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

# Experience Analytics

> Understand and analyze performance metrics for your experiences

Analytics help you understand how your experiences perform and make data-driven decisions. This guide covers the metrics available for each experience type and how to interpret them.

## Analytics Overview

Access experience analytics from:

* **Dashboard** - Aggregate metrics across all experiences
* **Experience Detail** - Specific experience performance

*Caption: The dashboard provides an overview of all experience performance*

## Common Metrics

These metrics apply to all experience types:

### Entry Metrics

| Metric              | Description                        | Good Indicator             |
| ------------------- | ---------------------------------- | -------------------------- |
| **Total Entries**   | Number of consumers who entered    | Higher is generally better |
| **Entry Rate**      | Entries per hour/day               | Consistent or growing      |
| **Unique Entrants** | Distinct consumers                 | Higher conversion          |
| **Repeat Attempts** | Consumers who tried multiple times | Interest level             |

### Conversion Metrics

| Metric              | Description                         | Benchmark         |
| ------------------- | ----------------------------------- | ----------------- |
| **Completion Rate** | Entries that completed successfully | 70-90% for queues |
| **Drop-off Rate**   | Entries that didn't complete        | Under 30%         |
| **Timeout Rate**    | Entries that expired                | Under 10%         |

### Engagement Metrics

| Metric                 | Description                       |
| ---------------------- | --------------------------------- |
| **Time in Experience** | Average duration of participation |
| **Page Views**         | Total views of experience pages   |
| **Return Visits**      | Consumers who came back           |

## Queue Analytics

*Caption: Queue analytics show position distribution and completion rates*

### Queue-Specific Metrics

| Metric                    | Description                  | What It Tells You   |
| ------------------------- | ---------------------------- | ------------------- |
| **Current Queue Length**  | Active consumers waiting     | Current demand      |
| **Average Wait Time**     | Time from entry to service   | Consumer experience |
| **Processing Rate**       | Consumers served per hour    | Throughput          |
| **Position Distribution** | Where consumers are in queue | Bottlenecks         |

### Queue Funnel

Track consumers through the queue stages:

1. **Entered** - Joined the queue
2. **Waiting** - Currently in line
3. **At Front** - Ready to checkout
4. **Completed** - Successfully purchased
5. **Dropped** - Left without completing

*Caption: The funnel shows where consumers drop off in the queue process*

### Queue Performance Indicators

| Indicator       | Good      | Warning   | Critical |
| --------------- | --------- | --------- | -------- |
| Completion Rate | > 80%     | 60-80%    | \< 60%   |
| Average Wait    | \< 30 min | 30-60 min | > 60 min |
| Timeout Rate    | \< 5%     | 5-15%     | > 15%    |

## Draw Analytics

*Caption: Draw analytics track entry patterns and winner outcomes*

### Draw-Specific Metrics

| Metric                       | Description                 |
| ---------------------------- | --------------------------- |
| **Entry Timeline**           | When entries were submitted |
| **Entry Sources**            | Where entries came from     |
| **Winner Confirmation Rate** | Winners who confirmed       |
| **Forfeit Rate**             | Winners who didn't claim    |
| **Alternate Promotion Rate** | Backups who became winners  |

### Draw Performance Indicators

| Indicator           | Good   | Warning | Critical |
| ------------------- | ------ | ------- | -------- |
| Entry Goal Met      | > 100% | 50-100% | \< 50%   |
| Winner Confirmation | > 90%  | 70-90%  | \< 70%   |
| Forfeit Rate        | \< 10% | 10-25%  | > 25%    |

## Auction Analytics

*Caption: Auction analytics track bidding activity and price progression*

### Auction-Specific Metrics

| Metric                | Description                 |
| --------------------- | --------------------------- |
| **Total Bids**        | Number of bids placed       |
| **Unique Bidders**    | Distinct participants       |
| **Bid Timeline**      | When bids were placed       |
| **Price Progression** | How the price changed       |
| **Reserve Met**       | Whether reserve was reached |
| **Final Price**       | Winning bid amount          |

### Auction Performance Indicators

| Indicator          | Good   | Warning | Critical |
| ------------------ | ------ | ------- | -------- |
| Bidder Count       | > 10   | 3-10    | \< 3     |
| Bids Per Bidder    | > 3    | 1-3     | 1        |
| Reserve Met        | Yes    | Close   | No       |
| Final vs. Expected | > 100% | 75-100% | \< 75%   |

