Feedback Analyzer

Expert customer feedback analysis system that transforms unstructured feedback into actionable product and service insights. This skill provides structured workflows for collecting, categorizing, analyzing, and acting on customer feedback from multiple sources.

Customer feedback is the most direct signal of what's working and what isn't. But raw feedback is noisy, contradictory, and overwhelming. This skill helps you extract patterns, prioritize themes, and close the feedback loop effectively.

Built on voice-of-customer best practices and qualitative research methods, this skill combines text analysis, pattern recognition, and stakeholder communication to turn feedback into action.

Core Workflows

Workflow 1: Feedback Collection & Aggregation

Gather feedback from all sources into unified view

1. Feedback Sources

   • Direct Surveys: NPS, CSAT, CES, custom surveys
   • Support Channels: Tickets, chat transcripts, calls
   • In-App Feedback: Feature requests, bug reports, ratings
   • Social Media: Mentions, reviews, comments
   • Sales Conversations: Objections, lost deal reasons
   • User Research: Interviews, usability tests
   • Community: Forums, Slack, Discord

2. Data Standardization

   Field	Description
   Source	Where feedback came from
   Date	When received
   Customer ID	Link to customer record
   Segment	Customer type/tier
   Raw Text	Original feedback
   Category	Topic classification
   Sentiment	Positive/neutral/negative
   Priority	Urgency/impact level

3. Collection Automation

   • API integrations with feedback tools
   • Automatic ticket tagging
   • Survey response routing
   • Social listening alerts
   • Scheduled data syncs

4. Quality Filters

   • Remove spam and duplicates
   • Flag potentially inaccurate data
   • Note context (e.g., during outage)
   • Weight by customer segment
   • Identify feedback loops (same issue, multiple channels)

Workflow 2: Categorization & Tagging

Organize feedback into meaningful categories

1. Category Taxonomy

   • Product Features: Specific functionality feedback
   • Usability/UX: Interface and experience issues
   • Performance: Speed, reliability, bugs
   • Pricing/Value: Cost concerns and value perception
   • Support Experience: Service quality feedback
   • Onboarding: Getting started experience
   • Documentation: Help content feedback
   • Integration: Third-party connection issues

2. Subcategory Examples

   Product Features
   ├── Feature Requests
   │   ├── New feature ideas
   │   └── Feature enhancements
   ├── Missing Features
   │   ├── Competitor comparisons
   │   └── Workflow gaps
   └── Feature Feedback
       ├── What works well
       └── What doesn't work
   

3. Tagging Best Practices

   • Use consistent, specific tags
   • Allow multiple tags per feedback
   • Create tag hierarchy (parent/child)
   • Review and consolidate tags quarterly
   • Train team on tagging standards

4. Automated Classification

   • Keyword-based routing rules
   • ML-based topic classification
   • Sentiment detection
   • Priority scoring algorithms
   • Entity extraction (features, pages, actions)

Workflow 3: Sentiment & Urgency Analysis

Understand emotional context and priority

1. Sentiment Classification

   Sentiment	Indicators	Action Level
   Very Negative	Anger, threats to leave	Urgent escalation
   Negative	Frustration, complaints	Address in sprint
   Neutral	Suggestions, questions	Standard review
   Positive	Praise, appreciation	Share with team
   Very Positive	Advocacy, testimonial	Request case study

2. Urgency Scoring Factors

   • Customer tier (enterprise = higher weight)
   • Revenue at risk
   • Frequency of same issue
   • Time sensitivity mentioned
   • Escalation history
   • Regulatory/compliance implications

3. Trend Detection

   • Volume spikes (sudden increase in topic)
   • Sentiment shifts (getting worse/better)
   • New issues emerging
   • Seasonal patterns
   • Release-correlated feedback

4. Alert Triggers

   • High-value customer escalation
   • Sentiment score below threshold
   • Issue volume exceeds normal
   • Churn-risk keywords detected
   • Security/privacy concerns

Workflow 4: Pattern Recognition & Insights

Extract actionable patterns from feedback mass

1. Quantitative Analysis

   • Frequency by category
   • Trend over time
   • Segment distribution
   • Correlation with churn
   • Impact on NPS/CSAT

2. Qualitative Analysis

   • Representative quote extraction
   • Use case pattern identification
   • User journey mapping
   • Pain point articulation
   • Unmet need discovery

3. Insight Synthesis

   Insight Template:
   
   FINDING: [What the data 
   

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