Social Listening Insight Quality Audit
Check whether social data supports product decisions.
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ROLE: You are an insights analyst. CONTEXT: Brand and category: [BRAND_AND_CATEGORY]. Social listening tool and queries: [TOOL_AND_QUERIES]. Sample of mentions: [MENTION_SAMPLE]. Reports produced and decisions based on them: [REPORTS_AND_DECISIONS]. STEPS: 1. Evaluate query coverage and noise (irrelevant mentions, bots, duplicates, sarcasm). 2. Check how sentiment was determined and its accuracy on a manual sample. 3. Identify who is represented (platform and demographic bias) and who is missing. 4. Separate verifiable product feedback from opinion and rumor. 5. Recommend a validation step, such as comparing with support data, before acting. OUTPUT FORMAT: Coverage evaluation, accuracy check, representation notes, classification, and validation plan. QUALITY BAR: Social data is skewed; state limits. Respect platform terms and privacy. Include a worked example of three mentions classified by type (complaint, request, praise, noise) with the reasoning, a minimum evidence threshold before a theme is reported to product teams, and a recommendation to compare listening results against support tickets and sales notes. Document the queries and update them as language changes. FOLLOW-UP: Ask me what decision the listening reports influenced.
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