--- title: "Audiences — reusable synthetic research samples | Minds" canonical_url: "https://getminds.ai/guide/panels" last_updated: "2026-08-13T13:55:58.421Z" meta: description: "Build a reusable Audience from existing Minds or a plain-language segment description, then use it across interviews, questionnaires, concept tests, and..." "og:description": "Build a reusable Audience from existing Minds or a plain-language segment description, then use it across interviews, questionnaires, concept tests, and..." "og:title": "Audiences — reusable synthetic research samples | Minds" "twitter:description": "Build a reusable Audience from existing Minds or a plain-language segment description, then use it across interviews, questionnaires, concept tests, and..." "twitter:title": "Audiences — reusable synthetic research samples | Minds" --- Minds March 18, 2026·Minds Team # **Audiences — reusable synthetic research samples** An Audience is a reusable group of synthetic respondents. Build one from existing Minds or a grounded segment description, then bring it into a Study to collect comparable individual responses and synthesized findings. An **Audience** is a reusable group of Minds representing the people you want to understand. Researchers may also call this a synthetic sample or panel, but Minds uses **Audience** consistently in the product. An Audience is not the research activity itself. The work happens in a **Study**, where you can use one Mind or one or more Audiences with the method that fits your decision: - an **in-depth interview** with one Mind - **qualitative exploration at scale** across an Audience - a **questionnaire** with directional quantitative readouts - a **concept or message test** - a **segment comparison** - a **mixed-method Study** that combines these approaches ## Build an Audience in two ways ### Select existing Minds 1. Turn on multi-select in the Minds list and choose the Minds you need. 2. Select **Create Audience** in the action bar. 3. Review the Audience, give it a clear name, and save it for reuse. You can also open **New Audience** and add existing Minds there. ### Describe the people you need When the right Minds do not exist yet, describe the segment and let Minds draft it: 1. Select **New Audience** in the sidebar. 2. Describe the people you want to understand—for example, _“working parents in Germany aged 30–45 with children in school”_ or _“B2B CMOs at Series B companies.”_ 3. Minds researches relevant sources and drafts a synthetic sample. You can inspect the sources, personas, and grounding information as they become available. 4. Remove unsuitable draft Minds or request a revision, such as _“include more skeptical buyers”_ or _“add a rural segment.”_ 5. Select **Create**. The Minds continue building in the background if you leave the screen. ## Choose the Audience design Under **Additional settings**, choose how Minds should shape the draft: - **Balanced** — creates a compact Audience while representing the grounded distributions supported by the source data. - **Segment coverage** — covers more supported segmentation criteria and relevant subgroups. - **Benchmark depth** — uses deeper benchmark-style coverage when the decision requires more granular cells. These settings affect the Audience design; they do not create a separate research method. ## Start a Study Open an Audience and select **Start Study**, or start a new Study from the sidebar. Add the question, brief, website, image, ad, video, or document you want to explore. Choose how much setup you need: - **Quick** — analyzes the request and sources, recommends relevant saved or new Audiences, and asks for confirmation before starting. - **Custom** — opens an AI-guided setup where you define the research goal, sources, Audiences, method, questions, and response formats before anything runs. Existing Audiences and **NEW** drafts are selected in the Audience step; NEW drafts remain uncreated through review and use the grounded Quick Audience-creation pipeline only after final confirmation. The Study opens when those Audiences are ready. - **Blank** — opens an empty Study so you can assemble it yourself. “Custom” describes the setup experience. It is not a research method. The Study itself can use an interview, questionnaire, qualitative exploration, concept test, segment comparison, or a combination of methods. ## Review questions and response formats Before a planned Study runs, review the proposed questions and response format. A question can use open text, choices, a standard **1–5**, **1–7**, **1–10**, or **0–10** scale, or a custom integer scale. The confirmed format stays attached to the Study through processing and aggregation. API and MCP clients receive the same per-question `response` contract, so the confirmed format remains consistent across interfaces. Quick planning, generated Custom questions, plan revisions, v1, and MCP use the same research-planning policy and fast planner model. When the confirmation selects an available method—Custom research, Focused question, Questionnaire, Qualitative exploration, or a calculator-backed method such as MaxDiff, NPS, top/bottom box scoring, key driver analysis, TURF, Gabor-Granger, Van Westendorp, Kano, ranked preferences, or segment comparison—the exact method contract stays attached through the normal stream or durable questionnaire processor. Experimental and planned methods remain visible in the plan, but the Run action stays blocked; they are never silently converted to Custom research. Minds can detect multiple questions in a prompt or uploaded questionnaire. You can distinguish your original questions from suggestions, edit the plan, and confirm the final sequence before it runs. ## Add methodological complexity only when it helps Most requests do not need a named method. Start with the decision, main source or asset, and the questions the user wants answered. Add methodological complexity only when the user requests it or when it materially changes the evidence. - **Available** methods run through the current Study runner. Ten calculator-backed methods are available: MaxDiff builds balanced forced-choice tasks, and NPS, top/bottom box scoring, key driver analysis, TURF, Gabor-Granger, Van Westendorp, Kano, ranked preferences, and segment comparison each design their questions server-side, then calculate deterministic overall and per-Audience results after collection. Segment comparison includes pairwise significance tests across the answering Audiences. - **Experimental** methods can be represented and reviewed but are rejected at execution. - **Planned** methods are represented for forward compatibility but are not executable. Conjoint is currently planned. The question processor remains method-agnostic: it collects exact task answers and owns retries, quota, ordering, and persistence. Versioned server adapters design method-specific tasks and calculate artifacts, so future built-in or client-specific methodologies can be added without rewriting the processor. The result summary receives those deterministic artifacts as authoritative evidence and explains them without recalculating the scores. Conjoint still needs its own validated design, estimator, diagnostics, and simulator adapter before it becomes available. The Study loader uses persisted server acceptance and processor-start timestamps. Reopening or reloading the Study therefore continues the same elapsed counter instead of starting again at zero. ## Compare Audiences in one Study Add two or more Audiences to the same Study to compare segments side by side. Ask the same question and examine where their individual responses, distributions, themes, and synthesized findings converge or differ. This is a **segment comparison within a Study**, not a separate “panel” object. ## Go from breadth to depth If one response stands out, open that Mind for an in-depth interview. Probe the reasoning, show another stimulus, or ask what would change the response. The original Audience Study remains saved, so you can return to the wider evidence at any time. ## Understand the evidence Depending on the question and configured response format, a Study can show: - individual Mind-level responses - distributions and comparisons - clustered qualitative themes - synthesized findings grounded in the collected responses - available alignment indicators for the Audience and its grounding Treat quantitative outputs from synthetic respondents as **directional evidence**, not automatically as population estimates. The strength of the result depends on the Audience definition, grounding, question design, and available validation evidence. ## Tips - State the decision you need to make, not only the topic. - Use a single Mind for depth and an Audience for breadth or comparison. - Attach the actual stimulus when testing a claim, concept, page, image, or video. - Use explicit choices or scales when you need comparable directional readouts. - Follow up on surprising responses before relying on a synthesized pattern. - Compare different Audiences only when the difference is relevant to the decision. --- **Audience** is who you study. **Study** is the saved research workspace. **Method** is how you learn.