Why Use Topic Discovery?
| Challenge | How Topic Discovery Helps |
|---|---|
| Theme Discovery | Identifies recurring conversation topics and patterns across interactions. |
| Emerging Issues | Detects new customer issues early before they escalate. |
| Performance Trends | Tracks sentiment, resolution, and AHT trends by topic. |
| Coaching Focus | Highlights low-performing topics for targeted coaching. |
| Topic Analysis | Supports drill-down analysis from topic trends to individual conversations. |
| Taxonomy Expansion | Discovers AI-generated themes outside the configured taxonomy. |
Key Capabilities
- Topic Performance Analysis: Analyze topics using sentiment, resolution, and AHT metrics.
- Advanced Filtering: Filter data by channel, direction, language, queue, and agent.
- Intent Comparison: Compare configured intents with AI-generated topics.
- Trend Drill-down: Move from topic-level trends to individual conversations.
- Hierarchical Topic Analysis: Track trends across L1, L2, and L3 topic levels.
- Coaching Support: Identify low-performing topics for targeted coaching and process improvement.
- Proactive Detection: Surface emerging issues early through AI-generated themes.
- Direction-aware Analysis: Filter all topic visualizations, metrics, and conversation lists by contact direction (Inbound or Outbound) within each channel.
Access Topic Discovery
Navigate to Quality AI > ANALYZE > Topic Discovery.
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, and matching topics highlight instantly. | When looking for a specific issue (for example, “Payment Failure”). |
| Configured or Generated Intents | Switch between taxonomy-based topics and AI-discovered themes. | Select Configured Intents for your taxonomy and 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 | Focus on conversations by sentiment. | Adjust the score range slider (0-10). The default is full range. | Narrow to low-sentiment conversations for quality monitoring. |
| Resolution | Filter by resolution success rates. | Adjust the score range slider (0-100). The default is full range. | Zero in on unresolved or low-resolution conversations. |
Advanced Topic Filters
Select Filters to access additional filtering options and refine the dashboard view.| Filter | Options |
|---|---|
| Channel | Filter by Voice or Chat or with additional selection for Inbound and Outbound interactions. |
| Language | Multi-select with search to select one or more languages, and allows removing individual selections or clearing all. |
| Queue | Narrow by specific queues, and the Agent filter updates automatically. |
| Agent | Search and select one or more agents based on selected queues. |
| AHT | Set minimum and maximum values in seconds or define an acceptable variance range. |
Bubble Visualization Canvas
Topics are displayed as color-coded bubbles to help analyze conversation volume, performance metrics, and topic relationships.Bubble Attributes
Each bubble encodes key topic attributes visually:| 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 to reveal patterns and clusters. |
| Labels | L1 topics: labels outside. L2 and L3: labels appear inside. |
Sentiment Color Coding
L3 sentiment aggregates to L2/L1 with color-coded trends.| Color | Sentiment |
|---|---|
| Green | Positive |
| Grey | Neutral |
| Red | Poor |
Resolution Color Coding
Topics use color codes by resolution rate, supporting combined analysis with sentiment.| Color | Resolution Rate |
|---|---|
| Red | 0-50% (Low) |
| Grey | 50-70% (Moderate) |
| Green | 70-100% (High) |
Bobble Tooltips
Hovering over a bubble shows a tooltip with key metrics for quick assessment and deeper analysis 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 | Conversation counts with trend indicators (spikes or dips in percentage). |
| Average Sentiment | Overall sentiment score with trend analysis. |
| Sentiment Breakdown | Distribution across positive, neutral, and negative interactions. |
Configured vs. Generated Intents
Topic Discovery provides two topic views to support different analysis needs.| View | Description | When to Use |
|---|---|---|
| Configured Intents | Displays topics based on your organization’s predefined taxonomy and trained conversation categories. | Use for monitoring known business categories, tracking taxonomy performance, and comparing historical trends. |
| Generated Intents | Uses AI to automatically discover conversation themes that may not exist in the configured taxonomy. | Use for identifying blind spots, detecting emerging issues, exploring unexpected conversation patterns, and expanding the taxonomy. |
Topic View Detail Pane
Select View Details from any bubble tooltip to open the detail pane for comprehensive analytics on a specific topic. The detail pane provides topic-level analytics, trend insights, and conversation-level drill-down.Overview Tab
Shows the Overview tab to analyze topic performance over time.Time Granularity
Daily displays day-by-day trends for short-term monitoring, while Weekly aggregates data into weekly trends for pattern analysis.Topic Metrics
| Metric | Description |
|---|---|
| Total Conversations % | Topic’s share of all conversations. |
| Average Sentiment Score | Overall sentiment with trend analysis. |
| Sentiment Breakdown | Distribution across emotional categories (Positive, neutral, and negative). |
| Average Handle Time (AHT) | AHT trend for the selected topic. |
| Average Resolution % | Resolution success rate analysis. |
| Top Keywords | Most frequent terms in topic conversations. |
| Emotion Detection | Top 6 emotions identified in conversations. |
Conversations Tab
Shows the Conversations tab with list and detailed views of interactions for analysis of the selected topic.
Conversation List Columns
| 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. |
Navigation and Access
- Sorting: Most recent conversations first.
- Pagination: 10 conversations per page.
- All Conversations: Opens Conversation Mining - Interactions with topic filters applied.
Full Conversation View
Select a conversation from the Conversations tab to open the full interaction view.Conversation Details
| Section | Content |
|---|---|
| Complete Thread | Full customer-agent interaction. |
| Topic Highlighting | Topic markers within the conversation. |
| Metadata | Channel, duration, resolution status, sentiment scores. |
| Timeline View | Chronological conversation flow. |
| Context | Queue, agent, and channel details. |
Analysis Tools
| 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.