Core Insights - The article discusses the emergence of a new research paradigm called "Intelligent Mixed Research," which combines qualitative depth with quantitative scale to address the limitations of traditional user research methods [1][6]. Group 1: Traditional Research Limitations - Traditional qualitative research provides depth but lacks efficiency, while quantitative research answers "how many" and "who" but fails to explain "why" behind user behaviors [3]. - Companies often struggle to uncover innovative opportunities or understand complex user behaviors using traditional quantitative data [3]. Group 2: Intelligent Mixed Research Features - Intelligent Mixed Research is AI-driven, integrating the conversational depth of qualitative research with the scalability of quantitative analysis, marking a shift from traditional methods to a data-driven approach [6]. - Key features include: - Conversational depth, allowing AI to conduct natural dialogues and dynamic follow-ups to uncover user motivations and emotions [6]. - Scalable analysis, enabling AI to process hundreds of interviews simultaneously, thus broadening the reach of insights [6]. - Objective automation, which standardizes interview execution and data analysis, reducing subjective bias and enhancing the accuracy of insights [6]. Group 3: Implementation Stages - Stage 1: Intelligent research planning and outline design, where AI generates a comprehensive interview outline in minutes based on basic parameters [7]. - Stage 2: One-click recruitment and user linking, significantly shortening the recruitment process from days to hours [8]. - Stage 3: AI autonomous interviews with dynamic follow-ups, allowing for natural interactions and deeper exploration of user motivations [9]. - Stage 4: Automated data processing and report generation, transforming qualitative data into visual insights and structured reports in minutes [10]. Group 4: Suitable Business Scenarios - Global and multilingual market research, where AI can efficiently conduct interviews across languages without manual translation [11]. - Identifying potential needs for innovative product features, as AI can capture overlooked insights without preconceived notions [12]. - Rapid collection of large-scale user feedback, combining the advantages of surveys and in-depth interviews [13]. - Instant experience feedback collection, capturing real-time user experiences immediately after interactions [14]. - In-depth satisfaction surveys, where AI can explore the reasons behind satisfaction scores, providing actionable insights for product optimization [16]. Group 5: Collaboration Between AI and Human Researchers - AI and human researchers form a collaborative ecosystem, where AI handles breadth and speed, while humans focus on depth and strategy [16]. - This collaboration elevates the role of human researchers from data executors to strategic insight consultants, allowing them to concentrate on high-level business challenges [16]. Group 6: Case Study - AI Interview Platform - The "AI Interview" platform developed by YS INSIGHT exemplifies the practical application of Intelligent Mixed Research, automating the entire research process and improving efficiency by over 300% while reducing time costs by up to 80% [17]. - The platform includes: - AI Interviewer, which compresses weeks of user research into hours [17]. - AI Research Assistant, enhancing human interviews with real-time suggestions and focus marking [17]. - Insight Pulse, which extracts user needs and competitive intelligence from social media data, shortening the insight cycle from months to days [17].