Customer Support & Success5.0 · 0 ratings

Customer Sentiment And Theme Analyzer

Analyzes a batch of support conversations to surface sentiment, recurring themes, and prioritized improvement actions.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a Voice-of-Customer analyst who turns raw support conversations into prioritized, actionable insight.

CONTEXT: Below is a batch of support interactions to analyze: [CONVERSATIONS]. Business priorities this quarter: [PRIORITIES]. Product areas to tag against: [PRODUCT_AREAS].

TASK:
1. Tag each conversation with overall sentiment (Positive / Neutral / Negative) and a confidence note.
2. Cluster the conversations into recurring themes; name each theme and count its frequency.
3. For the top 3 themes, identify the root driver and the likely business impact (churn risk, support cost, CSAT, revenue).
4. Rank themes by a simple frequency-times-impact score and recommend one concrete action per top theme.
5. Surface any single high-severity outlier even if it is rare.

OUTPUT FORMAT:
- Sentiment breakdown (counts + %)
- Theme table: Theme | Frequency | Sentiment skew | Likely driver | Suggested action
- 'Top 3 priorities' ranked with rationale
- 'Watch item' for any severe outlier

CONSTRAINTS: Only use evidence present in CONVERSATIONS; quote a short snippet to justify each top theme. Do not fabricate counts. If the sample is too small to generalize, say so. Tie recommendations back to PRIORITIES where possible.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

Learn this technique
Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

Learn this technique
Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

Learn this technique

Recommended models

claudegpt-4ogemini

Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

Open in Studio How to prompt AI correctly

More in Customer Support & Success

Empathetic Escalation De-Escalation Reply

Crafts a calm, accountable reply that defuses an angry customer, acknowledges the failure, and offers a concrete remedy.

Read prompt

Tiered Triage And Severity Classifier

Classifies an inbound support ticket by severity, category, and routing queue with a justification and suggested SLA.

Read prompt

Knowledge Base Article From A Resolved Ticket

Converts a closed support conversation into a clean, searchable, evergreen help-center article with steps and edge cases.

Read prompt

Churn-Risk Save Outreach For At-Risk Accounts

Drafts a personalized retention outreach to a customer showing churn signals, anchored on their goals and usage data.

Read prompt