Prompt Almanac

Audit intermediate Works with: Any AI

Customer Support Ticket Trend Audit for Product Insight

Json output Has example

Turn tickets into product evidence.

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Turn support tickets into product evidence. Tickets (anonymized: ID, date, channel, subject, text, resolution, tags if any): [PASTE_TICKETS]. Product areas: [PRODUCT_AREAS]. Period: [PERIOD].

Return valid JSON only, in this shape:
{"summary": {"total_tickets": number, "period": string, "top_themes": [ {"theme": string, "count": number, "share_percent": number, "example_ticket_ids": [..]} ] },
"themes": [ {"name": string, "product_area": string, "type": "bug"|"usability"|"missing_feature"|"documentation"|"billing"|"how_to"|"other", "count": number, "severity": 1-5, "user_quote": string (short, verbatim, no personal data), "root_cause_hypothesis": string, "evidence_ids": [..], "recommended_action": string, "owner_suggestion": string} ],
"trends": [ {"observation": string, "evidence": string, "confidence": "low"|"medium"|"high"} ],
"data_quality_issues": [string],
"questions_for_followup": [string] }

Rules: count only what appears in the data; do not extrapolate to all customers; keep user quotes free of names and contact details; if tickets are too few for a trend, say so under data_quality_issues. After the JSON, add two plain lines: the one theme to fix first and why.

Make sure the JSON parses, use null for unknown values, and keep every string on one line. Where a theme appears in fewer than three tickets, mark its confidence as low so nobody builds a roadmap on a single complaint.

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Replace each bracketed placeholder with your own details, then paste into Any AI or another assistant.

See an example result (fictional data)
{"summary": {"total_tickets": 212, "period": "September", "top_themes": [{"theme": "Cannot export report", "count": 38, "share_percent": 17.9, "example_ticket_ids": ["T-1021", "T-1090"]}]},
"themes": [{"name": "Export fails for large reports", "product_area": "Reports", "type": "bug", "count": 38, "severity": 4, "user_quote": "Export spins and never finishes with over 5,000 rows", "root_cause_hypothesis": "Timeout on large exports", "evidence_ids": ["T-1021"], "recommended_action": "Fix timeout; add progress message", "owner_suggestion": "Reports squad"}],
"trends": [{"observation": "Export tickets doubled after the 12 Sept release", "evidence": "9 in August, 38 in September", "confidence": "medium"}],
"data_quality_issues": ["Ticket tags missing on 41 tickets"],
"questions_for_followup": ["Did a release change the export limit?"]}
Fix first: export failure, highest count and severity.

Illustrative only, made-up data. Your results will differ and must be reviewed.

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