detect-conversation-type

Conversation Type Detection and Classification

Overview

This skill detects and classifies the type of conversation occurring in ServiceNow engagement channels (Virtual Agent, chat, email, portal) to enable intelligent routing and handling:

Inquiry: Information-seeking questions about services, policies, or status
Request: Actionable service requests or catalog orders
Complaint: Expressions of dissatisfaction requiring service recovery
Feedback: Constructive input about services, processes, or experiences
Escalation: Urgent issues requiring immediate attention or management involvement
Troubleshooting: Technical problem-solving requiring diagnostic steps
Follow-up: Continuation of a previous conversation or existing ticket

When to use: When building Virtual Agent topic routing, when enriching interaction records with classification metadata, when automating conversation handoff decisions, or when analyzing conversation patterns for service improvement.

Prerequisites

Roles: admin, sn_customerservice_manager, itil, or virtual_agent_admin
Plugins: com.glide.cs.chatbot (Virtual Agent), com.glide.interaction (Agent Workspace Interaction)
Access: Read access to sys_cs_conversation, sys_cs_message, interaction tables
Knowledge: Virtual Agent topic design, conversation flow concepts, sentiment analysis basics
Related Skills: csm/sentiment-analysis for sentiment scoring, csm/chat-recommendation for response suggestions

Procedure

Step 1: Retrieve Conversation Messages

Fetch the conversation history for classification.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: sys_cs_message
  query: conversation=<conversation_sys_id>^ORDERBYsys_created_on
  fields: sys_id,body,direction,sys_created_on,typed_by,message_type
  limit: 50

REST Approach:

GET /api/now/table/sys_cs_message
  ?sysparm_query=conversation=<conversation_sys_id>^ORDERBYsys_created_on
  &sysparm_fields=sys_id,body,direction,sys_created_on,typed_by,message_type
  &sysparm_limit=50

For interaction-based conversations:

Tool: SN-Query-Table
Parameters:
  table_name: interaction
  query: sys_id=<interaction_sys_id>
  fields: sys_id,short_description,type,channel,state,opened_for,assigned_to,opened_at,work_notes
  limit: 1

Step 2: Analyze Initial User Message

The first user message is the strongest signal for conversation type. Extract:

Intent Signals:

Signal Type	Examples	Classification
Question words	"How do I...", "What is...", "Where can I..."	Inquiry
Action verbs	"I need...", "Please set up...", "Can you create..."	Request
Negative sentiment	"This is unacceptable...", "I'm frustrated..."	Complaint
Suggestion language	"It would be nice if...", "Have you considered..."	Feedback
Urgency markers	"URGENT", "This is critical", "Need help NOW"	Escalation
Problem description	"It's not working", "I'm getting an error..."	Troubleshooting
Reference to prior	"Following up on INC...", "As discussed..."	Follow-up

Step 3: Apply Classification Rules

Score the conversation against each type using a weighted analysis:

=== CONVERSATION CLASSIFICATION ===
Conversation ID: CS0045678
Channel: Virtual Agent (Service Portal)
User: Jane Smith (Engineering)
Initial Message: "I've been waiting 5 days for my laptop and no one
  has contacted me. This is the third time I've had to follow up.
  I need this resolved today or I need to speak with a manager."

Classification Scores:
| Type            | Score | Signals Detected |
|-----------------|-------|------------------|
| Complaint       | 85%   | Negative sentiment, dissatisfaction, wait time mention |
| Escalation      | 75%   | "speak with a manager", urgency ("today") |
| Follow-up       | 60%   | "third time", reference to prior interaction |
| Request         | 30%   | "need this resolved" (implicit service need) |
| Inquiry         | 10%   | No information-seeking language |
| Feedback        | 5%    | No constructive suggestion |
| Troubleshooting | 5%    | No technical problem described |

PRIMARY: Complaint (85%)
SECONDARY: Escalation (75%)
COMPOSITE: Complaint with Escalation Request

URGENCY: High
SENTIMENT: Negative (frustrated)
PRIORITY ACTION: Route to human agent with escalation flag

Step 4: Detect Multi-Intent Conversations

Some conversations contain multiple intents. Identify all:

MCP Approach:

Tool: SN-Query-Table
Parameters:
  query: 123TEXTQUERY321=laptop delivery delay complaint escalation
  table_name: sys_cb_topic
  limit: 10

Analyze message-by-message for intent shifts:

Message 1: "I need to reset my password" -> Request
Message 2: "Also, the VPN has been slow all week" -> Troubleshooting
Message 3: "And when will the new laptops be available?" -> Inquiry

Classification: Multi-intent conversation
Primary: Request (password reset - most actionable)
Secondary: Troubleshooting (VPN performance)
Tertiary: Inquiry (laptop availability)

Step 5: Map Classification to Routing Rules

Determine the appropriate handling workflow based on classification.

