Extract Structured Data from Conversations - Dasha BlackBox Documentation

Configure analysis forms with labeled fields, and AI populates them from each transcript. Results arrive via webhook or API, ready for your CRM, analytics pipeline, or alerting workflows. What you’ll learn: Label configuration, extraction types, CRM integration, and analytics patterns.

Why Use Post-Call Analysis?

Eliminate Manual Review

Instead of listening to call recordings or reading transcripts, Dasha BlackBox automatically delivers structured summaries after each call.

Enable CRM Integration

The system flows extracted data directly into your CRM systems, automatically updating customer records with conversation outcomes and sentiment.

Business Intelligence

Aggregate analysis data across thousands of calls to identify trends, measure satisfaction, track resolution rates, and optimize agent performance.

Compliance and Quality Assurance

Automatically verify calls cover required topics, disclosures are made, or specific information is collected.

Trigger Workflows

Use extracted data to trigger automated workflows: escalate negative sentiment calls, schedule follow-ups, update ticket systems, or alert managers.

How to Configure Post-Call Analysis

Step 1: Create Analysis Form

Open your agent’s configuration
Navigate to the Features tab
Scroll to Post-Call Analysis section
Click Add Analysis Form

You can create multiple analysis forms per agent, each analyzing different aspects of conversations.

Step 2: Configure Analysis Form

Analysis Name

Identifier for this analysis
Example: “Customer Satisfaction Analysis”, “Lead Qualification Data”, “Support Ticket Info”

Enable/Disable Toggle

Control whether this analysis runs
Useful for temporarily disabling specific analyses without deleting configuration

Step 3: Define Data Labels

Click Add Label to define each label the AI should determine from the conversation. For each label, configure: Label Name

Identifier (alphanumeric, hyphens, underscores only)
Used in API responses and webhooks
Example: customer_sentiment, issue_resolved, product_name

Label Description

What the AI should determine from the conversation
Be specific and clear
Example: “The customer’s overall sentiment about the interaction: positive, neutral, or negative”

Data Type

String: Free-form text (names, descriptions, notes)
Number: Numeric values (ratings, counts, amounts)
Boolean: True/false values (yes/no questions)
Enum: Predefined set of options (dropdown-style choices)

Enum Values (if Data Type is Enum)

List allowed values
Example for sentiment: positive, neutral, negative
Example for product: ProductA, ProductB, ProductC

Required Flag

Whether this field must have a value
If required and not found in transcript, analysis may mark field as unable to determine

Configuration Examples

Example: Customer Support Analysis

{
  "analysisName": "Support Call Analysis",
  "enabled": true,
  "labels": [
    {
      "name": "customer_sentiment",
      "description": "Customer's overall sentiment: positive, neutral, or negative",
      "type": "enum",
      "values": ["positive", "neutral", "negative"],
      "required": true
    },
    {
      "name": "issue_resolved",
      "description": "Was the customer's issue fully resolved during the call?",
      "type": "boolean",
      "required": true
    },
    {
      "name": "issue_category",
      "description": "Category of the issue discussed",
      "type": "enum",
      "values": ["billing", "technical", "account", "shipping", "other"],
      "required": true
    },
    {
      "name": "follow_up_needed",
      "description": "Does this customer need follow-up contact?",
      "type": "boolean",
      "required": false
    },
    {
      "name": "resolution_notes",
      "description": "Brief summary of how the issue was resolved",
      "type": "string",
      "required": false
    }
  ]
}

Example: Sales Lead Qualification

{
  "analysisName": "Lead Qualification",
  "enabled": true,
  "labels": [
    {
      "name": "interest_level",
      "description": "Prospect's level of interest in the product",
      "type": "enum",
      "values": ["high", "medium", "low", "not_interested"],
      "required": true
    },
    {
      "name": "budget_discussed",
      "description": "Did the prospect discuss budget or pricing?",
      "type": "boolean",
      "required": true
    },
    {
      "name": "timeline",
      "description": "When does the prospect want to make a decision?",
      "type": "enum",
      "values": ["immediate", "this_month", "this_quarter", "long_term", "unclear"],
      "required": true
    },
    {
      "name": "decision_maker",
      "description": "Is this prospect the decision maker?",
      "type": "boolean",
      "required": true
    },
    {
      "name": "next_step",
      "description": "What is the agreed upon next step?",
      "type": "string",
      "required": true
    },
    {
      "name": "competitors_mentioned",
      "description": "Names of competitors the prospect mentioned, if any",
      "type": "string",
      "required": false
    }
  ]
}

