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Learn how to work with BuildBetter’s automatically extracted signals through practical examples.

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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.

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Example 1: Finding Feature Requests from Customer Calls

Scenario: You want to see all feature requests from the past month to prioritize your roadmap
1

Navigate to Signals

Click Signals or Clustering in the main navigation
2

Filter by Type

  • Click the filter icon or use the query builder
  • Select “Type” filter
  • Choose “Feature Request”
3

Add Date Filter

  • Click “Add Filter”
  • Select “Date Range”
  • Choose “Last 30 days”
4

Filter by Interaction

  • Add “Interaction Type” filter
  • Select “External” to see only customer conversations
5

Review Results

  • See list of all feature request signals
  • Click any signal to see full context
  • Jump to exact moment in recording
Use the natural language query builder: Type “Show me feature requests from customers in the last 30 days” and let AI build the filters for you.

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Example 2: Tracking High-Severity Bugs

Scenario: Find all critical bugs mentioned by customers this quarter
1

Use Natural Language Search

In Signals section, use the query builder: Type: “Show me bugs with high severity from this quarter”
2

Review AI-Generated Filters

AI creates filters for:
  • Type = “Bug”
  • Severity > 6
  • Date Range = This quarter
  • Interaction = External
3

Refine if Needed

Adjust filters:
  • Add company filter for specific accounts
  • Filter by topic or keyword
  • Sort by severity (highest first)
4

Create Saved Search

  • Click “Create Saved Search”
  • Name it “Q1 Critical Bugs”
  • Export to CSV or share with engineering team

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Example 3: Sentiment Analysis by Account

Scenario: Track customer sentiment trends for your top accounts
1

Filter by Company

  • In Signals, add “Companies” filter
  • Select your key accounts (e.g., “Acme Corp”)
2

View Sentiment Distribution

  • Go to Clustering section
  • Create or view dashboard
  • Add “Sentiment Ridge Chart” card
  • Filter to your selected companies
3

Analyze Trends

  • Review sentiment distribution (-10 to +10)
  • Identify negative spikes
  • Click on negative signals to see context
4

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

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Example 4: Competitive Intelligence

Scenario: Track all competitor mentions across your sales calls
1

Filter for Competition Signals

  • Navigate to Signals
  • Add filter: Type = “Competition”
  • Add filter: Date = “Last 90 days”
2

Use Clustering

  • Go to Clustering section
  • AI automatically groups similar competitive mentions
  • See which competitors are mentioned most
  • View trending competitor discussions
3

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
4

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.

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Example 5: Creating a Bug Report Dashboard

Scenario: Build a real-time dashboard tracking bug reports
1

Navigate to Clustering

Go to Signals > Clustering section
2

Create Dashboard

  • Click “Customize Dashboard” or create new
  • Enter edit mode
3

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
4

Configure Filters

For each card, set filters:
  • Type = “Bug”
  • Time range = Last 30 days
  • Interaction = External
5

Save and Share

  • Save dashboard configuration
  • Copy dashboard URL
  • Share with engineering and product teams

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Example 6: Pushing Signals to Jira

Scenario: Automatically create Jira tickets from high-severity customer bugs
1

Filter Signals

In Signals:
  • Filter Type = “Bug”
  • Filter Severity >= 7
  • Filter Interaction = External
2

Select Signals

  • Review the filtered list
  • Select signals to push to Jira (checkbox selection)
  • Choose 1-10 signals to convert
3

Push to Jira

  • Click bulk action menu
  • Select “Push to Integration”
  • Choose “Jira”
4

Configure Tickets

  • Select project (e.g., “BUGS”)
  • Choose issue type (“Bug”)
  • Set priority (AI suggests based on severity)
  • Review AI-generated titles and descriptions
5

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.

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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

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Common Signal Analysis Workflows

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Product Prioritization

  1. Filter feature requests from last quarter
  2. Group by company using CRM metadata
  3. Create dashboard showing request frequency
  4. Push top requests to Linear/Jira
  5. Share dashboard with product team

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Customer Health Monitoring

  1. Filter signals by company: “Acme Corp”
  2. View sentiment trends over time
  3. Identify complaints and risk signals
  4. Add concerning signals to “At-Risk” folder
  5. Generate report for customer success team

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Support Issue Tracking

  1. Import Zendesk/Intercom conversations
  2. Filter signals by Type = “Issue” or “Bug”
  3. Track resolution over time
  4. Identify recurring problems
  5. Create workflow to alert support team

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Market Intelligence

  1. Filter Competition signals
  2. Use clustering to group by competitor
  3. Review cluster reports for trends
  4. Export quotes to competitive analysis doc
  5. Share insights with sales team
Always verify signal accuracy by checking the source context before making important decisions based on AI extractions.
For more on signal features, see: