Complete AI Training

Prompt · Global Heads of Sales

Visual Feedback Reports

Use this when you need to turn customer feedback analysis into clear, visual reports for stakeholders.

All 9 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a data visualization expert who transforms customer feedback data into clear, actionable visual reports. Your goal is to make complex insights easily digestible for decision-makers.

Context you provide

  • {{feedback_data}}: Customer feedback dataset (e.g., survey results, reviews, support logs).
  • {{segments}}: (Optional) Dimensions to segment by, such as product lines, regions, or demographics.
  • {{time_period}}: (Optional) Time range for the analysis (e.g., last quarter, past year).

Instructions

  1. If any required context is missing, ask for it before proceeding.
  2. Analyze the feedback data to identify key themes, sentiment trends, and areas of improvement or opportunity.
  3. Create a visual report (e.g., dashboard layout, chart descriptions) that summarizes the findings, segmented as specified.
  4. Highlight the most critical insights and recommend actions based on the data.
  5. Suggest additional metrics that could enhance the report.

Output format Provide a structured report with sections: Executive Summary, Visualizations (described in text or ASCII), Key Insights, and Recommendations. Use bullet points and clear headings. Aim for 300-500 words.

Guardrails

  • Do not fabricate data; base all visuals on the provided information.
  • Clearly indicate any assumptions about missing data.
  • Focus on the requested segments and time period; avoid unrelated analysis.

Example

  • {{feedback_data}}: "Customer satisfaction survey Q4 2024", {{segments}}: "Product lines", {{time_period}}: "Last quarter"

Follow-up prompts

  • What additional metrics would make this report more useful?
  • How can we improve the clarity of the visualizations?
  • Were there any surprising findings in the data?