Advanced Analytics and Reporting

Beyond the basic dashboard metrics, Feedback Manager provides rich data for deep analysis. This guide covers advanced analytics techniques, trend identification, custom reporting, and how to use feedback data for strategic product decisions.

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Data Analysis Fundamentals

Types of Feedback Data

Quantitative Metrics:

• Vote counts
• Comment counts
• Submission volume
• Response times
• Engagement scores
• Completion rates

Qualitative Data:

• Feedback content
• User comments
• Category themes
• Sentiment
• Feature requests
• Pain points

Metadata:

• User segments
• Submission dates
• Status history
• Category assignments
• Backlog links
• Integration data

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Trend Analysis

Volume Trends

Tracking Submission Patterns:

Daily Volume:

Analysis: Count submissions per day
Pattern: Identify spikes or drops
Action: Investigate causes

Weekly Patterns:

Monday: Often highest (weekend accumulation)
Friday: Often lower (end of week)
Weekend: Typically lowest

Monthly Cycles:

Month-end: May spike (quarter/month reviews)
Post-release: Spike after product launches
Holiday periods: Typically lower

Calculating Growth Rate:

Growth Rate = ((Current Period - Previous Period) / Previous Period) × 100

Example:
Last month: 150 items
This month: 180 items
Growth: ((180 - 150) / 150) × 100 = 20%

Engagement Trends

Vote Velocity Analysis:

Rising Items Detection:

Velocity = Votes in Last 7 Days / Total Days Since Creation

High Velocity (>5 votes/day): Trending
Medium Velocity (2-5 votes/day): Growing interest
Low Velocity (<2 votes/day): Stable

Engagement Decay:

Pattern: Items get most votes in first 30 days
Analysis: Compare vote rate over time
Action: Re-surface old valuable items

Comment Activity:

Active Discussion: >5 comments in last 7 days
Moderate Activity: 2-5 comments
Low Activity: <2 comments
Dormant: No comments in 30+ days

Category Trends

Category Distribution Analysis:

Current Distribution:

Feature Requests: 45%
Bug Reports: 30%
Performance: 15%
UI/UX: 10%

Trend Over Time:

Month 1: Bugs 50%, Features 30%
Month 2: Bugs 40%, Features 40%
Month 3: Bugs 30%, Features 45%

Interpretation: Product stabilizing, users requesting features

Emerging Categories:

New category gaining >5% share in 30 days
Example: "Mobile" category growing from 2% to 12%
Action: Investigate mobile-specific issues

Segment Trends

Engagement Score Changes:

Month-over-Month:

Enterprise: 50.0 → 55.0 (+10% improvement)
SMB: 32.0 → 28.0 (-12.5% decline)
Free Tier: 8.0 → 12.0 (+50% improvement)

Interpretation:

• Enterprise: Increased engagement, good sign
• SMB: Declining engagement, needs attention
• Free Tier: Strong growth, potential for conversion

Segment Shift Analysis:

Track which segments are growing/shrinking
Identify segments changing behavior
Correlate with product changes or campaigns

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Comparative Analysis

Time Period Comparisons

Week-over-Week:

Metric: Total Feedback
This week: 45 items
Last week: 38 items
Change: +7 items (+18.4%)

Month-over-Month:

Metric: Average Response Time
This month: 4.2 hours
Last month: 5.8 hours
Change: -1.6 hours (-27.6% improvement)

Year-over-Year:

Metric: Backlog Link Rate
This year: 35%
Last year: 22%
Change: +13 percentage points (+59% improvement)

Cohort Analysis

Submission Cohorts:

Definition:

Group feedback by submission month
Track lifecycle metrics for each cohort

Example Analysis:

January Cohort (100 items):
- 30 days: 20% linked to backlog
- 60 days: 45% linked to backlog
- 90 days: 60% linked to backlog
- 120 days: 65% completed

February Cohort (120 items):
- 30 days: 25% linked (improvement!)
- 60 days: 50% linked
- 90 days: 70% linked

Insights:

• February cohort processed faster
• Improvement in linking workflow
• Higher completion rate

User Segment Cohorts:

Track engagement by segment over time
Identify which segments stay engaged
Measure retention and churn patterns

Competitive Benchmarking

Internal Benchmarks:

Your Product A: 4.2h avg response time
Your Product B: 6.5h avg response time
Target: Match Product A performance

Industry Benchmarks:

Your Performance: 4.2h avg response time
Industry Average: 8-12h
Your Position: Top quartile

Best-in-Class:

Your Backlog Link Rate: 35%
Best Practice: 40-50%
Gap: 5-15 percentage points
Action: Improve linking workflow

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Advanced Metrics

Feedback Quality Score

Formula:

Quality Score = (
  (Vote Count × 0.4) +
  (Comment Count × 0.3) +
  (Detail Level × 0.2) +
  (Clarity × 0.1)
) / 4

Detail Level: 0-10 (based on description length/completeness)
Clarity: 0-10 (subjective or NLP-based)

Use Cases:

• Prioritize high-quality feedback
• Identify power users
• Improve submission guidelines

Customer Influence Score

Formula:

Influence Score = (
  (Feedback Submitted × 1) +
  (Votes Given × 0.5) +
  (Comments Made × 0.7) +
  (Items Implemented × 2)
)

Interpretation:

• High score: Influential customer, engage closely
• Medium score: Active participant
• Low score: Passive user or new customer

Feedback-to-Feature Ratio

Formula:

F2F Ratio = (Features Shipped from Feedback / Total Feedback) × 100

Example:

Total Feedback: 500 items
Features Shipped: 75 items
F2F Ratio: (75 / 500) × 100 = 15%

Benchmarks:

• Excellent: >20%
• Good: 15-20%
• Acceptable: 10-15%
• Needs Improvement: <10%

Response Efficiency

First Response Rate:

FRR = (Items with Response / Total Items) × 100
Target: >95%

Resolution Rate:

RR = (Completed + Resolved Items / Total Items) × 100
Target: Steady increase, >50% after 6 months

Time to Resolution:

TTR = Average time from submission to completion
Varies by priority:
- Critical: <7 days
- High: <30 days
- Medium: <90 days
- Low: <180 days

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Reporting Strategies

Executive Dashboard

Key Metrics:

1. Total Feedback (trend)
2. Top 5 Feature Requests (by votes)
3. Critical Issues Count
4. Backlog Link Rate
5. Customer Satisfaction Proxy (engagement)

Frequency: Monthly

Format: Visual dashboard with charts

Insights:

• Overall health of feedback system
• Customer priorities
• Team responsiveness
• Product-market fit indicators

Product Team Report

Key Metrics:

1. Rising Items (last 30 days)
2. Category Distribution
3. Segment-Specific Requests
4. Backlog Alignment
5. Feature Request Themes

Frequency: Weekly

Format: Detailed report with analysis

Insights:

• What to build next
• Emerging trends
• Customer segment needs
• Roadmap validation

Customer Success Report

Key Metrics:

1. Response Time Trends
2. Unprocessed Count
3. Customer-Specific Feedback
4. At-Risk Accounts (low engagement)
5. Champion Accounts (high engagement)

Frequency: Weekly

Format: Action-oriented list

Insights:

• Which customers need outreach
• Response performance
• Engagement health
• Churn risk indicators

Engineering Team Report

Key Metrics:

1. Bug Reports (by severity)
2. Technical Debt Items
3. Performance Issues
4. Integration Requests
5. Backlog Progress

Frequency: Daily/Weekly

Format: Prioritized list

Insights:

• What to fix first
• Technical priorities
• Resource allocation
• Progress tracking

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Data Export and Integration

Export Options

CSV Export:

Fields:
- ID, Title, Description
- Type, Category, Status
- Votes, Comments
- Created Date, Updated Date
- User Segment, User Email
- Backlog Link

Use Cases:

• Offline analysis in Excel
• Import to BI tools
• Archive for compliance
• Share with stakeholders

API Access:

GET /feedback-items?filters=...
Returns: JSON array of items
Use: Custom integrations, automation

BI Tool Integration

Supported Tools:

• Tableau
• Power BI
• Looker
• Google Data Studio
• Metabase

Integration Methods:

1. Direct API: Real-time data
2. Scheduled Export: Daily/weekly CSV
3. Database Connector: Direct DB access (if available)

Custom Dashboards:

Create visualizations:
- Trend charts
- Heatmaps
- Funnel analysis
- Cohort retention
- Segment comparison

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Predictive Analytics

Churn Prediction

Indicators:

Low engagement score (declining)
No feedback in 60+ days
Declining vote activity
Unresolved critical issues
Approaching renewal date

