Airgentic Help
The Conversational Trends tab on the Insights screen helps you understand what your customers are asking about over time — organised into stable themes you can track week to week.
While the other Insights tabs show individual conversations and point-in-time metrics, Conversational Trends reveals patterns: which topics are growing, which are declining, and what new questions are emerging.
Conversational Trends uses AI to analyse the questions users ask your service. It groups similar questions together based on their meaning and organises them into a two-layer structure:
Questions → Clusters → Themes
At the lowest level, the system captures the standalone question from each user interaction — a clear, self-contained version of what the user was asking. Questions that are semantically similar (asking about the same thing in different words) are grouped together.
Clusters are groups of very similar questions discovered during a specific analysis period (typically a week). The system uses AI to identify questions that share the same intent and groups them together.
For example, these questions would likely end up in the same cluster:
Clusters are ephemeral — each analysis run discovers fresh clusters based on the questions in that time period. This means clusters can reveal new, specific issues as they emerge.
Themes are stable, persistent categories that exist across time. While clusters come and go with each analysis, themes provide continuity for tracking and reporting.
Examples of themes might include:
Each theme has an AI-generated label and description that captures the common intent behind the questions it contains. When new clusters are discovered, the system automatically maps them to existing themes — or creates new themes if the cluster represents something genuinely new.
Themes are designed to stay consistent so you can track trends week over week and month over month.
Each analysis run:
This two-layer approach gives you both stability (themes for consistent reporting) and discovery (clusters for spotting emerging patterns).
The Trends tab shows your themes and clusters in three connected views.
The default view displays a table of all themes for your service:
| Column | Description |
|---|---|
| Theme | The name of the theme (e.g. "Connectivity Issues"). |
| Status | A badge showing the theme's current state — Active, Emerging, Declining, or Inactive. |
| Usage | The total number of questions mapped to this theme in the most recent analysis period. |
| Trend | An indicator showing whether usage is going up, down, or staying flat compared to the previous period. |
| First Seen | When this theme was first identified. |
Click View on any row to drill into that theme.
When you select a theme, you'll see:
Each cluster row shows:
| Column | Description |
|---|---|
| Cluster | A short label describing the cluster (e.g. "Wi-Fi drops after firmware update"). |
| Usage | The total number of times questions in this cluster were asked. If 10 users asked similar questions, usage = 10. |
| Members | The count of distinct question phrasings in the cluster. If those 10 users asked 3 different variations of the same question, members = 3. |
| Match | A confidence score (0–1) showing how well this cluster fits the theme. A score of 1.0 means the AI is very confident this cluster belongs here. |
Usage vs Members: A cluster about "password reset" might have 50 usage (50 total questions asked) but only 8 members (8 unique phrasings). High usage with low members suggests a common, well-understood topic. High members with low usage suggests many variations of a less common question.
Click View on any cluster row to see the actual questions.
The cluster detail view shows:
The Mapping section shows:
| Field | Description |
|---|---|
| Theme | The canonical theme this cluster is mapped to. |
| Method | How the mapping was made — llm means AI matched it automatically, seed means it was created when the theme was first established. |
| Confidence | How confident the AI was in the match (0–1). Higher is better. |
| Review | Whether the mapping has been verified — suggested means automatically assigned, confirmed means a human has verified it's correct. |
Use this view to understand exactly what users are asking and whether the questions are being grouped correctly. If a cluster seems mis-matched to its theme, the example questions will help you understand what's really being asked.
Themes move through a lifecycle as your service evolves:
| Status | Meaning |
|---|---|
| Active | This theme has been seen consistently and represents an established category. |
| Emerging | This theme is new or growing — it has appeared recently or is seeing increased activity. Watch these closely. |
| Declining | This theme has seen reduced activity recently. Users may be asking about this topic less often. |
| Inactive | This theme hasn't been seen in several analysis periods and may no longer be relevant. |
The system automatically updates theme statuses based on usage trends across analysis runs.
New themes can appear in two ways:
Seeding — When you run your first analysis, the system creates initial themes based on the clusters it discovers. These become your baseline.
Emergence — In subsequent runs, if a cluster is discovered that doesn't match any existing theme well, the system creates a new theme for it. These start with "Emerging" status.
Emerging themes are worth watching closely — they can indicate new product issues, changing customer needs, or gaps in your content.
The system analyses questions periodically. To run a new analysis:
The analysis will process questions from the selected period, group them into clusters, and map those clusters to themes. This may take a few moments depending on the volume of data.
Click Refresh to reload the results after generation completes.
Conversational Trends is designed to help you improve your AI service over time. Here are some practical ways to use it:
If a theme or cluster shows high volume but your AI is struggling to answer (check the main Insights tab for completeness), you may need to add or improve content on that topic.
After updating prompts, adding content, or making configuration changes, use Trends to see whether the affected themes show improved outcomes in subsequent periods.
Watch for new "Emerging" themes or clusters that suddenly appear with high volume — these often indicate a new product issue, a marketing campaign driving questions, or a gap in your knowledge base.
The stable theme structure makes it easy to report on customer intent over time. Themes stay consistent, so your reports can compare apples to apples.