Why Topic Discovery?
| Challenge | How Topic Discovery Helps |
|---|---|
| Pattern recognition | Identifies recurring themes across thousands of daily interactions. |
| Metric correlation | Connects topics to resolution rates, sentiment scores, and handle times. |
| Reactive issue management | Spots emerging issues before they escalate. |
| Resource allocation | Pinpoints problem areas for targeted coaching resources. |
| Sentiment analysis | Reveals customer sentiment patterns across conversation categories. |
| Resolution tracking | Monitors success rates across topic categories. |
Key Capabilities
- Trend identification: Assess high-volume topics and their performance metrics.
- Coaching focus: Pinpoint topics with poor sentiment or low resolution rates.
- Performance monitoring: Track topic-specific AHT and resolution rates.
- Proactive management: Discover emerging issues through AI-generated topics before they become widespread problems.
Topic Hierarchy
Topic Discovery uses a three-level structure:| Level | Description | Example |
|---|---|---|
| L1 | Top-level theme | Billing Issues |
| L2 | Subtopic under L1 | Payment Problems |
| L3 | Granular subtopic under L2 | Credit Card Declined |
Filters
The Top Filter Bar is the central control for customizing the Topic Discovery view. Every adjustment instantly updates the visualization.
| Filter | What it does | How to use it | When to use it |
|---|---|---|---|
| Search Topic Names | Locate topics in the visualization. | Start typing a keyword; matching topics highlight instantly. | When looking for a specific issue (for example, “Payment Failure”). |
| Configured / Generated Intents | Switch between taxonomy-based topics and AI-discovered themes. | Select Configured Intents for your taxonomy; Generated Intents for blind spots. | Use Generated Intents to find new themes outside your taxonomy. |
| Time Range | Adjust the analysis period. | Options: 7 days (default), 28 days, 30 days, 90 days, custom. | Compare weekly vs. monthly trends to spot recurring issues. |
| Sentiment Filter | Focus on conversations by sentiment. | Adjust the score range slider (0–10); default is full range. | Narrow to low-sentiment conversations for quality monitoring. |
| Resolution Filter | Filter by resolution success rates. | Adjust the score range slider (0–100); default is full range. | Zero in on unresolved or low-resolution conversations. |
Advanced Topic Filters
Select Filters to access additional options:| Filter | Options |
|---|---|
| Channel | Voice, Chat, or both. |
| Language | Multi-select with search; selectively remove or clear all. |
| Queue | Narrow by queues; agent filter updates automatically. |
| Agent | Search and select agents (queue-dependent). |
| AHT | Set min–max values or acceptable variance. |
Bubble Visualization Canvas
Topics display as interactive bubbles with meaningful visual encoding for an at-a-glance view of conversation volumes, performance metrics, and topic relationships.
Bubble Attributes
| Attribute | Meaning |
|---|---|
| Size | Conversation volume — larger bubbles = more conversations. |
| Color | Topic performance based on the selected metric (sentiment or resolution). |
| Position | Groups related topics together. |
| Labels | L1 topics: labels outside. L2 and L3: labels inside. |
Sentiment Color Coding
| Color | Sentiment |
|---|---|
| Green | Positive |
| Grey | Neutral |
| Red | Poor |
Resolution Color Coding
| Color | Resolution Rate |
|---|---|
| Red | 0–50% (Low) |
| Grey | 50–70% (Moderate) |
| Green | 70–100% (High) |
Hover Tooltips
Hovering over a bubble shows key metrics without leaving the main view:| Field | Description |
|---|---|
| Topic Name | Full name if truncated in the visualization. |
| Conversation Count | Total interactions for that topic. |
| Total Conversations | Total with trend indicators (spike/dip %). |
| Average Sentiment Score | Overall sentiment with trend analysis. |
| Sentiment Breakdown | Distribution across Positive/Neutral/Negative. |
Configured Intents vs. Generated Intents
Configured Intents
Displays topics based on your organization’s pre-defined taxonomy. Use when:- Monitoring known business categories and established conversation types.
- Tracking performance against your documented taxonomy.
- Comparing results to historical data using consistent categorization.
Generated Intents
Uses AI to discover conversation themes your configured taxonomy may not capture. Use when:- Discovering blind spots in your taxonomy.
- Identifying emerging customer issues.
- Exploring unexpected conversation patterns.
- Validating and expanding your taxonomy structure.
- Uncovering new product issues or customer needs.
Topic Detail Pane
Select View Details from any bubble tooltip to open the detail pane for comprehensive analytics on a specific topic.
Overview Tab
| Metric | Description |
|---|---|
| Total Conversations % | Topic’s share of all conversations. |
| Average Sentiment Score | Overall sentiment with trend analysis. |
| Sentiment Breakdown | Distribution across emotional categories. |
| Average Handle Time | Performance metric with trends. |
| Average Resolution % | Success rate analysis. |
| Top Keywords | Most frequent terms in topic conversations. |
| Emotion Detection | Top 6 emotions identified in conversations. |
Conversations Tab
Shows individual interactions for the selected topic.
| Column | Information | Purpose |
|---|---|---|
| Agent Name | Name | Identify the conversation handler. |
| Channel | Voice or Chat | Understand the interaction method. |
| Queue | Service category | Context for conversation type. |
| Actions | Conversation details | Access full interaction details. |
- Sorting: Most recent conversations first.
- Pagination: 10 conversations per page.
- All Conversations: Opens Conversation Mining - Interactions with topic filters pre-applied.
Full Conversation View
Select an interaction to open the full conversation pane.
| Section | Content |
|---|---|
| Complete Thread | Full customer-agent interaction. |
| Topic Highlighting | Visual indicators for detected topics. |
| Metadata | Channel, duration, resolution status, sentiment scores. |
| Timeline View | Chronological flow. |
| Context | Queue, agent, and channel details. |
| Tool | Description |
|---|---|
| Sentiment Score | Sentiment throughout the conversation. |
| Empathy Score | Empathy throughout the conversation. |
| Crutch Word Score | Crutch word usage throughout the conversation. |
Use Case: Identifying Agent Coaching Opportunities
Scenario: A QA Manager notices increasing customer complaints and needs to find specific areas for improvement.Step-by-Step
-
Initial analysis
- Open Topic Discovery with the default 7-day view.
- Scan L1 topics for large bubbles with negative sentiment.
- Identify “Technical Support” as a high-volume, low-sentiment topic.
-
Drill-down investigation
- Select “Technical Support” to reveal L2 topics.
- Notice “Software Installation” has poor resolution rates.
- Select “Software Installation” to see L3 subtopics.
- Identify “Driver Installation” as the primary problem area.
-
Detailed analysis
- Open the “Driver Installation” detail pane.
- Metrics: 150 conversations, 45% resolution rate, average sentiment: 2.
- Top keywords:
error,crash,incompatible,frustrated. - Top emotions: Anger (40%), Frustration (35%), Confusion (25%).
-
Conversation review
- Select View Conversations to review individual interactions.
- Review 3–4 representative conversations to identify failure patterns.
- Identify knowledge gaps in driver troubleshooting procedures.
-
Action planning
- Develop a targeted training module on driver installation.
- Create job aids for common driver compatibility issues.
- Schedule coaching sessions with agents handling technical support.