Your customers are telling you exactly what they need. The challenge is hearing the signal through the noise. With feedback scattered across NPS surveys, support tickets, app store reviews, and social mentions, synthesizing insights manually takes weeks.
AI changes this equation. But “analyze this feedback” yields generic summaries. This framework teaches you to prompt AI for the deep insights that actually inform product decisions, improve customer experience, and reduce churn.
Before
Summarize this customer feedback.
After
Analyze this customer feedback using the VOICE framework: FEEDBACK DATA: [Paste 50-100 feedback items with source labels] ANALYSIS STRUCTURE: 1. VOLUME ASSESSMENT - Total responses by channel (NPS, reviews, tickets, surveys) - Time period and any notable spikes - Response rate and sample representativeness 2. THEME ORGANIZATION - Group into 5-7 major themes - Count mentions per theme - Identify sub-themes within each category 3. SENTIMENT MAPPING - Overall sentiment distribution (positive/neutral/negative) - Sentiment by theme (which areas drive satisfaction vs. frustration?) - Intensity indicators (mild concern vs. strong emotion) 4. PRIORITY SCORING For each theme, rate on: - Frequency (how often mentioned) - Severity (impact on customer experience) - Strategic fit (alignment with our roadmap) - Effort (complexity to address) 5. ACTION EXTRACTION - Top 3 quick wins (high impact, low effort) - Top 3 strategic initiatives (high impact, requires planning) - Trends requiring monitoring Format as an executive summary followed by detailed analysis.
Types of Feedback Data
Different feedback sources reveal different insights. Understanding each helps you structure better prompts.
- NPS Surveys: Quantitative loyalty score plus qualitative comments. Best for tracking trends over time and identifying promoters vs. detractors.
- Customer Reviews: App stores, G2, Capterra. Public, unfiltered, often emotional. Great for competitive positioning and feature discovery.
- Support Tickets: Real problems with context. High signal for usability issues, bugs, and documentation gaps.
- Surveys: Structured responses to specific questions. Best for hypothesis validation and targeted research.
- Social Mentions: Twitter, Reddit, LinkedIn. Unsolicited opinions. Reveals brand perception and emerging issues.
Insight
Combine multiple sources for complete picture. NPS tells you who is happy, support tickets tell you why others are not, and reviews tell you how you compare.
The VOICE Framework
VOICE stands for Volume, Organize, Identify, Categorize, Extract. Each step builds context for deeper analysis.
1
Volume Assessment
Quantify the feedback. How many responses? What channels? What time period? Understanding scale helps prioritize analysis depth.
2
Organize by Theme
Group feedback into meaningful categories. Usability, pricing, features, support, onboarding. Let patterns emerge from the data.
3
Identify Sentiment
Beyond positive/negative. Detect frustration intensity, enthusiasm level, and emotional triggers. Context matters as much as polarity.
4
Categorize Priority
Not all feedback is equal. Weight by customer value, frequency of mention, alignment with strategy, and effort to address.
5
Extract Action Items
Turn insights into decisions. What should product build? What should support improve? What messaging needs updating?
Pro Tip
Process feedback in batches of 50-100 items for best results. Too few misses patterns. Too many overwhelms the analysis.
Volume Assessment
Start by understanding the scope and composition of your feedback data.
Volume Assessment Prompt
Assess the volume and composition of this customer feedback: [PASTE YOUR FEEDBACK DATA] ANALYZE: 1. TOTAL COUNT - How many feedback items total? - Breakdown by source/channel 2. TIME DISTRIBUTION - What date range does this cover? - Any notable spikes or patterns in submission timing? 3. CUSTOMER SEGMENTS - Can you identify different customer types (based on context)? - Any patterns by segment? 4. RESPONSE QUALITY - What percentage are substantive (more than 1 sentence)? - What percentage are actionable (contain specific feedback)? 5. SAMPLE ASSESSMENT - Is this representative or biased toward any sentiment? - What gaps in coverage do you notice? Summarize findings in 3-4 bullet points.
Organize by Theme
Group feedback into meaningful categories that align with your product and team structure.
Theme Organization Prompt
Organize this customer feedback into themes: [PASTE YOUR FEEDBACK DATA] INSTRUCTIONS: 1. Read through all feedback items 2. Identify 5-7 major themes that emerge naturally 3. For each theme, identify 2-3 sub-themes 4. Categorize every feedback item (items can belong to multiple themes) OUTPUT FORMAT: THEME 1: [Theme Name] (X mentions, Y% of total) Sub-themes: - [Sub-theme A]: X mentions - [Sub-theme B]: X mentions Representative quotes: - "[Actual quote from feedback]" - "[Actual quote from feedback]" [Repeat for each theme] UNCATEGORIZED: - List any feedback that does not fit cleanly into themes THEME RELATIONSHIPS: - Which themes often appear together? - What dependencies exist between themes?
