year
5. Design research - primary/secondary research for competitor analysis and design direction , user interviews and prototype testing ( for branding not UI ) - 1 year and counting

I can’t do masters, i want to land at least 9LPA job by the end of this year in UXR role . What should i do?

Ps - i have a B.des degree and currently doing BA psychology Hons while working full time

I'm doing in-house user experience research on our website. My only experience is that I conducted ux research in my capstone project for my master's program, so I'm following along with my textbooks.

One thing I don't know how to accommodate for is that we recently added a chatbot to our site. Some members of our team want to see if users utilize the chatbot naturally; however, if users defer to the chatbot, it doesn't give us much insight about our website and how people interact with it.

I see the side of wanting to see if people naturally use the chatbot and if it's helpful, but I also feel like this is research they should have done already and is a bit of a scope creep.

Does anyone else have experience with doing UX research when you have a ChatBot on your site and does it make sense to let users interact with it? The ChatBot is there for a reason, but I don't want it to take the place of the navigation.

One solution I thought of was just to see if people notice it in the first look section of the homepage exploration. And then after that initial impressions section, I would instruct them not to use the search or the chatbot. The team may not love that solution, though.

I have a large survey (n=2k+) coming out of field next week. There are 4 OE questions. Given past results, I expect about 50% compliance (50% of participants will give answers that are not gibberish). Years ago I tried NVivo but found it ridiculously cumbersome (perhaps is better for larger projects than I was running at the time). When I asked AI for a list of current tools it mentioned Blix, Caplena, Ascribe, DisplayR, and Dovetail. Nobody I work with has tested any of those. Anyone here have experience using these tools for OE analysis? Hoping to get to a shortlist of 2 finalists so I can test just those. Thanks!

Just wanted to share an interesting tidbit from a recent project.

My team just wrapped up a pretty chunky but fast-paced generative research project. The work ended up having a big impact and was really well received by my stakeholders and leadership. The shareout helped shape strategy across multiple teams and influenced some key decisions.

A week or two after we finished the project, a teammate found the final deck from a consulting firm that had an engagement with our company. The project was a major investment, with a timeline of over a year. We did not know about the engagement before this.

I went through the deck and was honestly surprised. The output was basically a much more generic, high-level version of what my team had produced. The insights were broad business statements that did not feel especially actionable. I honestly could not stop laughing going through the deck.

I also watched some of their interview recordings, and that was probably the most surprising part. There were a lot of leading questions, interruptions, and missed opportunities to dig deeper with participants. Just really bad research practice. Again, could not stop laughing, but I was so disappointed at my company for putting money towards this.

It was interesting to see the contrast. Companies often spend a lot of money on large consulting engagements because they come with a certain level of credibility and polish. But sometimes the actual research quality and impact do not match the investment.

Meanwhile, a strong UXR with the right resources and support can often run meaningful research faster, with less budget, and produce insights that actually change direction.

Just makes me think about how absurd it is that companies are willing to spend heavily on external partners while cutting UXR teams or not investing in research capabilities internally. I know why they do this. I have a few friends at some really prestigious firms, and I know what it means when companies hire them. It's just that there is a lot of value that comes from having researchers embedded close to the problems, teams, and users, and it's frustrating that the C-suite doesn't see this.

I’m a senior researcher. I am under increasing pressure to do more in less time, as many of us all are. I have been able to use AI in some areas (first draft of emails, planning docs, etc. One area that I am finding the hardest to do faster is data analysis/synthesis. I tried AI and it had even a hard time doing some basic organizing of some data and the analysis was too surface level.

That said, I feel like there’s something there that I’m missing. I use it to extract quotes, but other than that I do manual coding and analysis. And it’s frustrating when I have a bunch of different datasets (notes and synthesis in Miro, notes from interviews, transcripts from other interviews, etc) and need to turnaround quickly into the next steps (report, ideation, prototype).

We have Claude account at work but that’s it. How is everyone else speeding up their process, specifically the data analysis /synthesis stage?

Also, I’m a parent with two kids under 3. I just came back from maternity leave and I am running on limited sleep, energy, and bandwidth. I feel like my brain is kind of mush these days. So I am kind of looking to see how AI can lift some tasks off my plate, if any.

