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

# Analytics

> For a conceptual overview of Goal Analytics, Journey Analytics, and uplift measurement, see [Analytics](/concepts/analytics) in Product Concepts.

## 6.1 Goal Analytics

Goal Analytics shows performance toward your business goals.

### Accessing Goal Analytics

1. Click **Analytics** > **Goal Analytics**
2. Select an goal from the dropdown

### What is an Goal?

An **goal** is a measurable business goal:

* Increase purchases
* Boost app engagement
* Reduce churn
* Drive signups

### Key Metrics

| Metric | Description | Calculation |
| - | - | - |
| **Conversions** | Number of goal completions | Count of goal events |
| **Uplift** | Impact of treatments | (Treatment Rate - Control Rate) / Control Rate |

### Understanding Uplift

**Uplift** measures the incremental impact of your treatments.

```
Uplift = (Treatment Rate - Control Rate) / Control Rate × 100%
```

**Example:**

* Treatment conversion rate: 5.0%
* Control conversion rate: 4.0%
* Uplift = (5.0 - 4.0) / 4.0 = 25%

This means your treatments drove a 25% improvement in conversions.

### Goal Analytics Dashboard

The dashboard shows:

| Section | Content |
| - | - |
| **Summary KPIs** | Total conversions, conversion rate, uplift |
| **Trend Chart** | Performance over time |
| **Journey Breakdown** | Which journeys contribute to this goal |
| **Treatment Performance** | Individual treatment metrics |

### Date Range Selection

Filter analytics by time period:

* Today
* Yesterday
* Last 7 Days
* Last 30 Days
* Last 90 Days
* Custom range

### Interpreting Results

**Positive Uplift (Good):**

* Treatments are driving incremental conversions
* Your personalization strategy is working

**Zero/Negative Uplift (Investigate):**

* Treatments may not be effective
* Check content, targeting, or timing
* Consider A/B testing alternatives

***

## 6.2 Journey Analytics

Journey Analytics shows journey-level performance.

### Accessing Journey Analytics

1. Click **Analytics** > **Journey Analytics**
2. Select a journey from the dropdown

### Journey-Level Metrics

| Metric | Description |
| - | - |
| **Impressions** | Total treatment views |
| **Clicks** | Total treatment interactions |
| **CTR** | Click-through rate |
| **Conversions** | Goal completions |
| **Uplift** | Journey-level impact |

### Journey Analytics Dashboard

The dashboard includes:

| Section | Content |
| - | - |
| **Journey Summary** | Overall journey KPIs |
| **Treatment Comparison** | Side-by-side treatment metrics |
| **Surface Analysis** | Performance by surface |
| **Time Trends** | Daily/weekly performance |

### Treatment Comparison

Compare treatments within a journey:

| Treatment | Impressions | Clicks | CTR | Conversions |
| - | - | - | - | - |
| Welcome Banner v1 | 10,000 | 500 | 5.0% | 50 |
| Welcome Banner v2 | 10,000 | 600 | 6.0% | 55 |
| Welcome Modal | 8,000 | 400 | 5.0% | 48 |

Use this to identify top performers and optimize.

### Surface Analysis

See how different surfaces perform:

| Surface | Impressions | CTR |
| - | - | - |
| Home Screen | 15,000 | 5.5% |
| Product Page | 8,000 | 4.2% |
| Checkout | 5,000 | 6.8% |

### Filtering Options

Filter journey analytics by:

* Date range
* Treatment type
* Surface
* Status

***

## 6.3 Key Metrics Explained

### Engagement Metrics

**Impressions**

* Count of times treatments were shown to users
* Each view = one impression
* Same user can have multiple impressions

**Clicks**

* Count of user interactions with treatments
* Clicking CTA button
* Engaging with treatment content

**Click-Through Rate (CTR)**

```
CTR = Clicks / Impressions × 100%
```

Typical CTR benchmarks:

| Treatment Type | Good CTR |
| - | - |
| Push notification | 3-5% |
| In-app message | 5-10% |
| Banner | 2-4% |
| Modal | 8-15% |

### Conversion Metrics

**Conversions**

* Users who completed the goal action
* E.g., made a purchase, signed up, completed profile

**Conversion Rate**

```
Conversion Rate = Conversions / Users × 100%
```

### Impact Metrics

**Uplift**

* The incremental impact of treatments
* Compares treated users vs control group
* Positive uplift = treatments are working

**Statistical Significance**

* Whether uplift is reliable or due to chance
* Look for confidence indicators
* Generally need 95%+ confidence

***

## 6.4 Control Groups

### What is a Control Group?

A **control group** is a subset of users who don't receive treatments, used to measure true impact.

