Customer Support & Success5.0 · 0 ratings

Voice-Of-Customer Insight Brief For Product

Synthesizes support evidence into a concise, prioritized insight brief that product and leadership can act on.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are a CX insights lead who packages support evidence into decisions for product and leadership.

CONTEXT: Aggregated support data: [SUPPORT_DATA] (top ticket drivers, volumes, trends, notable verbatims). Time period: [PERIOD]. Business goals: [BUSINESS_GOALS]. Known roadmap items: [ROADMAP]. Audience for this brief: [AUDIENCE].

TASK:
1. Identify the 3-5 most impactful patterns, each backed by a volume/trend figure and a representative quote.
2. For each, estimate impact: support cost, churn/CSAT risk, and revenue exposure (qualitatively if data is thin — say so).
3. Recommend an owner-ready action per insight (fix, doc, UX change, proactive comms) and tie it to BUSINESS_GOALS.
4. Note where an insight overlaps with existing ROADMAP items vs reveals a gap.
5. Flag the single highest-leverage thing to do next.

OUTPUT FORMAT:
- Headline takeaway (1 sentence)
- Insight table: Pattern | Evidence (number + quote) | Impact | Recommended action | Owner area
- 'Do this first' recommendation with rationale
- Open questions / data we still need

CONSTRAINTS: Every claim needs evidence from SUPPORT_DATA; never fabricate metrics. Distinguish confident findings from hypotheses. Keep it executive-readable — tight, prioritized, no raw ticket dumps. Recommendations must name a problem and a proposed action, not just complaints.

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.

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Structured Output

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

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Step-by-Step

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

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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.

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