Example: Appointment Scheduling

{
  "analysisName": "Appointment Details",
  "enabled": true,
  "labels": [
    {
      "name": "appointment_confirmed",
      "description": "Did the caller confirm their appointment?",
      "type": "boolean",
      "required": true
    },
    {
      "name": "appointment_date",
      "description": "The confirmed appointment date and time",
      "type": "string",
      "required": true
    },
    {
      "name": "reminder_preferred",
      "description": "Does the caller want a reminder call or SMS?",
      "type": "boolean",
      "required": false
    },
    {
      "name": "special_requests",
      "description": "Any special requests or accommodations needed",
      "type": "string",
      "required": false
    },
    {
      "name": "caller_mood",
      "description": "Overall mood/demeanor of the caller",
      "type": "enum",
      "values": ["friendly", "neutral", "frustrated", "rushed"],
      "required": false
    }
  ]
}

Example: Survey Response Collection

{
  "analysisName": "Survey Responses",
  "enabled": true,
  "labels": [
    {
      "name": "nps_score",
      "description": "Net Promoter Score: How likely is the customer to recommend us? (0-10)",
      "type": "number",
      "required": true
    },
    {
      "name": "service_rating",
      "description": "Customer rating of service quality (1-5)",
      "type": "number",
      "required": true
    },
    {
      "name": "primary_feedback",
      "description": "Main feedback or suggestion the customer provided",
      "type": "string",
      "required": false
    },
    {
      "name": "willing_to_follow_up",
      "description": "Is customer willing to be contacted for follow-up questions?",
      "type": "boolean",
      "required": false
    }
  ]
}

Accessing Analysis Results

Via Webhooks

Analysis results are included in result webhook payloads:

{
  "call_id": "call_abc123",
  "transcript": "...",
  "analysis_results": {
    "Support Call Analysis": {
      "customer_sentiment": "positive",
      "issue_resolved": true,
      "issue_category": "billing",
      "follow_up_needed": false,
      "resolution_notes": "Updated payment method, issue resolved"
    }
  }
}

Configure result webhooks to receive analysis data automatically.

Via API

Retrieve call results including analysis data:

GET /v1/calls/{call_id}/result

Response includes analysis_results with all configured analyses.

Via Dashboard

View analysis results in the call history:

Navigate to Calls page
Select completed call
View “Analysis Results” section

Common Use Cases

Customer Support

Labels: Sentiment, resolution status, issue category, follow-up needs Use: Update support tickets, measure satisfaction, identify escalation needs

Sales Qualification

Labels: Interest level, budget discussed, decision maker status, timeline Use: Score leads, prioritize follow-ups, update CRM automatically

Appointment Scheduling

Labels: Confirmation status, appointment details, reminders needed Use: Update calendar systems, send confirmations, schedule reminders

Survey Collection

Labels: Ratings, scores, feedback, willingness to follow up Use: Aggregate survey data, identify promoters/detractors, analyze trends

Compliance Verification

Labels: Required disclosures made, consent obtained, information provided Use: Quality assurance, regulatory compliance, agent performance tracking

Product Feedback

Labels: Products mentioned, feature requests, pain points, competitive intel Use: Product roadmap, competitive analysis, customer insights

Technical Considerations

Analysis Accuracy

Post-call analysis is typically 85-95% accurate:

Clear conversations with specific information are easiest to analyze
Vague or ambiguous conversations may result in “unable to determine” for some labels
Enum labels are generally more accurate than free-form string labels

Processing Time

Analysis occurs after call completion:

Typically processes within 5-30 seconds after call ends
Complex analyses with many fields may take longer
Results delivered via webhook once processing completes

Transcript Quality

Analysis accuracy depends on transcript quality:

Clear audio improves extraction accuracy
Background noise or crosstalk can affect results
Proper speech recognition configuration helps

Multiple Analyses

You can configure multiple analysis forms:

Each runs independently
Results are namespaced by analysis name
Use multiple forms to organize different data extraction needs

Data Privacy

Be mindful of data privacy:

Only analyze information you have legitimate need for
Consider applicable data privacy regulations when labeling personal data
Analysis results flow through webhooks—ensure secure transmission

Measuring Effectiveness

Key Metrics

Label completion rate - Percentage of calls with all required labels populated
Accuracy rate - Manual verification of label correctness
Unable to determine rate - How often labels can’t be determined from conversation
Downstream integration success - Do CRM/analytics systems receive correct data?