## Appointment Analytics

*Caption: Appointment analytics show booking patterns and slot utilization*

### Appointment-Specific Metrics

| Metric                | Description                  |
| --------------------- | ---------------------------- |
| **Booking Rate**      | Slots booked vs. available   |
| **Popular Times**     | Most requested time slots    |
| **Lead Time**         | How far ahead people book    |
| **Cancellation Rate** | Bookings that were cancelled |
| **No-Show Rate**      | Consumers who didn't arrive  |
| **Reschedule Rate**   | Bookings that were changed   |

### Appointment Performance Indicators

| Indicator         | Good   | Warning | Critical |
| ----------------- | ------ | ------- | -------- |
| Utilization       | > 80%  | 50-80%  | \< 50%   |
| No-Show Rate      | \< 5%  | 5-15%   | > 15%    |
| Cancellation Rate | \< 10% | 10-20%  | > 20%    |

## Analytics Dashboard Features

### Time Period Selection

View analytics for different time ranges:

* Last 24 hours
* Last 7 days
* Last 30 days
* Last 90 days
* Custom range

*Caption: Select different time periods to analyze trends*

### Auto-Refresh

Enable auto-refresh to see real-time updates:

* Toggle auto-refresh on/off
* 30-second refresh interval
* Manual refresh available

### View Toggle

Switch between analytics views:

* **Channel Attribution** - Traffic sources and conversion paths
* **Audience Segmentation** - Metrics by audience segment

## Exporting Data

Export analytics for further analysis:

1. Navigate to the analytics view
2. Click **Export** or download icon
3. Choose format (CSV, Excel)
4. Select date range
5. Download the file

*Caption: Export analytics data for offline analysis*

### Available Exports

| Export          | Contents           |
| --------------- | ------------------ |
| **Summary**     | High-level metrics |
| **Entries**     | All entry records  |
| **Timeline**    | Time-series data   |
| **Full Detail** | Complete data dump |

## Interpreting Results

### Identifying Issues

Use analytics to spot problems:

| Symptom        | Possible Cause   | Solution           |
| -------------- | ---------------- | ------------------ |
| Low entries    | Poor promotion   | Increase marketing |
| High drop-off  | Complex process  | Simplify checkout  |
| High timeout   | Short window     | Increase timeout   |
| Low conversion | Technical issues | Check error logs   |

### Benchmarking

Compare your results to benchmarks:

| Metric     | Poor     | Average   | Good       | Excellent |
| ---------- | -------- | --------- | ---------- | --------- |
| Entry Rate | \< 50/hr | 50-200/hr | 200-500/hr | > 500/hr  |
| Completion | \< 50%   | 50-70%    | 70-85%     | > 85%     |
| Engagement | \< 2 min | 2-5 min   | 5-10 min   | > 10 min  |

### Trend Analysis

Look for patterns over time:

* **Growing** - More entries/completions each experience
* **Stable** - Consistent performance
* **Declining** - Investigate causes

## Real-Time Monitoring

### During Active Experiences

Monitor these in real-time:

* Current entry count
* Processing rate
* Error rate
* Queue length (for queues)

### Alert Thresholds

Set up alerts for:

* Entry rate drops suddenly
* Error rate exceeds threshold
* Inventory running low
* Processing delays

## Best Practices

### Before Launch

1. **Set goals** - Define success metrics
2. **Establish baselines** - Know what good looks like
3. **Configure alerts** - Get notified of issues

### During Experience

1. **Monitor frequently** - Check every 15-30 minutes
2. **React quickly** - Address issues immediately
3. **Document observations** - Note anything unusual

### After Completion

1. **Review thoroughly** - Analyze all metrics
2. **Compare to goals** - Did you meet targets?
3. **Document learnings** - Record insights
4. **Plan improvements** - Apply to future experiences

## Related Guides

* [Dashboard Overview](/dashboard/overview) - Main analytics dashboard
* [Experience Schedule](/dashboard/experiences/experience-lifecycle) - Understanding derived timing
* [Analytics Dashboard](/dashboard/analytics/overview) - Organization-wide analytics