Routing Matrix:

Classification	Channel	Priority	Route To
Inquiry	Virtual Agent	Low	Knowledge search -> FAQ topic
Request	Virtual Agent	Medium	Catalog item topic -> Fulfillment
Complaint	Live Agent	High	CSM queue -> Service Recovery
Feedback	Async	Low	Feedback collection -> Survey
Escalation	Live Agent	Critical	Manager queue -> Priority handling
Troubleshooting	Virtual Agent	Medium	Diagnostic topic -> IT Support
Follow-up	Live Agent	Medium	Original assignee -> Context resume

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: sys_cb_topic
  query: nameLIKEcomplaint^active=true
  fields: sys_id,name,description,queue,priority,active
  limit: 5

Step 6: Enrich the Interaction Record

Update the interaction or conversation record with classification metadata.

MCP Approach:

Tool: SN-Update-Record
Parameters:
  table_name: interaction
  sys_id: <interaction_sys_id>
  data:
    u_conversation_type: "complaint"
    u_secondary_type: "escalation"
    u_urgency: "high"
    u_sentiment: "negative"
    u_classification_confidence: "85"
    u_routing_recommendation: "csm_escalation_queue"

REST Approach:

PATCH /api/now/table/interaction/<interaction_sys_id>
Body: {
  "u_conversation_type": "complaint",
  "u_secondary_type": "escalation",
  "u_urgency": "high",
  "u_sentiment": "negative"
}

Step 7: Create Downstream Records Based on Type

Automatically create appropriate records based on classification.

For Complaints -- Create CSM Case:

MCP Approach:

Tool: SN-Create-Record
Parameters:
  table_name: sn_customerservice_case
  data:
    short_description: "Complaint: Laptop delivery delay - 3rd follow-up"
    description: "<conversation summary>"
    priority: 2
    contact: "<user_sys_id>"
    category: "complaint"
    u_complaint_type: "service_delivery"
    u_source_conversation: "<conversation_sys_id>"

For Requests -- Create Catalog Request:

Tool: SN-Create-Record
Parameters:
  table_name: sc_request
  data:
    requested_for: "<user_sys_id>"
    short_description: "Password reset request via chat"
    description: "<conversation context>"
    u_source_conversation: "<conversation_sys_id>"

Step 8: Handle Escalation Routing

When an escalation is detected, trigger immediate routing.

MCP Approach:

Tool: SN-Update-Record
Parameters:
  table_name: interaction
  sys_id: <interaction_sys_id>
  data:
    state: "transferred_to_agent"
    assignment_group: "<escalation_queue_sys_id>"
    u_escalation_reason: "Customer requested manager involvement"
    u_escalation_priority: "high"
    work_notes: "AI Classification: Complaint with escalation request. Customer has followed up 3 times regarding laptop delivery delay. Sentiment: Negative/Frustrated. Routing to escalation queue."

Step 9: Track Classification Accuracy

Monitor and validate classification decisions over time.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: interaction
  query: u_conversation_typeISNOTEMPTY^sys_created_on>javascript:gs.daysAgo(30)
  fields: u_conversation_type,u_classification_confidence,state,u_agent_override_type
  limit: 500

Calculate accuracy metrics:

=== CLASSIFICATION ACCURACY (Last 30 Days) ===

| Type            | Classified | Agent Override | Accuracy |
|-----------------|-----------|---------------|----------|
| Inquiry         | 245       | 12            | 95.1%    |
| Request         | 189       | 8             | 95.8%    |
| Complaint       | 67        | 5             | 92.5%    |
| Troubleshooting | 156       | 11            | 92.9%    |
| Escalation      | 34        | 3             | 91.2%    |
| Feedback        | 28        | 4             | 85.7%    |
| Follow-up       | 42        | 6             | 85.7%    |

Overall Accuracy: 93.4%
Most Common Misclassification: Feedback classified as Inquiry

Step 10: Refine Classification Rules

Use override data to improve classification accuracy.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: interaction
  query: u_agent_override_typeISNOTEMPTY^u_conversation_type!=u_agent_override_type
  fields: u_conversation_type,u_agent_override_type,short_description
  limit: 50

Analyze misclassifications to identify patterns and update routing rules.

