Existing customers, split by needs and behaviours
Ground the study in customer evidence relevant to this decision.
Evidence you can examine.
The business needs an answer before the next research cycle. Turn your existing customer evidence into audiences you can interview, challenge and learn from in minutes.
Review themes, source references and confidence before a finding becomes a recommendation. Keep the open questions visible alongside the answer.
Use depth interviews to surface motivations and objections, then sharpen the questions you take into fieldwork.
Explore depth interviewsPut competing propositions in front of the same audience and examine where reactions differ.
Explore concept testingGround audiences in previous research and revisit them when a new stakeholder question arrives.
Explore depth interviewsBring a real question. We’ll scope the audience, evidence and study together.
Ground the study in customer evidence relevant to this decision.
Interview transcripts
Survey findings
Research question
Keep the question, the evidence and the next action connected.
What stops security-conscious customers adopting a new protection feature?
Separate lack of understanding from lack of trust. Review which audience reactions point to each explanation and how well they are supported.
Take the strongest hypotheses and unresolved questions into interviews with real customers.
Illustrative research brief. This is an example of how to use findings, not a completed customer study or a product screenshot.
Sunzu internal back-testing. Human insights, retested is the reference set to 100%. The same humans were retested within a two-week interval. This is a study-specific comparison, not an overall accuracy score or a guarantee for your study.
In just a couple of months, over 170 of our colleagues have run over 500 studies.
We’re now directly integrating audiences into many of our research, product and marketing workflows.
In a market flooded with AI-generated noise, this was the first platform that genuinely earned our trust.
By grounding its models in research principles, behavioural science, and real customer data, it delivers insights that feel credible, transparent, and actionable.
It changed our perspective on what’s possible with AI in customer research.