Voice of Customer Analysis Framework
Market ResearchintermediateClaude 3.5 Sonnet recommended for nuanced pattern recognition across unstructured feedback and ability to extract meaningful themes. GPT-4o also strong for this task with slightly faster processing of large feedback datasets.
When to Use This Prompt
Use this prompt when you have accumulated customer feedback from multiple sources and need to extract strategic insights quickly. Ideal for quarterly business reviews, product roadmap planning, or repositioning marketing messaging to align with actual customer needs and language.
The Prompt
You are a customer insights analyst. Analyze the following customer feedback data to identify patterns, themes, and actionable insights.
## Input Data
Review this customer feedback from [FEEDBACK_SOURCE: reviews, surveys, support tickets, social media, interviews]:
[PASTE_CUSTOMER_FEEDBACK_HERE]
## Analysis Framework
### 1. Theme Identification
Identify the top 5-7 recurring themes or topics customers mention. For each theme, note:
- Frequency (how often mentioned)
- Sentiment (positive, negative, neutral)
- Customer segments most affected
### 2. Pain Points & Opportunities
List the primary pain points customers express. For each:
- Describe the specific problem
- Estimate impact (high/medium/low)
- Suggest one potential solution
### 3. Emotional Drivers
Identify what emotions customers express (frustration, delight, confusion, trust, etc.). Connect emotions to specific product/service aspects.
### 4. Unmet Needs
Highlight gaps between what customers want and what they currently experience. Prioritize by business impact.
### 5. Messaging Opportunities
Extract direct customer language and phrases that resonate. Suggest how marketing can use authentic customer voice in messaging.
## Output Format
Provide analysis in clear sections with bullet points. Use specific customer quotes to support each finding. Conclude with 3 strategic recommendations for [BUSINESS_AREA: product, marketing, customer success, pricing].
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Tips for Best Results
- 1.Paste 50+ data points for reliable pattern detection. Small sample sizes may produce skewed themes that don't represent your full customer base.
- 2.Include metadata like customer segment, product tier, and tenure when possible. This helps identify if pain points are universal or segment-specific.
- 3.Ask the AI to cite specific customer quotes for each finding. This grounds insights in reality and provides ready-to-use content for stakeholder presentations.
- 4.Run this analysis quarterly on new feedback only, then compare themes year-over-year to track whether product changes actually address customer concerns.
Example Output
## Theme Identification
**Theme 1: Onboarding Complexity (Negative, High Frequency)**
- 34% of feedback mentions difficulty getting started
- Affects new customers most, especially SMBs
- Sentiment: Frustrated but willing to persist
**Theme 2: Integration Capabilities (Positive, Medium Frequency)**
- 22% praise native integrations with existing tools
- Enterprise customers mention this most
- Sentiment: Delighted, reduces switching costs
**Theme 3: Customer Support Responsiveness (Mixed)**
- 18% mention slow response times
- 15% praise quick resolution when contacted
- Sentiment: Frustrated when delayed, loyal when helped
## Pain Points & Opportunities
**Pain Point 1: Setup Takes 2-3 Weeks (High Impact)**
- Customers report implementation timeline exceeds expectations
- Direct quote: "We expected to launch in days, not weeks"
- Solution: Create pre-built templates for common use cases
**Pain Point 2: Documentation Gaps (Medium Impact)**
- Advanced features lack clear how-to guides
- Quote: "Had to contact support for basics that should be documented"
- Solution: Video tutorial library for top 10 features
## Messaging Opportunities
Customers use language like "finally found a solution that understands our workflow" and "integrates seamlessly with our stack." Marketing should emphasize workflow-first positioning rather than feature lists. Authentic customer quote for testimonial: "It just works with what we already use."
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