Identify Sentiment
Go beyond simple positive/negative classification. Understand the intensity and emotional drivers.
Sentiment Analysis Prompt
Analyze sentiment in this customer feedback: [PASTE YOUR FEEDBACK DATA] SENTIMENT DIMENSIONS: 1. OVERALL DISTRIBUTION - Positive (satisfied, enthusiastic, grateful): X% - Neutral (informational, mixed, balanced): X% - Negative (frustrated, disappointed, angry): X% 2. SENTIMENT BY THEME For each theme identified earlier: | Theme | Positive | Neutral | Negative | Dominant Emotion | Create this table with actual counts 3. INTENSITY ANALYSIS - High intensity negative (urgency, frustration, anger): [List specific quotes and issues] - High intensity positive (enthusiasm, loyalty, advocacy): [List specific quotes and drivers] 4. EMOTIONAL TRIGGERS - What specific features/experiences trigger strong positive emotion? - What specific issues trigger strong negative emotion? - What language patterns indicate at-risk customers? 5. SENTIMENT TRENDS - Any patterns in sentiment over time? - Sentiment differences by customer segment? - Sentiment correlation with specific product areas? Summarize the emotional landscape in 2-3 paragraphs.
Warning
Do not mistake frequency for importance. One enterprise customer's detailed feedback may matter more than ten brief positive reviews.
Categorize Priority
Weight feedback by impact to focus on what matters most.
Priority Scoring Prompt
Prioritize the themes from this customer feedback analysis: [PASTE YOUR THEME AND SENTIMENT ANALYSIS] CONTEXT: - Our strategic priorities: [LIST YOUR PRIORITIES] - Current product focus: [YOUR FOCUS AREAS] - Resource constraints: [ANY LIMITATIONS] PRIORITY SCORING: For each theme, score 1-5 on: 1. FREQUENCY (how often mentioned) 1 = Rare (<5% of feedback) 5 = Pervasive (>30% of feedback) 2. SEVERITY (impact on customer experience) 1 = Minor inconvenience 5 = Blocking/causing churn 3. STRATEGIC FIT (alignment with roadmap) 1 = Not aligned with direction 5 = Core to strategy 4. EFFORT (complexity to address) 1 = Simple fix 5 = Major initiative required PRIORITY MATRIX: | Theme | Frequency | Severity | Strategic Fit | Effort | Priority Score | Calculate Priority Score = (Frequency + Severity + Strategic Fit) - (Effort * 0.5) TOP PRIORITIES: - List themes by priority score, highest first - For each, explain the scoring rationale QUICK WINS (High frequency/severity, low effort): - What can be addressed this quarter? STRATEGIC INVESTMENTS (High priority, higher effort): - What requires roadmap planning?
Extract Action Items
Transform insights into specific, assignable actions for your team.
Action Extraction Prompt
Convert this feedback analysis into action items: [PASTE YOUR PRIORITIZED ANALYSIS] TEAM STRUCTURE: - Product: [FOCUS AREAS] - Engineering: [CURRENT CAPACITY] - Support: [CURRENT PRIORITIES] - Marketing: [MESSAGING FOCUS] GENERATE ACTION ITEMS: 1. PRODUCT ACTIONS For each product-related theme: - Specific feature/improvement recommendation - User story format: "As a [user], I want [feature] so that [benefit]" - Success metric to track 2. SUPPORT ACTIONS - Documentation gaps to fill - Common issues to create canned responses for - Training needs identified 3. MARKETING ACTIONS - Messaging adjustments needed - Competitive positioning insights - Success stories to highlight 4. PROCESS IMPROVEMENTS - Feedback collection gaps to address - Response time or quality issues - Internal communication improvements OUTPUT FORMAT: | Action | Owner | Priority | Effort | Due Date Suggestion | Provide 3-5 actions per team, prioritized by impact.
Prompt Templates for Specific Use Cases
These templates address common feedback analysis scenarios.
Feature Request Synthesis
Synthesize feature requests from this customer feedback: [PASTE FEEDBACK DATA] ANALYSIS: 1. FEATURE REQUEST INVENTORY - List every distinct feature request mentioned - Count frequency of each request - Note customer segment requesting (if identifiable) 2. GROUPING - Group similar requests into feature categories - Identify underlying needs (not just surface requests) - Note conflicts between requests 3. VALIDATION SIGNALS - Which requests come from paying customers vs. free users? - Which requests include specific use cases? - Which requests reference competitor capabilities? 4. RECOMMENDATION For top 5 feature requests: - Describe the feature clearly - Explain the user need it addresses - Estimate impact (number of customers affected) - Note any risks or concerns - Suggest MVP scope Output as a prioritized feature request report.
