--- name: product-feedback-routing description: Extract product feedback from support tickets, surveys, and community forums, then route to product and engineering teams for prioritization. Use when building feedback loops between support and product teams, extracting feature requests from tickets, prioritizing product improvements based on customer feedback, or measuring support-driven product impact. Triggers on phrases like "product feedback", "feedback routing", "feature request extraction", "support to product", "customer feedback loop", "product improvement", "voice of customer product", "feedback prioritization". --- # Product Feedback Extraction & Routing Extract actionable product feedback from support interactions and route to product teams. ## Workflow ### Feedback Extraction Pipeline Trigger: Weekly batch processing; monthly product review; quarterly roadmap planning: 1. **Data collection**: Aggregate feedback sources — support tickets (tags: feature_request, bug_report, improvement), survey responses (NPS comments, CSAT comments), community forum posts (feature request category), in-app feedback widgets, sales notes, customer success interview notes. 2. **Classification**: Categorize feedback — Bug (broken functionality), Feature Request (new capability), Improvement (existing feature enhancement), Documentation (missing/poor docs), UX (usability issue), Performance (speed/reliability); classify by product area and severity. 3. **Deduplication**: Group similar feedback — semantic similarity matching; cluster by theme; count unique customers requesting same feature; identify priority customers (enterprise, high revenue). 4. **Impact scoring**: Calculate priority score — (customer count × revenue weight × sentiment urgency × strategic alignment); rank feedback items; identify top 10–20 for product review. 5. **Routing to product team**: Submit prioritized list to product manager via shared tool (Jira, Aha!, ProductBoard); include: customer quotes, revenue impact, ticket volume, trend data; request status update (Planned, Backlog, Declined). 6. **Status tracking**: Track feedback items through product lifecycle — Received → Under Review → Planned → In Development → Released → Closed; update support team on status changes. 7. **Customer communication**: When feature released, notify requesting customers ("You asked, we built!"); close related tickets; update knowledge base; share in community forum. 8. **Closed-loop reporting**: Monthly report to support team — what feedback was implemented, what's planned, what was declined (with rationale); quarterly report to leadership on support-driven product impact. ### Feedback Classification Framework ``` PRODUCT FEEDBACK CLASSIFICATION ================================= Category 1: Bug Reports Definition: Product doesn't work as expected; breaks; errors Priority: High (fix ASAP) Data collected: Steps to reproduce, error message, browser/OS, frequency Example: "Dashboard crashes when I click 'Export to PDF'" Routing: Engineering bug tracker (Jira) — Priority based on severity Category 2: Feature Requests Definition: New capability that doesn't exist Priority: Medium–High (based on demand) Data collected: Use case description, expected outcome, alternative solutions tried Example: "I want to integrate with Shopify automatically" Routing: Product roadmap tool (Aha!, ProductBoard) — scored and prioritized Category 3: Improvements Definition: Existing feature needs enhancement Priority: Medium (based on impact) Data collected: Current behavior, desired behavior, impact on workflow Example: "The search function is too slow with large datasets" Routing: Product roadmap tool — grouped with similar improvement requests Category 4: Documentation Definition: Missing, unclear, or outdated documentation Priority: Low–Medium (quick win) Data collected: Missing topic, current doc link, suggested content Example: "No documentation on how to set up webhooks" Routing: Documentation team — weekly review Category 5: UX Issues Definition: Confusing interface, poor workflow, accessibility concern Priority: Medium (based on frequency) Data collected: Screen location, expected vs. actual experience, user role Example: "The 'Delete' button is too close to 'Save' — I almost deleted by mistake" Routing: Design team — UX review cycle Category 6: Performance Definition: Slow loading, timeouts, resource issues Priority: High (if widespread) Data collected: Page/feature, load time, data volume, browser/OS Example: "Report generation takes 5+ minutes with 10K records" Routing: Engineering — performance team ``` ### Feedback Impact Scoring ``` FEEDBACK PRIORITY SCORING MODEL ================================= Score = (Customer Count × 2) + (Revenue Weight × 3) + (Urgency × 2) + (Strategic Fit × 2) Customer Count (1–10): 10 = 50+ customers requesting 7 = 20–49 customers 5 = 10–19 customers 3 = 5–9 customers 1 = 1–4 customers Revenue Weight (1–10): 10 = Top 10% of customers by ARR requesting 7 = Enterprise customers requesting 5 = Mix of tiers requesting 3 = Mostly mid-tier 1 = Mostly free/basic tier Urgency (1–10): 10 = Blocking workflow / causing churn 7 = Significant productivity impact 5 = Annoying but workable 3 = Nice to have 1 = Low priority Strategic Fit (1–10): 10 = Directly aligns with product roadmap 7 = Aligns with company strategy 5 = Neutral / doesn't conflict 3 = Partial alignment 1 = Conflicts with strategy Priority Thresholds: Score ≥ 25: Must have (include in next roadmap review) Score 15–24: Should have (add to backlog, review quarterly) Score 10–14: Could have (review annually) Score < 10: Won't have (communicate rationale to customer) Monthly Report to Product Team: - Top 10 feedback items by score - New requests since last month (count + summary) - Requests approaching priority threshold - Released features that were support-requested - Customer quotes for top requests ``` ## Edge Cases - **Conflicting feedback** (different customers want opposite features): - Example: "Make the interface simpler" vs. "Add more advanced features" - Strategy: Segment by customer tier — SMB wants simplicity; Enterprise wants power; build configurable interface - Communication: Acknowledge both perspectives; explain segmentation approach - Product approach: Tiered features; configurable complexity; progressive disclosure - Decision: When conflict exists, prioritize by revenue impact and strategic fit - **High-volume low-value requests** (many customers request low-impact feature): - Example: 100 basic-tier customers request feature that won't drive retention or expansion - Strategy: Score model weights revenue; high volume of low-value customers may not justify development - Alternative: Community-driven solution; documentation; workaround - Communication: "We've heard this request from many customers. Here's why we prioritized X instead..." - Exception: If feature has viral/referral potential, may still be worth building - **Bug vs. feature request confusion** (customer reports "bug" but it's actually missing feature): - Detection: Engineering review — "This isn't a bug, it's expected behavior / missing feature" - Handling: Reclassify from Bug to Feature Request; communicate to customer ("This is actually a feature request, not a bug") - Customer experience: Manage expectations — bugs get fixed faster; features depend on roadmap - Support guidance: Train agents to distinguish — "Does this used to work?" = bug; "I wish it could do X" = feature - **Competitive feature requests** (customer wants feature because competitor has it): - Detection: Competitor name mentioned in feedback; "Competitor X can do Y" - Strategy: Competitive analysis — is this a table-stakes feature or differentiator? - Urgency: Higher if competitor feature is causing churn or blocking deals - Communication: "We're aware of [competitor] feature and it's on our radar. Here's how we approach this differently..." - Sales coordination: Share competitive intelligence with sales team; prepare battle cards - **Feedback from churned customers** (valuable but may reflect unresolved issues): - Value: Churned customer feedback often most honest and actionable - Handling: Prioritize feedback that addresses churn reason; potential win-back lever - Communication: "We've implemented [feature] based on feedback from customers like you. Would you like to try again?" - Prevention: Exit survey feedback routed to product team with high priority flag - Metric: % of churn reasons that are product-related; track resolution over time ## Integration Points - **Help desk**: Zendesk, Freshdesk, Intercom — ticket data, tags, customer history - **Product management**: Jira, Aha!, ProductBoard, Roadmunk — feature tracking, roadmap planning - **Survey tools**: Delighted, SurveyMonkey, Qualtrics — NPS, CSAT, feedback collection - **Community**: Discourse, Circle.so — forum posts, feature request voting - **In-app feedback**: Canny, UserVoice, custom widget — in-product feedback collection - **CRM**: Salesforce, HubSpot — customer data, account tier, churn status - **Analytics**: Mixpanel, Amplitude — usage correlation with feedback - **NLP**: OpenAI, Google Cloud NL — feedback classification, sentiment, theme extraction - **Reporting**: Tableau, Power BI — feedback dashboards, trend analysis - **Data warehouse**: Snowflake, BigQuery — unified feedback data, historical analysis