Tool Usage

Tool	Purpose	When to Use
SN-Query-Table	Fetch conversations, messages, topics, metrics	Primary data retrieval
SN-Get-Record	Retrieve specific interaction details	Single conversation analysis
SN-Create-Record	Create downstream records (cases, incidents)	Acting on classification
SN-Update-Record	Enrich interaction with classification metadata	Recording classification results
SN-Natural-Language-Search	Find matching topics or similar conversations	Topic routing and pattern matching

Best Practices

Classify on first message -- do not wait for multiple exchanges to make an initial classification
Support reclassification -- conversations can shift type mid-stream; update classification dynamically
Use confidence thresholds -- below 70% confidence, route to human agent for classification
Combine intent and sentiment -- a request with negative sentiment may actually be a complaint
Handle multi-intent gracefully -- address the most urgent intent first, then secondary intents
Preserve conversation context -- pass full classification metadata to receiving agent or workflow
Track agent overrides -- use override data as training signal for improving classification rules
Consider channel context -- phone calls have different patterns than chat or email
Detect language and tone shifts -- escalation often manifests as a shift from neutral to negative
Respect privacy -- do not log or classify sensitive personal information in metadata fields

Troubleshooting

Issue	Cause	Resolution
Messages not retrieved	Wrong conversation table or ID format	Check `sys_cs_conversation` vs `interaction` table
Classification always "Inquiry"	Default fallback too aggressive	Lower inquiry threshold, add more signal patterns
Escalation not detected	Urgency language not in signal list	Add domain-specific urgency phrases to detection rules
Multi-intent not handled	Only first intent extracted	Implement per-message analysis with intent accumulation
Routing to wrong queue	Topic mapping outdated	Update `sys_cb_topic` routing configuration
Low confidence scores	Ambiguous or very short messages	Request clarification from user before classifying

Examples

Example 1: Virtual Agent Inquiry Classification

Input: User message: "What are the company holidays for 2026?"

Classification: Inquiry (95% confidence). Route to Knowledge search topic. Suggested KB article: "2026 Company Holiday Calendar."

Example 2: Complaint with Escalation Detection

Input: User message: "I submitted a request two weeks ago and nothing has happened. This is completely unacceptable. I need to talk to someone who can actually help."

Classification: Complaint (88%) + Escalation (80%). Route to live agent in escalation queue. Flag as high priority. Auto-search for existing open requests by this user.

Example 3: Multi-Intent Chat Session

Input: User sends 3 messages: password reset request, question about VPN policy, and feedback about the portal design.

Classification: Multi-intent detected. Primary: Request (password reset, actionable). Secondary: Inquiry (VPN policy). Tertiary: Feedback (portal). Route password reset to IT topic, queue VPN question for knowledge search, log feedback for UX team review.

Related Skills

csm/sentiment-analysis - Deep sentiment analysis for conversations
csm/chat-recommendation - Suggested responses for agents
hrsd/chat-reply-recommendation - HR-specific chat response suggestions
genai/playbook-recommendations - Match conversation to handling playbooks
itsm/incident-triage - Incident classification and routing

About this skill

Overview

Prerequisites

Procedure

Step 1: Retrieve Conversation Messages

Step 2: Analyze Initial User Message

Step 3: Apply Classification Rules

Step 4: Detect Multi-Intent Conversations

Step 5: Map Classification to Routing Rules

Step 6: Enrich the Interaction Record

Step 7: Create Downstream Records Based on Type

Step 8: Handle Escalation Routing

Step 9: Track Classification Accuracy

Step 10: Refine Classification Rules

Tool Usage

Best Practices

Troubleshooting

Examples

Example 1: Virtual Agent Inquiry Classification

Example 2: Complaint with Escalation Detection

Example 3: Multi-Intent Chat Session

Related Skills