Executive Summary
Generate an executive summary of this customer feedback: [PASTE YOUR COMPLETE ANALYSIS] TARGET AUDIENCE: [Leadership team / Board / All-hands] TIME PERIOD: [Month/Quarter covered] EXECUTIVE SUMMARY FORMAT: ## Key Metrics - Total feedback analyzed: X - Overall sentiment: X% positive, X% negative - NPS trend: [if applicable] ## What Customers Love (Top 3) Brief bullet points with supporting data ## Critical Issues (Top 3) Brief bullet points with business impact ## Action Items Underway What are we doing about the issues? ## Recommendations What decisions do we need leadership input on? ## Outlook What trends are emerging? What should we watch? Keep total length under 1 page. Use data to support every claim. Write in clear, jargon-free language.
Churn Risk Analysis
Identify churn risk signals in this customer feedback:
[PASTE FEEDBACK DATA]
CHURN INDICATORS TO DETECT:
1. EXPLICIT SIGNALS
- Mentions of cancellation or leaving
- Comparisons to competitors with preference for alternative
- Statements about switching or evaluating options
- Deadline ultimatums ("if X is not fixed by...")
2. IMPLICIT SIGNALS
- Repeated complaints about same issue
- Decreasing engagement/feedback frequency
- Frustration escalation over time
- Loss of advocacy language
3. AT-RISK CUSTOMERS
List customers showing multiple signals:
| Customer/ID | Signals Present | Risk Level | Key Issue |
4. INTERVENTION OPPORTUNITIES
For high-risk customers:
- What specific action could retain them?
- Who should reach out?
- What timeline is critical?
5. SYSTEMIC ISSUES
- What patterns appear across at-risk customers?
- What structural changes would reduce churn risk?
Prioritize by customer value and recovery likelihood.Working with Different Feedback Sources
Each feedback channel has unique characteristics. Adjust your prompts accordingly.
- NPS Data: Include the score with each comment. Ask AI to correlate themes with score ranges. Detractors (0-6) reveal problems, promoters (9-10) reveal what to amplify.
- Support Tickets: Include ticket metadata (category, priority, resolution time). Ask for patterns in escalations and repeat contacts.
- App Store Reviews: Include star rating and version number. Ask for analysis of how sentiment changes across versions.
- Survey Responses: Include the original questions. Ask AI to note where responses conflict with or validate structured answers.
- Social Mentions: Include the platform and engagement metrics. Ask for analysis of what content spreads and why.
Pro Tip
Label each feedback item with its source. This allows AI to identify patterns specific to channels and weight feedback appropriately.
Presenting Insights to Stakeholders
Different audiences need different formats. Use these prompts to generate appropriate presentations.
Stakeholder Presentation Prompt
Reformat this feedback analysis for [AUDIENCE]: [PASTE YOUR ANALYSIS] AUDIENCE OPTIONS: - "product team": Technical details, feature specifications, user stories - "executives": High-level trends, business impact, decisions needed - "customer success": Individual customer concerns, intervention opportunities - "marketing": Messaging insights, competitive positioning, testimonials - "all-hands": Balanced view, wins and challenges, team recognition INCLUDE: - Visualizations to create (describe charts/graphs needed) - Key metrics to highlight - Talking points for discussion - Questions to anticipate - Follow-up actions to propose Format for a [15-minute presentation / one-page summary / Slack update].
Closing the Feedback Loop
Analysis without action wastes customer trust. Close the loop to show customers their voice matters.
Close the Loop Prompt
Generate feedback loop communications: CONTEXT: - Issue addressed: [DESCRIBE THE ISSUE] - Solution implemented: [DESCRIBE THE FIX] - Customers affected: [SEGMENT DESCRIPTION] - Timeline: [WHEN IT WAS FIXED] GENERATE: 1. INDIVIDUAL RESPONSE For customers who reported this specific issue: - Acknowledge their feedback - Explain what we changed - Thank them for helping improve the product - Invite further feedback 2. CHANGELOG ENTRY For public release notes: - Clear description of improvement - Why we made this change (customer feedback!) - How to use the new feature/fix 3. SOCIAL ANNOUNCEMENT For Twitter/LinkedIn: - Celebrate the improvement - Credit customer feedback - Encourage more input 4. INTERNAL UPDATE For team communication: - What we learned from this feedback - How quickly we responded - What this means for our feedback process Tone: Genuine, appreciative, not corporate-speak.
Insight
Customers who see their feedback implemented become your strongest advocates. Always close the loop personally when possible.
Next Steps
The VOICE framework transforms scattered feedback into strategic intelligence. AskSmarter.ai guides you through each step with targeted questions tailored to your product and customers.
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