Guides
Frustration Detection
Frustration Detection = Conversation Labeling
Conversation Labeling is a powerful feature that allows you to monitor conversations in the backend by tagging or labeling specific aspects of user interactions. This capability, now built into the customization settings under the “Conversation Labeling” section, operates as a background agent within your chatbot’s architecture. It enables you to optimize the bot’s performance, take appropriate actions, and proactively manage user experience.Key Features of Conversation Labeling
Tagging and Assigning Actions:
- Label conversations to identify user sentiment, specific intents, or critical keywords.
- Use these tags to assign appropriate actions, such as initiating a human escalation or triggering automated workflows.
Monitoring and Analyzing Conversations:
- Track conversations in the background to identify patterns of frustration or other user sentiments.
- Analyze tagged conversations to uncover areas where user challenges can be addressed or workflows can be improved.
Proactive Optimization:
- Train your chatbot to handle user frustration by analyzing tagged data and implementing plans to smooth out problem areas.
- Allow for human intervention before issues escalate, increasing customer satisfaction without requiring manual review of entire messages.
Enhanced User Experience:
- Continuously refine your bot based on tagged data to ensure a seamless and effective interaction for end-users.
How to Set Up and Use Conversation Labeling
Access the Feature:
- Navigate to the Customization Settings under the “Conversation Labeling” section.
- This feature is integrated into the architecture and acts as a background agent to monitor and tag conversations.
Label Conversations:
- Define criteria for tags that identify specific conversation elements, such as user frustration, positive sentiment, or actionable intents.
- Examples of tags include:
- Frustration Detected: Triggered by repeated queries or negative sentiment.
- Escalation Needed: Signals when a human agent should intervene.
- Positive Feedback: Highlights successful user interactions.
Assign Colors and Criteria:
- Customize tags by specifying their name, color, and criteria for assignment.
- Ensure the criteria are clear and concise, as these are evaluated against the user’s recent conversation history.
Analyze and Optimize:
- Use tagged conversations to analyze common user challenges and refine chatbot workflows.
- Deep dive into data to identify where user experience improvements are needed, such as refining bot responses or automating specific actions.
Proactive Steps:
- Based on the insights gathered, train your chatbot to handle similar scenarios in the future.
- Implement a plan to address frustration points and enhance overall interaction quality.
Benefits of Conversation Labeling
- Preventive Action: Detect and address issues before they escalate.
- Streamlined Monitoring: Monitor conversations in real time without manual intervention.
- Human Escalation Awareness: Assign conversations to human agents when necessary, backed by detailed tags and analysis.
- Continuous Improvement: Leverage tagged data to train and optimize chatbot responses, ensuring a better user experience over time.