Application Pipeline
  ├─ 142 Applications Submitted (100%)
  ├─ 45 Responded (31.7%)
  ├─ 12 Interviews Scheduled (8.5%)
  ├─ 6 Final Rounds (4.2%)
  └─ 3 Offers Received (2.1%)
```text

<Info>
  The funnel visualization helps identify bottlenecks in your application process and areas for improvement.
</Info>

### Weekly Activity Trends

Track application submission patterns:

| Week | Applications | Responses | Interviews | Offers |
|------|--------------|-----------|------------|--------|
| Week 1 | 28 | 9 | 2 | 0 |
| Week 2 | 35 | 11 | 3 | 1 |
| Week 3 | 42 | 14 | 4 | 1 |
| Week 4 | 37 | 11 | 3 | 1 |

## Success Metrics

### Response Rate by Company Size

Analyze how company size affects response rates:

<Tabs>
  <Tab title="Startups (<50)">
    **Response Rate**: 42%
    
    **Average Response Time**: 3.1 days
    
    **Interview Rate**: 12%
    
    Higher engagement but more volatile
  </Tab>
  
  <Tab title="Mid-Size (50-500)">
    **Response Rate**: 28%
    
    **Average Response Time**: 5.8 days
    
    **Interview Rate**: 9%
    
    Balanced response with structured process
  </Tab>
  
  <Tab title="Enterprise (500+)">
    **Response Rate**: 18%
    
    **Average Response Time**: 8.4 days
    
    **Interview Rate**: 6%
    
    Lower response but higher conversion
  </Tab>
</Tabs>

### Industry Performance

Breakdown by industry sector:

| Industry | Applications | Response Rate | Offer Rate |
|----------|--------------|---------------|------------|
| **AI/ML** | 45 | 35% | 3.2% |
| **Cloud/Infrastructure** | 38 | 28% | 2.8% |
| **FinTech** | 32 | 22% | 2.1% |
| **E-commerce** | 27 | 31% | 1.9% |

## Active Pipeline Analytics

Track your **18 active opportunities** currently in discussion:

### Pipeline Stage Distribution

```text
Active Pipeline (18 total)
  ├─ Initial Contact: 6 companies (33%)
  ├─ Screening Round: 5 companies (28%)
  ├─ Technical Interview: 4 companies (22%)
  ├─ Final Round: 2 companies (11%)
  └─ Offer Negotiation: 1 company (6%)
```text

### Pipeline Velocity

<Card title="Average Time per Stage" icon="gauge">
  - **Application to Response**: 5.2 days
  - **Response to First Interview**: 8.3 days
  - **First to Final Interview**: 12.7 days
  - **Final Interview to Offer**: 6.1 days
  
  **Total Cycle Time**: ~32 days average
</Card>

## Network Health

Monitor communication effectiveness:

### Response Rate Efficiency

<Info>
  **Current Score: 82%**
  
  Measures your response rate to recruiter communications and interview follow-ups.
  
  **Breakdown**:
  - Follow-up emails sent: 95%
  - Response within 24 hours: 87%
  - Thank you notes sent: 94%
  - LinkedIn connections made: 68%
</Info>

### Email Engagement