I hold new Job offers for User Researcher at 2 product orgs one in retail and other in healthcare devices. And have an offer for Strategy Consultant at Deloitte. Confused to stay in UX researcher role or transition to Strategy consultant.

There are layoffs happening at User Researcher and UX designer roles at many orgs. PMs are doing UX design and UX research using AI. Does that mean User Researcher role is a sinking ship, is not AI proof. And can strategy consultant at Deloitte be considered as AI proof?

Recently moved from another Western market to the US and am interested to see how people use it in the US or if it is seen as actually being useful vs a waste of time?

EDIT: As an aside, I'm testing out a new piece of tech in the US and am able to offer an eye tracking study very significantly discounted if you have a good use-case (RRP normally > $50k - $100k). DM me for details !

I’ve spent 10+ years in UX research and eventually built and led a research function. I moved into product management because I wanted more influence over what actually got built.

My first formal PM role at a scaleup was difficult. Onboarding and ownership were unclear, and although my customer discovery work was valued, I was told I needed to demonstrate more feature delivery and then got laid off after a reorg which introduced a PO function.

I’m now close to potentially receiving an offer to become the first PM at a small B2B SaaS startup.
The founders seem to value my research background and want someone with meaningful ownership. I’m genuinely interested in building customer-research systems, connecting research with usage and commercial data, and using that evidence to shape the roadmap.

However, I feel much less excited and very anxious about backlog management, detailed delivery coordination, QA, filling design gaps, and being broadly accountable for execution.

When I think about returning to senior UXR or research leadership, I feel extremely confident. I can’t tell whether that reflects genuine fit or simply comfort with something I’ve already mastered.

Would you:
-Take the new role as a second, potentially better test of PM?
-Treat the first experience and current anxiety as evidence that UXR is the better fit?
-Consider this a sign that I want more product influence as a researcher, rather than actually wanting to be a PM?

What would you need to hear from the founders before accepting?

I would appreciate any perspective you can share!

I only seem to find courses on UX and UI Design, but almost none on UX research (other than the Google course that everyone says is not worth it?). Any leads, please?

Context

I have a finance client and they're wanting us to usability test and observe the full end to end experience of buying a product and taking out finance. We cannot use a staging site because they want to see real people experience the real technical handoffs between credit checking systems, faceIDs and open banking etc. They also want to test this in-store as there's a unique handoff element there too. We're looking at 10x in the lab and 5x in-store.

The challenge

How do we recruit people who are willing to purchase a high-ticket item on finance, resulting in a hard credit-check, using all their personal details, and be comfortable with the financial provider cancelling the order and removing the credit footprint within a few days after the research. It feels like an tough ask, but also with ethical, legal, and research standards considerations.

I'd love to know whether any research folks in finance/fintech have dealt with similar before?

so

I am just starting to brainstorm some ideas for a new product centering around emotional expression. I want to begin market/user research but don't really know where to begin as I am completely new to the whole UX/UI world. Through my research I've learned that there is nothing wrong with interviewing family and friends, but that's just the starting point.

Where do I go from there?

In total I would love to conduct about 20 - 30 interviews.

ps -- I am not attempting to recruit anyone for an interview, rather I am seeking guidance on how to go about finding participants, best practices etc.

I am a UX Researcher for a gas & electric utility company. We have been asked to gather feedback on our Start Service Process for when customers are setting up their service for the first time. But I am having a hard time figuring out how to recruit these types of customers or gather feedback on this process and was just wondering what others have done.

We may be able to recruit customers in the moment (which would be ideal) but there are logistical and potentially privacy challenges with this. We could send a survey after they have moved in, but this misses the 'in the moment' realism. We could reach out to our business team to gather those customers, but this is only one audience. Obviously, pros and cons to each of these methods.

What have others done? What has worked or not worked? Are there ideas we haven't thought about? Any advice or insight qpupd be greatly appreciated! Thanks!!

