Audit intermediate Works with: Any AI

Post-Event Feedback Survey Results Audit

Table output

Analyse event feedback responses for response rate, bias and recurring themes, and turn them into prioritised changes with owners.

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I ran an event and collected feedback. Please audit the results honestly, including how far they can be trusted, and turn them into improvements for next time.

Inputs: the questions asked [SURVEY_QUESTIONS], the responses, pasted as rows or summary counts [SURVEY_RESPONSES], number of attendees [ATTENDEE_COUNT], number of people invited to answer [INVITED_COUNT], how and when the survey was sent [SURVEY_METHOD_AND_TIMING], and my goals for the event [EVENT_GOALS].

Provide: (1) response rate and its arithmetic, plus a comment on who may be missing (for example only happy or only upset people answering); (2) a question-by-question table with columns Question, Responses, Average or most common answer, Spread, What it can and cannot tell us; (3) a theme list from open comments, grouped, with counts and two short quotes each, kept anonymous; (4) a list of survey design problems such as leading wording, double-barrelled questions or too many questions; (5) a prioritised action table with columns Action, Evidence, Effort (low/medium/high), Impact, Suggested owner, Due. Limit it to eight actions. Add a three-sentence summary I can share with attendees.

Guardrails: do not overstate small samples; with fewer than 20 answers, label results as indicative only. Do not invent responses or percentages. Never identify individuals from comments.

Add a worked example with invented numbers: 120 attendees, 54 responses, 8 comments mention long queues. Show 45 percent response rate and 15 percent of respondents mentioning queues.

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