Your AI agent can generate twenty plausible taglines. The next useful step is to learn what your intended audience thinks each one promises, which feels relevant, and what causes confusion.
Tagline testing uses User Intuition’s standard study workflow. Retrieve the study findings and supporting participant responses; do not assume a guaranteed preference-score field.
The path from draft to participant reactions
- Provide the candidate taglines, product context, and intended audience.
- Explicitly choose panel recruitment or BYOP.
- Ask the agent to create a draft and send the brief through
customize_study. - Answer any planning questions, then review the full saved plan from
get_study. - Approve the recruitment estimate or customer invitations before fieldwork.
- Retrieve the report and check the interview evidence behind the recommendation.
For a panel study, launch_panel with dry_run: true returns an estimate for the selected audience and country. Actual turnaround depends on recruitment and completion; a small sample is not a promise of results in a fixed number of hours.
Worked example: three SaaS taglines
Suppose a team is comparing:
- “Ship features your customers actually want.”
- “Stop guessing what customers think.”
- “Customer research in hours, not months.”
This is an illustrative research design, not a completed customer study. Ask participants what each line means before asking which they prefer. Explore whether the promise feels credible, what makes it distinctive, and what information is missing. Include a way for people to reject all three.
An overall preference can hide a segment difference. A founder may prioritize speed while a research lead prioritizes quality and evidence. Ask the agent to explain those differences and show the interviews supporting them.
What the current tools return
Study results expose findings, participant responses, sample profiles, recommendations, and source references in JSON. Use generate_report when analysis is needed, and get_interview to verify supporting messages and recording links. Preference shares, credibility scores, and ranked themes are not guaranteed typed fields in this response.
If you want a preference tally, include a consistent preference question in the approved plan and have your application or agent compute the count from the resulting evidence. State the denominator and excluded or missing answers. Do not label an inferred preference as an explicit vote.
Edge cases: when a small sample is not enough
Use a focused qualitative study to find confusing language, understand reactions, and improve the candidates. A close split across a small sample does not establish a market-wide winner. If the decision depends on a precise difference, use an appropriate quantitative design after the qualitative work clarifies what to measure.
When participants disagree, preserve that disagreement in the brief for the next iteration. Minority objections can reveal an audience mismatch that a single headline metric would conceal.
Keep the learning available
Agents can search findings and participant responses across authorized studies, then retrieve the underlying reports and interviews. Results preserve study context and source links; the calling agent interprets the evidence. Search coverage is explicit, and retrieving evidence does not launch research.
See agentic research for the supported workflow and concept testing for research design guidance.