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What are Satisfaction Signals?

Satisfaction signals are indicators of how users feel about their AI interactions. Thunder automatically detects two types:
  • CSAT (Satisfaction) - Signals that the user is happy, their question was answered, or the AI was helpful
  • DSAT (Dissatisfaction) - Signals that the user is frustrated, confused, or the AI failed to help

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How Signals are Detected

Thunder analyzes conversation content to identify explicit and implicit signals: CSAT Examples:
  • “Thanks, that’s exactly what I needed!”
  • “Perfect, you’re a lifesaver”
  • “This worked great”
DSAT Examples:
  • “That’s not what I asked”
  • “You’re not understanding me”
  • “This is frustrating”
  • “Let me talk to a human”
Detection uses AI models trained on conversational patterns - no explicit feedback buttons required.

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Signal Metrics

Query signals to understand their frequency:
MetricDescription
sessionsConversations where this signal was detected
usersUnique users who expressed this signal
messagesNumber of times this signal appeared

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Querying Signals

Get all satisfaction signals with their frequency: 
curl "https://api.usethunder.com/v1/query?\
groupBy=CSAT_DSAT&\
instanceId=your-instance-id&\
dateRange.start=2024-01-01T00:00:00Z&\
dateRange.end=2024-01-31T23:59:59Z&\
timeGranularity=ALL&\
fields=sessions,users,messages&\
sort.field=sessions&\
sort.order=DESC" \
  -H "x-api-key: YOUR_API_KEY"
Response: 
{
  "elements": [
    {
      "dimensions": {
        "csatDsatSignal": {
          "id": "signal-uuid",
          "name": "Expressed gratitude",
          "signalType": "SAT",
          "description": "User thanked the assistant"
        }
      },
      "fields": {
        "sessions": 523,
        "users": 412,
        "messages": 687
      }
    },
    {
      "dimensions": {
        "csatDsatSignal": {
          "id": "signal-uuid-2",
          "name": "Expressed confusion",
          "signalType": "DSAT",
          "description": "User indicated they didn't understand"
        }
      },
      "fields": {
        "sessions": 89,
        "users": 76,
        "messages": 124
      }
    }
  ]
}

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Session-Level Metrics

When querying sessions, you get aggregate signal counts: 
curl "https://api.usethunder.com/v1/query?\
groupBy=SESSION&\
instanceId=your-instance-id&\
dateRange.start=2024-01-01T00:00:00Z&\
dateRange.end=2024-01-31T23:59:59Z&\
timeGranularity=ALL&\
fields=messages,sat,dsat,netSat" \
  -H "x-api-key: YOUR_API_KEY"
FieldDescription
satCount of CSAT signals in the session
dsatCount of DSAT signals in the session
netSatNet satisfaction score: (sat - dsat) / messages * 100

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Finding Problem Sessions

Filter for sessions with specific signals: 
curl "https://api.usethunder.com/v1/query?\
groupBy=SESSION&\
instanceId=your-instance-id&\
signalIds=dsat-signal-uuid&\
dateRange.start=2024-01-01T00:00:00Z&\
dateRange.end=2024-01-31T23:59:59Z&\
timeGranularity=ALL&\
fields=messages,sat,dsat&\
dimensionFields=title,summary" \
  -H "x-api-key: YOUR_API_KEY"
This returns only sessions where that specific DSAT signal was detected.

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Use Cases

Track Overall Satisfaction

Monitor the ratio of CSAT to DSAT signals over time to gauge AI quality.

Identify Failure Patterns

Examine sessions with high DSAT to understand common failure modes.

Measure Improvements

Track signal trends before and after AI updates to measure impact.

Prioritize Fixes

Focus on the most common DSAT signals first for maximum impact.
See Querying Metrics for more query patterns and the full field reference.