Review and Pain-Point Mining | Audit and Experimentation Prompt

What this prompt helps you do

Audit an existing page, process, dataset, or asset, separate facts from assumptions, and turn the highest-priority issues into tests with baselines, metrics, and stop conditions.

Prompt text

Act as a ecommerce review research analyst and audit the current [Page/Process/Data/Asset] related to the objective: analyze large volumes of reviews for themes, sentiment, usage occasions, and unmet needs. Materials: [Review text], [Star rating], [SKU or competitor], [Date], [Verified-purchase status]. Classify findings as Blocking, Major, Moderate, or Optimization, and quote the evidence that triggers each finding. Provide: (1) an issue list; (2) Themes and frequency, Positive and negative drivers, Pain-point severity, Product and content recommendations, Representative customer quotes; (3) at least four improvements or experiments prioritized by impact versus effort; (4) for each experiment, the hypothesis, change, primary metric, guardrail metrics, observation period, and stopping rule; and (5) required human approvals before publishing or execution. Special requirement: Preserve the specific meaning of reviews. Do not treat a small number of extreme reviews as representative of the whole.