> ## Documentation Index
> Fetch the complete documentation index at: https://docs.duckie.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Breakdown Analysis

> Visualize ticket distribution

Breakdown analysis visualizes how conversations are distributed across categories and attributes — essential for understanding what customers are asking about.

{/* Screenshot: Breakdown page showing pie chart and bar charts */}

## Category Breakdown

See distribution by conversation type:

{/* Screenshot: Pie chart showing category distribution */}

**Example:**

* Billing: 35%
* Technical Support: 40%
* Account Issues: 15%
* Feature Requests: 5%
* General: 5%

### What It Tells You

* Which topics dominate support volume
* Where to focus knowledge improvements
* Staffing and resource allocation needs

## Attribute Breakdown

See distribution per attribute:

{/* Screenshot: Bar chart showing attribute distribution */}

**Priority:**

* High: 20%
* Medium: 45%
* Low: 35%

**Product Area:**

* Dashboard: 50%
* API: 30%
* Mobile: 20%

## Viewing Breakdown

<Steps>
  <Step title="Navigate to Breakdown">
    Go to **Analyze → Breakdown**.
  </Step>

  <Step title="Select View">
    Choose Category view or Attribute view.
  </Step>

  <Step title="Select Date Range">
    Choose the time period to analyze.
  </Step>

  <Step title="View Charts">
    See distribution visualizations.
  </Step>
</Steps>

## Drill-Down

Click any segment to see matching conversations:

{/* Screenshot: Run list filtered by clicked segment */}

1. Click a pie slice or bar
2. See list of conversations in that segment
3. Click individual runs for details

This helps you:

* Understand what's in each category
* Identify patterns within segments
* Find examples for training

## Comparing Periods

{/* Screenshot: Side-by-side breakdown comparison */}

Compare distribution across time:

| Category  | This Week | Last Week | Change |
| --------- | --------- | --------- | ------ |
| Billing   | 35%       | 30%       | +5%    |
| Technical | 40%       | 45%       | -5%    |
| Account   | 15%       | 15%       | —      |

**What changes tell you:**

* Emerging issues (category growing)
* Resolved issues (category shrinking)
* Seasonal patterns

## Cross-Analysis

Combine category and attribute analysis:

{/* Screenshot: Breakdown by category, then by attribute within category */}

**High Priority by Category:**

* Technical: 45%
* Billing: 35%
* Account: 15%

This reveals:

* Which categories have urgent issues
* Where to prioritize improvements
* Resource allocation needs

## Metrics by Segment

See performance metrics per segment:

| Category  | Volume | Resolution Rate | Escalation Rate |
| --------- | ------ | --------------- | --------------- |
| Billing   | 350    | 82%             | 8%              |
| Technical | 400    | 68%             | 15%             |
| Account   | 150    | 91%             | 3%              |

**Takeaways:**

* Technical has lower resolution — needs more knowledge
* Account resolves well — keep doing what works
* Billing escalation is moderate — check guardrails

## What Breakdown Shows

### High-Volume Categories

If a category dominates:

* Ensure knowledge coverage is excellent
* Consider specialized agents
* Review common questions

### Growing Categories

If a category is increasing:

* Investigate root cause
* Add knowledge proactively
* Consider product changes

### Low Resolution Categories

If a category resolves poorly:

* Review knowledge for gaps
* Check agent configuration
* Analyze escalation reasons

## Exporting Data

Export breakdown data for further analysis:

{/* Screenshot: Export button and options */}

* **CSV:** Raw data for spreadsheets
* **Chart image:** For presentations

## Next Steps

<CardGroup cols={2}>
  <Card title="Performance Metrics" icon="chart-line" href="/analytics/performance-metrics">
    See aggregate KPIs
  </Card>

  <Card title="Run History" icon="clock-rotate-left" href="/analytics/runs">
    Investigate individual runs
  </Card>
</CardGroup>