Insights on expert networks, market research, UX research, and AI training from the CleverX team.
Travel products serve users during high-emotion, high-spend moments where a single UX failure can ruin a vacation or a business trip. This guide covers research methods, recruitment, and frameworks for travel product teams.
Insurance products ask users to pay for something they hope they never use, creating a trust and comprehension challenge that generic UX methods cannot address. This guide covers research methods, recruitment, and frameworks for insurance product teams.
Automotive UX research happens at 70 mph. Distraction kills, safety is non-negotiable, and users interact with interfaces while operating heavy machinery. This guide covers research methods, recruitment, and frameworks for mobility product teams.
Choosing the right user research tools means knowing how platforms compare across method support, participant access, and pricing. This covers the top platforms in each category, with key specs, pricing models, and recommended stacks for B2B and B2C programs.
Participant screening determines whether the people who show up in research sessions actually represent the population the research needs to understand. AI-powered screening addresses the structural limitations of traditional screeners through behavioral consistency analysis, profile-based matching, and fraud pattern detection.
EdTech products serve two audiences whose needs often conflict: teachers who deliver instruction and students who receive it. This guide covers research methods, recruitment, and frameworks for education product teams.
The pitch for synthetic users is appealing: instant research subjects, any profile, no scheduling required. The problem is that the answers are not real. They are statistically plausible text generated by a language model, not observations of actual human behavior.
Most A/B tests fail because of poor planning, not poor execution. This template provides a step-by-step framework for structuring experiments that produce statistically valid, actionable results.
A research panel is a pre-recruited pool of individuals who have agreed to participate in research studies when invited. Panel members have provided profile information and consented to be contacted for research opportunities.
User research has always been constrained by analyst time. AI research assistant tools handle meaningful portions of the mechanical work within each research phase, freeing researchers to spend more time on the judgment-intensive work that determines research quality.
Analyzing user interviews manually is one of the most time-intensive operations in qualitative research. AI interview analysis tools have changed this economics substantially: transcription that would take hours takes minutes, and initial theme identification that would take a day surfaces within an hour of session completion.
Continuous discovery builds customer contact into the routine work of the product team itself, weekly interviews, structured synthesis, and assumption testing: so that product decisions are grounded in current evidence rather than the memory of the last research project.