Signal Types Overview
BuildBetter can extract various signals:
- Feature requests
- Bug reports
- Customer sentiment
- Product feedback
- Support needs
- Integration requests
Automatic Signals
AI-powered detection
Manual Tagging
User-defined signals
Example Configurations
Signal Rules
# Feature Request Detection
signal_rule:
type: "feature_request"
triggers:
- "need feature"
- "would be great if"
- "missing functionality"
context:
pre_words: 3
post_words: 5
priority:
default: "medium"
keywords:
urgent: "high"
nice: "low"
Sentiment Analysis
{
"sentiment_config": {
"granularity": "sentence",
"aspects": [
"product",
"support",
"pricing"
],
"scale": {
"range": [-1, 1],
"neutral_threshold": 0.1
}
}
}
Real-World Examples
Customer Feedback
source: "support_call"
extract:
- product_issues
- feature_requests
- satisfaction_level
context:
customer_segment: "enterprise"
product_version: "2.4.0"
Sales Call
source: "sales_meeting"
extract:
- pain_points
- competitor_mentions
- pricing_feedback
priority: "high"
notify: ["sales_team", "product"]
Signal Processing
# Signal extraction from text
config = {
"nlp": {
"models": ["sentiment", "entity"],
"language": "en",
"confidence": 0.8
},
"output": {
"format": "structured",
"include_context": true
}
}
# Signal extraction from audio
config = {
"audio": {
"speaker_detection": true,
"emotion_analysis": true,
"keyword_spotting": true
},
"timestamps": true
}
Advanced Usage
Custom Signal Types
# Define custom signal
custom_signal:
name: "integration_request"
patterns:
- "integrate with"
- "connection to"
- "sync with"
metadata:
- platform
- requirements
- priority
actions:
- create_ticket
- notify_team
Signal Correlation
Ensure signal correlation rules are properly validated to avoid false patterns.
{
"correlation_rules": {
"time_window": "7d",
"min_occurrences": 3,
"confidence": 0.85,
"grouping": [
"customer_segment",
"product_area"
]
}
}
Integration Examples
CRM Integration
# Salesforce signal sync
integration:
platform: "salesforce"
mapping:
feature_request:
object: "Product_Request__c"
fields:
description: "signal.content"
priority: "signal.priority"
source: "signal.meeting_id"
Project Management
# Jira ticket creation
automation:
trigger: "new_signal"
conditions:
type: "bug_report"
priority: "high"
action:
create_issue:
project: "PROD"
type: "Bug"
labels: ["customer-reported"]
Analysis Templates
Trend Analysis
# Signal trend detection
analysis:
timeframe: "30d"
grouping:
- signal_type
- product_area
metrics:
- volume
- sentiment
- priority
visualization:
type: "trend_chart"
breakdown: "weekly"
Impact Assessment
{
"impact_scoring": {
"factors": {
"customer_tier": {
"enterprise": 3,
"business": 2,
"starter": 1
},
"frequency": {
"weight": 0.4,
"scale": [1, 5]
},
"sentiment": {
"weight": 0.3,
"range": [-1, 1]
}
}
}
}
Best Practices
Signal Quality
- Validate patterns
- Check context
- Verify sources
Processing
- Regular updates
- Monitor accuracy
- Refine rules
Regular review of signal patterns improves extraction accuracy.
Troubleshooting
# Troubleshooting steps
checks:
- signal_rules
- source_quality
- processing_logs
- integration_status
# Enhancement steps
improvements:
- pattern_refinement
- context_expansion
- threshold_adjustment
- validation_rules
Use test data to validate signal extraction rules before deployment.
These examples can be adapted to match your specific signal extraction needs.