Risk Score:

Risk Score = (
  (Days Since Last Activity × 0.3) +
  (Unresolved Critical Issues × 0.4) +
  (Engagement Score Decline × 0.3)
)

High Risk: >70
Medium Risk: 40-70
Low Risk: <40

Feature Success Prediction

Pre-Launch Indicators:

High vote count (>100)
Multiple customer segments requesting
Rising trend (not just old item)
Clear, well-defined request
Alignment with strategy

Success Probability:

High (>80%): All indicators positive
Medium (50-80%): Most indicators positive
Low (<50%): Few indicators positive

Trend Forecasting

Volume Forecasting:

Method: Linear regression on historical data
Formula: y = mx + b
Use: Resource planning, capacity management

Example:

Historical: 100, 120, 140, 160 items/month
Trend: +20 items/month
Forecast: Next month = 180 items

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Actionable Insights

Identifying Quick Wins

Criteria:

High votes (>50)
Low effort (engineering estimate)
Clear requirements
Multiple segments requesting
Not currently on roadmap

Quick Win Score:

QW Score = (Vote Count × Segment Count) / Effort Estimate

Example:
Votes: 80
Segments: 3
Effort: 5 (days)
QW Score: (80 × 3) / 5 = 48

High Score (>40): Prioritize
Medium Score (20-40): Consider
Low Score (<20): Defer

Finding Hidden Gems

Characteristics:

Moderate votes (20-50)
High comment activity (>10)
Detailed discussions
Technical feasibility confirmed
Strategic alignment

Why Hidden:

• Newer submission (less time to accumulate votes)
• Niche but valuable
• Complex (requires discussion)
• Not obvious at first glance

Discovery Method:

1. Filter: Discussed tab
2. Review high-comment items
3. Read discussions for depth
4. Evaluate strategic fit
5. Consider despite lower votes

Detecting Patterns

Clustering Similar Feedback:

Manual Method:

1. Group by category
2. Read titles for themes
3. Identify common keywords
4. Create theme tags

Automated Method (if available):

NLP-based clustering
Keyword extraction
Similarity scoring
Automatic grouping

Pattern Examples:

Pattern: "Mobile app performance"
Items: 15 separate submissions
Action: Create epic, link all items

Pattern: "Export to Excel"
Items: 8 requests from enterprise segment
Action: Prioritize for enterprise tier

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Best Practices

Regular Analysis Cadence

Daily:
✅ Check volume trends
✅ Monitor response times
✅ Review unprocessed count

Weekly:
✅ Analyze rising items
✅ Review segment engagement
✅ Update team reports

Monthly:
✅ Trend analysis
✅ Executive reporting
✅ Strategic planning

Quarterly:
✅ Cohort analysis
✅ Benchmark review
✅ Process optimization

Data Quality

✅ Consistent categorization - Accurate analysis
✅ Complete metadata - Rich insights
✅ Regular cleanup - Remove duplicates
✅ Validate exports - Ensure data integrity

Insight Communication

✅ Visualize data - Charts over tables
✅ Tell stories - Context over numbers
✅ Actionable recommendations - What to do next
✅ Regular cadence - Consistent reporting

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Tools and Techniques

Spreadsheet Analysis

Excel/Google Sheets:

Pivot Tables: Category distribution
Charts: Trend visualization
Formulas: Custom calculations
Conditional Formatting: Highlight patterns

Example Analysis:

1. Export feedback to CSV
2. Create pivot table by category
3. Add vote count sum
4. Create bar chart
5. Identify top categories

SQL Queries (if DB access)

Custom Analysis:

-- Top categories by vote count
SELECT category, SUM(votes) as total_votes
FROM feedback_items
WHERE created_at > DATE_SUB(NOW(), INTERVAL 30 DAY)
GROUP BY category
ORDER BY total_votes DESC
LIMIT 10;

Statistical Analysis

Correlation Analysis:

Question: Does engagement correlate with retention?
Method: Calculate correlation coefficient
Data: Engagement score vs. renewal rate

Significance Testing:

Question: Is the improvement statistically significant?
Method: T-test or Chi-square
Data: Before/after process change

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Advanced analytics transforms raw feedback data into strategic insights. Regular analysis helps you stay ahead of trends, make data-driven decisions, and demonstrate the value of your feedback program to stakeholders.