```text
Email Performance
  ├─ Open Rate: 78%
  ├─ Response Rate: 65%
  ├─ Follow-up Success: 82%
  └─ Average Response Time: 4.2 hours
```text

## Time-Based Analytics

### Monthly Comparison

Track progress over time:

<Accordion title="Current Month">
  - Applications: 54 (+12% vs last month)
  - Interviews: 5 (+25% vs last month)
  - Offers: 1 (same as last month)
  - Average response time: 4.8 days (-8% improvement)
</Accordion>

<Accordion title="Last Month">
  - Applications: 48
  - Interviews: 4
  - Offers: 1
  - Average response time: 5.2 days
</Accordion>

<Accordion title="Two Months Ago">
  - Applications: 40
  - Interviews: 3
  - Offers: 1
  - Average response time: 6.1 days
</Accordion>

### Best Application Days

Optimize submission timing:

| Day | Applications | Response Rate | Best Time |
|-----|--------------|---------------|------------|
| **Tuesday** | 28 | 34% | 9-11 AM |
| **Wednesday** | 31 | 32% | 10 AM-12 PM |
| **Thursday** | 25 | 29% | 9-11 AM |
| **Monday** | 22 | 26% | 11 AM-1 PM |
| **Friday** | 18 | 21% | 9-10 AM |

<Warning>
  **Insight**: Tuesday and Wednesday mornings show highest response rates. Consider focusing applications during these windows.
</Warning>

## Resume Performance

Analyze which resume versions perform best:

### Resume Versions

<CardGroup cols={2}>
  <Card title="Resume v3.2 (AI-Focused)" icon="file-text">
    **Used in**: 48 applications
    
    **Response Rate**: 35%
    
    **Interview Rate**: 10.4%
    
    Best for AI/ML roles
  </Card>
  
  <Card title="Resume v3.1 (Full-Stack)" icon="file-text">
    **Used in**: 52 applications
    
    **Response Rate**: 28%
    
    **Interview Rate**: 7.7%
    
    Best for engineering roles
  </Card>
  
  <Card title="Resume v2.9 (Product)" icon="file-text">
    **Used in**: 27 applications
    
    **Response Rate**: 26%
    
    **Interview Rate**: 7.4%
    
    Best for product roles
  </Card>
  
  <Card title="Resume v2.8 (Generalist)" icon="file-text">
    **Used in**: 15 applications
    
    **Response Rate**: 20%
    
    **Interview Rate**: 6.7%
    
    General purpose version
  </Card>
</CardGroup>

## Geographic Analysis

Performance by location:

### Application Distribution

| Location | Applications | Response Rate | Avg Salary Range |
|----------|--------------|---------------|------------------|
| **San Francisco** | 52 | 32% | $180K-$250K |
| **New York** | 38 | 28% | $170K-$230K |
| **Seattle** | 28 | 31% | $165K-$220K |
| **Remote** | 24 | 35% | $150K-$200K |

## Comparative Benchmarks

How you compare to average job seekers:

<Card title="Your Performance vs Average" icon="bar-chart">
  | Metric | You | Average | Difference |
  |--------|-----|---------|------------|
  | Response Rate | 31.7% | 24% | +7.7% |
  | Interview Rate | 8.5% | 6.5% | +2.0% |
  | Offer Rate | 2.4% | 1.8% | +0.6% |
  | Response Time | 5.2 days | 7.8 days | -2.6 days |
</Card>

<Info>
  You're performing above average in all key metrics! Your response rate is particularly strong.
</Info>

## AI Insights

Machine learning-powered recommendations:

### Optimization Suggestions

<Accordion title="Application Timing">
  **Recommendation**: Focus applications on Tuesday-Wednesday mornings
  
  **Impact**: Potential +8% increase in response rate
  
  **Data**: Based on 142 applications showing 34% response rate on optimal days vs 26% on others
</Accordion>

<Accordion title="Resume Optimization">
  **Recommendation**: Use Resume v3.2 for AI/ML roles
  
  **Impact**: Potential +7% increase in response rate
  
  **Data**: v3.2 shows 35% response rate vs 28% for other versions in AI sector
</Accordion>

<Accordion title="Company Size Focus">
  **Recommendation**: Increase startup applications
  
  **Impact**: Potential +14% increase in response rate
  
  **Data**: Startups show 42% response rate vs 28% for mid-size companies
</Accordion>

<Accordion title="Follow-Up Strategy">
  **Recommendation**: Send follow-up after 5 days if no response
  
  **Impact**: Potential +12% increase in getting replies
  
  **Data**: Follow-ups at 5-day mark show highest success rate
</Accordion>

## Export & Reporting

Generate detailed reports:

<Steps>
  <Step title="Select Date Range">
    Choose the time period for your report (week, month, quarter, year)
  </Step>
  
  <Step title="Choose Metrics">
    Select which analytics to include in the export
  </Step>
  
  <Step title="Select Format">
    Export as PDF report, CSV data, or PowerPoint presentation
  </Step>
  
  <Step title="Generate Report">
    Download comprehensive analytics report with visualizations
  </Step>
</Steps>

## Real-Time Tracking

All analytics update automatically:

- **Live Sync**: Data refreshes every 15 minutes
- **Email Integration**: Automatic detection of new responses
- **Calendar Sync**: Interview schedules update analytics
- **Status Changes**: Application updates reflect immediately

## Design Specifications

### Analytics Cards

```text
Metric Card
  Border: 1px solid zinc-100
  Background: white
  Padding: 32px (2rem)
  Border Radius: 32px (2rem)
  Shadow: Hover shadow with blue tint
  Transition: Smooth scale on hover
```text

### Color Coding

- **Positive Trends**: Emerald green (#10b981)
- **Neutral Data**: Zinc gray (#71717a)
- **Negative Trends**: Red (#ef4444)
- **Primary Blue**: #1C4ED8

<Note>
  Analytics data is updated in real-time as you track applications, schedule interviews, and receive responses. All metrics are calculated automatically.
</Note>