VOC FACT Structured Analysis

Problem

Customer comments, complaint transcripts, and NPS open-ended answers often contain blanks, system defaults, ads, and pure emotional text. Turning that raw VOC material into product signals is difficult when noise is treated as intent. This skill focuses on single-entry VOC analysis: it filters usable content and structures each valid record with the FACT framework, covering F fact, A action, C context, and T trigger. It is useful for product, support, and operations teams who need item-level evidence before root-cause or opportunity analysis.

How it works

• Readiness check: before analysis, it reports how much material was read, why anything was missed, and any sampling method, so partial samples are not presented as full coverage.
• Invalid-entry cleaning: blank fields, system defaults, single-word replies, duplicates, ads, and emotion-only lines are marked and excluded from extraction.
• Structured extraction: each valid entry is broken into objective fact, concrete user action, usage context, and the micro-moment that triggered the feedback; unsupported fields are marked as none.
• Opportunity mapping: one actionable improvement is derived from the T trigger, such as auto-detecting the order and removing manual return form entry, instead of generic advice like “improve experience.”
• Output shape: the default output is a Markdown table with original text, F, A, C, T, and opportunity, followed by a deduplicated summary of theme-level recommendations.

Boundaries

This skill is for single-entry detail extraction, not full-corpus topic clustering. Context is marked as inferred when it is not explicitly stated, and F/A/C/T values are not invented. An emotion column can be added only on request.