Quality Assurance Process

Sample Review - Manually verify 10-20 call analyses weekly
Compare to Transcripts - Ensure labeled data matches conversation content
Track “Unable to Determine” - Identify patterns in analysis failures
Refine Descriptions - Improve label descriptions based on accuracy results
Adjust Requirements - Make fewer labels required if analysis often fails

Troubleshooting

Problem: Field Consistently Shows “Unable to Determine”

Solution: Description may be too vague or information genuinely not in conversations. Review transcripts to verify information is discussed. Improve label description with examples. Consider making field optional if not always applicable.

Problem: Wrong Enum Value Assigned

Solution: Enum values may be ambiguous or similar. Use more distinct value names. Improve label description to clarify when each value should be used. Add examples: “positive (customer expresses satisfaction)”.

Problem: String Fields Contain Too Much Text

Solution: Refine description to request “brief summary” or “one sentence”. Be specific about length expectations. Consider using enum instead if values are predictable.

Problem: Numbers Determined Incorrectly

Solution: Clarify in description what format numbers should be in. Example: “Rating from 1-5” vs. “Percentage from 0-100”. Verify transcripts actually contain numeric values.

Problem: Analysis Results Not in Webhook

Solution: Verify result webhook is configured. Check analysis is enabled. Ensure call completed successfully (analysis only runs for completed calls). Review webhook logs for delivery issues.

Integration Examples

CRM Integration

Use webhook to update CRM when calls complete:

// Webhook receiver
app.post('/call-result-webhook', (req, res) => {
  const { call_id, analysis_results } = req.body;

  const analysis = analysis_results["Support Call Analysis"];

  // Update CRM
  crm.updateTicket({
    callId: call_id,
    sentiment: analysis.customer_sentiment,
    resolved: analysis.issue_resolved,
    category: analysis.issue_category,
    followUpNeeded: analysis.follow_up_needed
  });

  res.sendStatus(200);
});

Escalation Workflow

Trigger manager alerts for negative calls:

if (analysis.customer_sentiment === "negative" && !analysis.issue_resolved) {
  notificationService.alertManager({
    priority: "high",
    subject: "Negative call requiring follow-up",
    callId: call_id
  });
}

Analytics Pipeline

Stream analysis data to analytics warehouse:

analyticsDB.insert({
  timestamp: new Date(),
  callId: call_id,
  sentiment: analysis.customer_sentiment,
  resolved: analysis.issue_resolved,
  category: analysis.issue_category
});

Next steps

Webhooks

Receive analysis results automatically

Tools & Functions

Extend agent capabilities during calls

Knowledge Bases

Improve response quality

Call History

Track conversation metrics

Documentation Index

​Why Use Post-Call Analysis?

​Eliminate Manual Review

​Enable CRM Integration

​Business Intelligence

​Compliance and Quality Assurance

​Trigger Workflows

​How to Configure Post-Call Analysis

​Step 1: Create Analysis Form

​Step 2: Configure Analysis Form

​Step 3: Define Data Labels

​Configuration Examples

​Accessing Analysis Results

​Via Dashboard

​Common Use Cases

​Customer Support

​Sales Qualification

​Appointment Scheduling

​Survey Collection

​Compliance Verification

​Product Feedback

​Technical Considerations

​Analysis Accuracy

​Processing Time

​Transcript Quality

​Multiple Analyses

​Data Privacy

​Measuring Effectiveness

​Key Metrics

​Quality Assurance Process

​Troubleshooting

​Problem: Field Consistently Shows “Unable to Determine”

​Problem: Wrong Enum Value Assigned

​Problem: String Fields Contain Too Much Text

​Problem: Numbers Determined Incorrectly

​Problem: Analysis Results Not in Webhook

​Integration Examples

​Next steps