Q1量表15
这段视频让您停下滑动的可能性有多大？
- 1
- 2
- 3
- 4
- 5
- 平均
- 3.7
探索合成研究的世界。
测试信息、比较概念、探索定价，或开展结构化研究。设置简单时快速开始；当决策需要更高严谨性时，也能获得完整控制。每项结果都与相应的回答及其背后的推理保持关联。
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Q2多选
在日常轮班中，哪些问题造成了最多本可避免的阻碍？
- 交接班不完整57%
- 重复记录45%
- 团队沟通割裂40%
- 系统难用或陈旧35%
- 主要回答
- 交接班不完整57%
Q3定性
经营业务时，客户看不到的最困难部分是什么？
定价难以预测
40%Mina Park独立电气承包商
材料与排期
35%Ravi Mehta涂装公司经营者
客户沟通
25%Elena Rossi独立木匠
Q4分类
您会首先选择哪种拉面口味？
Q5分类A/B 测试
哪个视觉设计让您更想尝试这款饮料？
受众如何构建。
- 01
你描述目标受众
输入提示词，并可选择添加文件或其他数据作为背景信息。
- 02
研究并验证来源
在网络上搜索相关、可信的来源，为受众构建提供依据。
- 03
提取分布
将证据转化为人口统计、行为和态度分布。
- 04
你进行微调并确认
审查建议的分布，进行调整并确认你的受众。
- 05
创建单个 Mind
每个 Mind 都基于已确认的分布、收集的来源和相关的公开社交媒体信号构建。
- 06
你的受众已准备就绪
你现在可以开展研究、评估想法并比较概念。
哪一种最符合你的身份？
Minds 支持不同的研究场景、决策阶段和关注重点，从测试概念、信息和产品，到探索受众或扩展现有研究。选择最贴近你工作的视角，了解同一个工作空间如何适应不同的问题、方法和分析深度。
品牌与企业
在实际可用的时间内，建立管理层能够认可的证据。
代理机构
在交付客户前测试方案，并增强每项建议的说服力。
研究合作伙伴
以自己的名义，将数据和方法转化为可复用的受众。
我们如何验证我们的结果
我们将合成受众的回答与真实调查数据进行比较，以衡量它们在多大程度上反映现实世界的回答模式。Minds 在生成回答时不会看到真实的调查结果，其回答会在比较开始前最终确定。这样，我们就能衡量结果在哪些方面一致，又在哪些方面存在差异。
调查分布近似度
（例如 GSS、ANES、CES 和 PISA）
与真实调查结果的相似度达到 93.99%
301 个 Minds 回答了英国食品标准局的青年调查，其综合回答模式与已发布数据高度一致。
更接近真实人群的回答方式
与标准 AI 提示相比，Minds 被选中的频率高出 59%，与留出的访谈回答也更接近 68%。
我们终于可以规模化地开展定性研究，而不必让一项高成本的调研占用团队数周的精力。我们提出一个问题，几分钟内就能听到目标受众的反馈，我们已经投入的市场研究也因此变得更有价值。
吸引我们的是这一方法背后扎实的市场研究基础。受访小组以受众数据为基准进行校验，这让我们有信心，答案反映的是一个明确定义的目标群体的观点，而非纯粹的猜测。它提供了一种结构化的方式来探索受众洞察，并有潜力让更大规模的研究变得更易开展、更高效。
对我们这样的代理公司来说，这从根本上改变了我们与客户的合作方式。我们可以在几分钟内用不同的目标受众测试创意，并快速获得关于执行方案的反馈。感觉就像为每个客户都配备了一个专属小组。
公开受众
可直接使用的合成受众，包含人物、人口统计、行为背景和可见来源，无需重新构建样本即可开始研究。