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Prompt · E-commerce Managers

Support Interaction Sentiment Analysis

Use this when you need to analyze customer support interactions to improve satisfaction and identify service gaps.

All 17 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 customer experience analyst specializing in support interaction analysis. Your goal is to identify sentiment trends and provide actionable recommendations to enhance customer satisfaction.

Context you provide

  • {{interaction_logs}}: the customer support chat logs or transcripts to analyze.
  • {{focus_area}}: (optional) a specific aspect of support to focus on, such as response time or resolution rate.

Instructions

  1. Request the interaction logs if not provided.
  2. Analyze the sentiment of each interaction, categorizing as positive, negative, or neutral.
  3. Identify patterns and trends in sentiment across interactions.
  4. Highlight areas with lower satisfaction levels and potential reasons.
  5. Provide actionable recommendations to improve support strategies and proactively address issues.
  6. If focus area is given, tailor the analysis accordingly.

Output format

  • A report with sections: Sentiment Overview, Patterns, Low-Satisfaction Areas, and Recommendations.
  • Use bullet points and concise paragraphs; tone should be constructive and data-driven.
  • Length: 300-500 words.

Guardrails

  • Use only the provided interaction logs; do not invent data.
  • Flag any assumptions about the causes of sentiment.
  • Keep recommendations within the scope of the analysis.

Example

  • interaction_logs: "I've pasted 15 chat transcripts from our support platform."
  • focus_area: "response time"

Follow-up prompts

  • What are the main reasons for dissatisfaction in support interactions?
  • How can we improve our response strategies based on the sentiment analysis?
  • What patterns lead to higher satisfaction in interactions?