### How Control Groups Work

```
All Eligible Users
├── Treatment Group (e.g., 90%) → Receives treatments
└── Control Group (e.g., 10%) → No treatments
```

### Why Control Groups Matter

Without a control group, you can't know if conversions would have happened anyway.

**Example:**

* 1,000 users converted after seeing treatment
* But how many would have converted without treatment?
* Control group answers this question

### Control Group Size

Typical control group sizes:

* 5-10% of eligible users
* Must be large enough for statistical significance
* Balance: larger = better measurement, but fewer treated users

***

## 6.5 Reading Analytics Reports

### Time Series Charts

**What to look for:**

* Overall trends (up, down, flat)
* Sudden changes (investigate causes)
* Day-of-week patterns
* Seasonal effects

### Comparison Tables

**Sorting:**

* Click column headers to sort
* Identify top/bottom performers

**Filtering:**

* Use filters to focus analysis
* Compare similar treatments fairly

### Export Options

Export data for further analysis:

* CSV download
* Date range selection
* Metric selection

***

## 6.6 Analytics Best Practices

### Regular Review Cadence

| Frequency | Focus |
| - | - |
| **Daily** | Check for anomalies, monitor active journeys |
| **Weekly** | Review trends, identify optimization opportunities |
| **Monthly** | Full performance review, strategic planning |
| **Quarterly** | Long-term trends, goal reassessment |

### What to Monitor Daily

* Sudden drops in impressions (delivery issues?)
* Unusual CTR changes (content problems?)
* Conversion rate shifts (external factors?)

### What to Analyze Weekly

* Treatment comparison within journeys
* Surface performance patterns
* Uplift trends

### Optimization Process

1. **Measure** - Establish baseline metrics
2. **Analyze** - Identify improvement opportunities
3. **Hypothesize** - What change might improve results?
4. **Test** - Create variant treatments
5. **Learn** - Analyze results, apply learnings
6. **Repeat** - Continuous improvement

### Common Analysis Mistakes

| Mistake | Better Approach |
| - | - |
| Looking at metrics in isolation | Consider context and trends |
| Short time windows | Use sufficient data (7+ days) |
| Ignoring statistical significance | Wait for confident results |
| Comparing dissimilar treatments | Control for variables |

***

## 6.7 Attribution

### What is Attribution?

Attribution determines which treatments get credit for conversions.

### Attribution Window

The time period during which a conversion is attributed to a treatment.

**Example:** If attribution window is 7 days:

* User sees treatment on Day 1
* User converts on Day 5
* Conversion is attributed to the treatment

### Multi-Touch Attribution

When users see multiple treatments:

* First touch: Credit to first treatment
* Last touch: Credit to last treatment
* Multi-touch: Credit distributed

> **Note:** Attribution settings are configured by your admin. Contact them for details on your attribution model.

***

## 6.8 Analytics Troubleshooting

### No Data Showing

**Possible Causes:**

* Date range doesn't include activity
* Journey/treatments are new
* Filters are too restrictive
* Data processing delay

**Actions:**

* Adjust date range
* Remove filters
* Wait for data processing (up to 24 hours)

### Metrics Look Wrong

**Check:**

* Correct goal/journey selected
* Date range is appropriate
* No filters hiding data
* Compare to raw treatment counts

### Uplift is Negative

**Investigate:**

* Treatment content issues
* Wrong audience targeting
* Timing problems
* External factors affecting control differently

### CTR is Zero

**Check:**

* Treatments are actually delivering (impressions > 0)
* CTA is visible and clickable
* Tracking is configured correctly

***

## 6.9 Advanced Analytics Features

### Cohort Analysis

Analyze performance by user cohorts:

* Sign-up date cohorts
* Behavioral cohorts
* Segment-based analysis

### A/B Testing Analysis

Compare treatment variants:

* Statistical significance indicators
* Winner determination
* Confidence intervals

### Custom Metrics

Some organizations configure custom metrics. Check with your admin for available custom metrics.

***

## 6.10 Analytics Permissions

### Who Can Access Analytics

| Permission | Access Level |
| - | - |
| ANALYZE\_OBJECTIVE\_VIEW | View goal analytics |
| ANALYZE\_PROGRAM\_VIEW | View journey analytics |
| TREATMENT\_VIEW | View treatment details |

### Requesting Access

If you can't access analytics:

1. Note the specific section you need
2. Contact your admin
3. Request appropriate permissions

***

## Next Section

Continue to [Section 7: Agent and AI Insights](/guides/comprehensive/ai-insights) for AI-powered analytics documentation.