Shoupai 5 JTBD Three-Real Demand Analysis

Problem

Customer voice, interview notes, support tickets, and review feedback often mix feature requests, emotional complaints, and fragmented contexts. Turning them directly into a requirement list can produce weak conclusions such as “cheaper” or “more usable,” and it is hard to tell which pain points deserve roadmap attention. This skill constrains insight work with a checkable analytical structure, helping identify real jobs, scenarios, and opportunities from raw feedback.

How It Works

It starts from user-provided VOC, then performs structured preprocessing: extract the original ask, emotional tone, trigger context, and current workaround, and break the evidence into FACT elements of fact, action, context, and trigger. It then applies NEO demand analysis: N keeps the user’s own wording, E uses repeated why-questions to reach the emotional job, and O identifies latent opportunities using social jobs, market gaps, and competitor gaps. It then expresses core demands using the JTBD sentence pattern and requires functional, emotional, and social dimensions to be filled in. Finally, it completes three-real analysis: real customer must include the struggle context, alternative solution, and shared motivation; real scenario must be a complete story with time, place, trigger, and consequence; real demand must return to the deeper job. The output is three Markdown tables with aligned row numbers for cross-referencing.

Boundaries

It is suitable for contexts with reasonably complete customer voice, especially product strategy, requirement clarification, and opportunity prioritization. Without real VOC, it can only infer from limited information and does not replace user research. It is qualitative and does not directly produce sample sizes, statistical significance, or A/B test conclusions; pain-point intensity ranking still depends on available frequency and impact data.