The Rise of the Empowered Solo UX Researcher: How technology and AI is Changing the Game
In the current financial climate, with limited resources, designers, product managers, and UX researchers often find themselves juggling multiple tasks, including extended research responsibilities.
Luckily the recent surge in AI-powered tools is not only expanding possibilities but also significantly reducing the effort required to conduct high-quality research, even for solo researchers.
This year, I have utilized three tools that have greatly enhanced my efficiency and research capabilities, leading to:
1. Userbrain.com: AI Insights for Lightning-Fast Feedback
Userbrain.com has always been my reliable platform for remote user testing. The new ‘AI Insights’ feature takes it further. Instead of manually watching and transcribing video interviews, AI automatically extracts key points and tags them for analysis. This is a lifesaver when time is tight, and you need actionable insights fast.
Tip: Instruct participants to “think aloud” while performing tasks and reflect afterward. This rich verbal data gives the AI more to work with.
2. Lyssna.com: Narrowing Your Research Focus
For years, I relied on Lyssna.com for testing product/marketing copy and conducting surveys. The new screener feature allows me to zero in on my ideal user cohort.
It’s easy to experiment with the screening feature and delivers fast results if the filter questions are not too narrow. I often use it to filter participants down to those that work in a particular work place setting, but it has a variety of use cases as listed on Lyssna.com:
- Sensitive research: If you’re researching sensitive topics or asking people to share personal information, a screener can help you find people who are comfortable talking about those things.
- Specialized knowledge: For research projects that need people with special knowledge or skills, like professionals in a field or users of a certain software tool, a screener can help find people who have the skills you need.
- Branding research: When you’re doing research focused on branding, you can use a screener to make sure that the people taking part have some knowledge about the brand.
- Product testing: If you’re testing a product, screeners can help filter participants based on their prior experience with similar products or their usage habits.
- Market segmentation: If you want to separate your target market into different groups based on things like what they like, how they act, or what they want to buy, screeners can help you find people who fit into each group.
The other area Lyssna has double downed on recently is it’s information architecture capabilities; Card-Sorting and Tree-Testing is now a research type and to my knowledge these types of tests have typically required a subscription licence on platforms like Maze and Optimal Workshop.
3. Dovetail: The Research Powerhouse Gets an AI Boost
Dovetail App has consistently impressed me with its speech-to-text transcription capabilities. I typically use video interviews and Userbrain recordings as inputs, which are automatically transcribed and tagged for various purposes such as feedback clustering, trend analysis, and affinity mapping. The real game-changer, however, is the insights reporting feature. It allows for the creation of a centralized document that includes user insights, video clips, tags, and transcripts, providing a more effective way of sharing research reports.
Previously, achieving these results required significant manual effort. However, Dovetail’s recently introduced Magic Insights feature promises to revolutionize this process by offering a fully AI-assisted platform experience. While I have only tested a few of the promised features so far, the auto summary and tagging features have already proven to be major time savers. Once fully integrated, Dovetail has the potential to transform from a luxury research tool to my daily driver.
Tech note: Dovetail emphasizes they use their own AI models in-house, keeping your sensitive research data secure.
The question lingers: Will AI replace UX Researchers anytime soon?
I’m on the fence about this, responding with a cautious “maybe.”
While AI tools can streamline tasks, expedite data processing, and offer suggestions that might reduce the need for UX researchers in a team, I’m convinced that the strategic insight and comprehensive perspective of an experienced researcher can’t be fully substituted by AI.
An experienced researcher is crucial in any organization and stands out in a competitive field, ensuring that product, engineering, and marketing efforts are focused correctly and timely. 👊
What do you think? Let me know in the comments what tech is helping you right now.
Thoughts and opinions are my own with a bit of added AI copy support 😁. None of the links mentioned are sponsored.
