Understanding Signals
Signals are automatically extracted insights from your calls and imported data. BuildBetter detects 35+ signal types including feature requests, bugs, complaints, competitive mentions, and more. No configuration needed - signals extract automatically when calls are processed or data is imported.Example 1: Finding Feature Requests from Customer Calls
Scenario: You want to see all feature requests from the past month to prioritize your roadmap1
Navigate to Signals
Click Signals or Clustering in the main navigation
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Filter by Type
- Click the filter icon or use the query builder
- Select “Type” filter
- Choose “Feature Request”
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Add Date Filter
- Click “Add Filter”
- Select “Date Range”
- Choose “Last 30 days”
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Filter by Interaction
- Add “Interaction Type” filter
- Select “External” to see only customer conversations
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Review Results
- See list of all feature request signals
- Click any signal to see full context
- Jump to exact moment in recording
Example 2: Tracking High-Severity Bugs
Scenario: Find all critical bugs mentioned by customers this quarter1
Use Natural Language Search
In Signals section, use the query builder:
Type: “Show me bugs with high severity from this quarter”
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Review AI-Generated Filters
AI creates filters for:
- Type = “Bug”
- Severity > 6
- Date Range = This quarter
- Interaction = External
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Refine if Needed
Adjust filters:
- Add company filter for specific accounts
- Filter by topic or keyword
- Sort by severity (highest first)
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Create Dataset
- Click “Save as Dataset”
- Name it “Q1 Critical Bugs”
- Export to CSV or share with engineering team
Example 3: Sentiment Analysis by Account
Scenario: Track customer sentiment trends for your top accounts1
Filter by Company
- In Signals, add “Companies” filter
- Select your key accounts (e.g., “Acme Corp”)
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View Sentiment Distribution
- Go to Clustering section
- Create or view dashboard
- Add “Sentiment Ridge Chart” card
- Filter to your selected companies
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Analyze Trends
- Review sentiment distribution (-10 to +10)
- Identify negative spikes
- Click on negative signals to see context
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Take Action
For concerning signals:
- Click signal to view source call
- Listen to the exact moment
- Add to folder “At-Risk Accounts”
- Create action item for customer success team
Example 4: Competitive Intelligence
Scenario: Track all competitor mentions across your sales calls1
Filter for Competition Signals
- Navigate to Signals
- Add filter: Type = “Competition”
- Add filter: Date = “Last 90 days”
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Use Clustering
- Go to Clustering section
- AI automatically groups similar competitive mentions
- See which competitors are mentioned most
- View trending competitor discussions
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Review Cluster Report
- Click on a competitor cluster
- Read AI-generated report with:
- Trend analysis (increasing/decreasing mentions)
- Customer quotes
- Common comparison points
- Recommended actions
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Share with Sales Team
- Export cluster to CSV
- Or generate document from cluster
- Share dashboard link with team
Clustering automatically identifies themes in your signals. Use it when you have 100+ signals to discover patterns you might miss manually.
Example 5: Creating a Bug Report Dashboard
Scenario: Build a real-time dashboard tracking bug reports1
Navigate to Clustering
Go to Signals > Clustering section
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Create Dashboard
- Click “Customize Dashboard” or create new
- Enter edit mode
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Add Visualizations
Add these cards:
- Time Series Chart: Bug volume over time
- Severity Distribution: Pie chart of severity levels
- Signal List: Filtered to bugs, sorted by severity
- Quote Cards: Recent customer bug reports
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Configure Filters
For each card, set filters:
- Type = “Bug”
- Time range = Last 30 days
- Interaction = External
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Save and Share
- Save dashboard configuration
- Copy dashboard URL
- Share with engineering and product teams
Example 6: Pushing Signals to Jira
Scenario: Automatically create Jira tickets from high-severity customer bugs1
Filter Signals
In Signals:
- Filter Type = “Bug”
- Filter Severity >= 7
- Filter Interaction = External
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Select Signals
- Review the filtered list
- Select signals to push to Jira (checkbox selection)
- Choose 1-10 signals to convert
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Push to Jira
- Click bulk action menu
- Select “Push to Integration”
- Choose “Jira”
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Configure Tickets
- Select project (e.g., “BUGS”)
- Choose issue type (“Bug”)
- Set priority (AI suggests based on severity)
- Review AI-generated titles and descriptions
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Create Tickets
- Click “Create Issues”
- Jira tickets created with:
- Customer quote in description
- Link back to BuildBetter signal
- Severity-based priority
Jira integration must be connected first in Settings > Integrations. Same process works for Linear.
Best Practices
Use natural language queries: “Show me complaints from enterprise customers” is easier than building complex filters
Save common filter views: Create saved views for frequent analyses
Leverage clustering: Let AI find patterns in large signal sets
Always check source context: Click signals to verify AI extraction is accurate
Create datasets for analysis: Save filtered signal sets with custom AI columns
Common Signal Analysis Workflows
Product Prioritization
- Filter feature requests from last quarter
- Group by company using CRM metadata
- Create dashboard showing request frequency
- Push top requests to Linear/Jira
- Share dashboard with product team
Customer Health Monitoring
- Filter signals by company: “Acme Corp”
- View sentiment trends over time
- Identify complaints and risk signals
- Add concerning signals to “At-Risk” folder
- Generate report for customer success team
Support Issue Tracking
- Import Zendesk/Intercom conversations
- Filter signals by Type = “Issue” or “Bug”
- Track resolution over time
- Identify recurring problems
- Create workflow to alert support team
Market Intelligence
- Filter Competition signals
- Use clustering to group by competitor
- Review cluster reports for trends
- Export quotes to competitive analysis doc
- Share insights with sales team