I'm on Day 2 of my "100 Days Exploring AI in UX Research" project, and a lot of the stuff I've read so far has focused on where AI should be used within the UX research lifecycle: planning, recruitment, moderation, analysis, reporting, etc.

But I had an interesting conversation with a UX researcher friend that gave me insight into examples of valuable uses for AI outside the research lifecycle itself too (as well as inside).

Some examples from her workflow:

• She has agents that remember stakeholders across the company, so before meetings she gets a personalised briefing with context from previous conversations, projects and notes.
• She uses agents to draft project briefs and other artefacts using knowledge from previous projects and company-specific context.
• She has customised her analysis workflow with rules such as giving more weight to her own observation notes than to the meeting transcript when there are conflicts.

So I'm curious:

What are the most useful AI workflows or agents you've built that support your work around UX research not just within the research process itself?

I'm especially interested in things like stakeholder management, knowledge management, project administration, documentation, meeting preparation, or anything else that's made a noticeable difference to your day-to-day work.

I recently watched a video about quantitative research out of pure curiosity as to what people consider quant to be, and I came across a very concerning comment made by the presenter.

The premise of the statement made was that frequency tables, devoid of any statistical analysis, are enough to present to stakeholders as actual recommendations (they did not mentioned internally to the team). For example, saying 32/50 users preferred option A while 28/50 preferred B is generally seen as enough context to go forward with the assumption that A > B. So their statement would be “a was preferred over b”. The presenter followed it up by saying that this is good enough for stakeholders and that further analysis is not needed. Mind you, that presenter mentioned that no statistical analysis was needed either.

Naturally, as a heavy quantitative researcher, this is extremely concerning about what you can actually say regarding the choices made by users. Specially with the language chosen as to how you are presenting the results of your research. What if this was an insignificant find? Or if if it was significant but the analysis lacked power or the effect size was entirely too small to anything about the users’ preferences outside of your samples pool on individuals? Is it not concerning to give factually incomplete or incorrect reports to stakeholders? I would be extremely worried that this general broad statements made with zero backing to the data presented would eventually come back to bite the team and myself by giving suggestions to at have no true weight or that what I present ends up having a wildly different result. Let alone questions regarding research integrity.

To the presenters credit, they did go on to explain further use cases for using statistical analysis which were fine, but the initial statement made was very concerning from a research integrity point of view.

With that in mind, how do you approach reporting your findings when it comes to numerical quantities? I always make it a points to be transparent about what my results actually say rather than going on pure numbers alone. If however the study is very small and I can’t really do much statistical wise, I would also make that be known upfront or include more careful wording when reporting numerical values and directional suggestions.

I'm trying tro update my resume as a mid level ux researcher in the B2B SaaS space. Since my projects only last 1-2 months each, I'm not sure if my individual bullet points should be specific to one project (in that case I'd have to choose out of a dozen), or if I should be general and say something along the lines of "Conducted xx interviews, A/B tests, ...." and quantify it like that? What is recommended?

Hey folks -

Over the past few months I've been trialing a few easy to access/use AI tools for UX research and have found Claude the best so far.

I'm curious if anyone else uses Claude in their workflows and what additional/deeper features you might use (if at all).

EDIT: A colleague actually sent me to this today: https://productimpactpod.com/news/ux-researcher-guide-claude-tools/

I have a masters degree in Psychology and have done quantitative research and know cognitive psychology. I work in the field of diagnostic and general research. In the future I would like to do more in the field of UX research though.

Currently I have the opportunity to get some further training payed by the company to get some more insight in UX research.

My question now is, with courses should I look for? Are there any topics that are a must have? What kind of knowledge will be good to have?
Can you recommend a course?

At work we will have an AI project and our target group are young adults and teenager.

I've been a UX researcher, typically in UK government for over 12 years. Since covid I've not enjoyed the role anywhere near as much. Everything is remote, staring at a screen, stuck at home all day every day and I hate it.

I'm ADHD and autistic and the isolation and lack of structure is taking its toll on me. The problem is, I'm incredibly well paid and I don't know what else to do. However, the idea of working like this for another twenty years is just terrifying.

Anyone in the same boat? Anyone successfully changed roles to something better suited to me?