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

# Analytics Overview: Experiences & Experiments

> Understand Jurni analytics for individual experiences and experiments, including sessions, conversion rates, revenue, attribution, and how to compare performance responsibly.

Jurni analytics should help you answer a marketing question—not just give you another dashboard to watch.

Start with what you are trying to learn:

<CardGroup cols={2}>
  <Card title="How is this page performing?" icon="chart-line" href="/analytics-reporting/experience-analytics">
    Use experience analytics when you want to understand the traffic and outcomes associated with one Jurni experience.
  </Card>

  <Card title="Which treatment performed better?" icon="flask" href="/experiments/reading-results">
    Use experiment results when traffic was deliberately split across variants and you want to compare the treatments.
  </Card>
</CardGroup>

That distinction matters. A page can receive direct traffic, campaign traffic, or experiment traffic. An experiment view is specifically about the traffic assigned through that test.

## The metrics marketers use most

| Metric               | The question it helps answer                               |
| -------------------- | ---------------------------------------------------------- |
| **Sessions**         | How many measured visits are in this view?                 |
| **Orders**           | How many attributed purchases came from those sessions?    |
| **Revenue**          | How much attributed revenue came from those sessions?      |
| **CVR**              | What share of sessions became orders?                      |
| **RPS**              | How much revenue did each session generate on average?     |
| **AOV**              | How much revenue did the average attributed order contain? |
| **CTR**              | What share of sessions included a tracked click?           |
| **Add-to-Cart Rate** | What share of sessions reached add to cart?                |
| **Checkout Rate**    | What share of sessions reached checkout?                   |

Use the [Metrics glossary](/data-dictionary/metrics-glossary) when you need the exact definition or formula.

## Start with the business outcome, then diagnose the journey

If the campaign goal is purchase conversion, start with CVR or the relevant revenue metric. Then use earlier steps to understand **where behavior changed**.

For example:

**Low CTR** can mean the page is not making the next action compelling or obvious.

**CTR is healthy but Add-to-Cart Rate is weak** can point to product, offer, or purchase-choice friction.

**Add-to-Cart Rate improves but CVR does not** means more people reached cart without a matching increase in orders. Look further down the journey before declaring the page better.

**CVR is flat but RPS improves** can mean the treatment influenced revenue per visit through order value or product mix.

These are diagnostic patterns, not automatic explanations. The numbers tell you **what changed**, not necessarily **why**.

## Use the same context when comparing performance

Before comparing two periods or views, check:

* date range
* campaign / traffic source
* audience
* offer
* product availability
* market / currency
* major page changes
* consent / tracking changes

A “better” week can simply contain different traffic or a stronger offer.

## Experience analytics versus experiment results

### Experience analytics

Use this when you want to understand the performance of a page itself across the traffic reaching it.

Good questions:

* How much traffic did this experience receive?
* What is its CVR / RPS?
* Where do shoppers progress or drop off?
* Did performance change after a page update?

Be careful when comparing two separate experiences that received different traffic. A higher CVR does not automatically mean one design caused the difference.

### Experiment results

Use this when you intentionally routed comparable traffic across variants through the experiment's Smart Link.

Good questions:

* Which variant leads on the metric we chose for this hypothesis?
* Did the treatment change earlier journey behavior?
* What did this test teach us?

For the complete marketer workflow, use [Run your first A/B test](/academy/playbooks/first-ab-test).

## Pick your primary metric before you chase the dashboard

A simple decision guide:

* **CVR** — purchase conversion is the main question.
* **RPS** — you care about the combined revenue effect of conversion and basket value.
* **AOV** — the treatment is specifically intended to influence order value among purchasers.
* **CTR / ATC / Checkout Rate** — the hypothesis is focused on an earlier step, or you are using these as diagnostics.

If the treatment changes subscription mix, bundles, quantities, or upsells, revenue metrics may tell a different story from CVR alone.

## Why Jurni can disagree with GA4, Meta, or another analytics platform

Different platforms can use different:

* session definitions
* identity / cookie rules
* attribution windows
* consent handling
* channel attribution
* timezone and date boundaries
* event deduplication

That means totals do not need to match exactly to be useful.

When investigating a large difference, align the date range and traffic first, then check the measurement path. Use [Why analytics platforms differ](/troubleshooting/analytics-differences) and [Attribution overview](/analytics-reporting/attribution-overview).

## Turn analytics into a next action

Use this prompt when you want Jurni AI to help organize what you see:

```text theme={null}
Here is the page context and Jurni analytics:

TRAFFIC / AUDIENCE
[context]

PAGE / OFFER CHANGES
[context]

METRICS
[paste relevant values]

Do not claim a cause from correlation.
Separate:
1. OBSERVATIONS — what changed
2. POSSIBLE FRICTION POINTS — hypotheses worth investigating
3. WHAT ELSE I SHOULD CHECK
4. 3 TESTABLE NEXT HYPOTHESES

Prioritize customer decisions over cosmetic changes.
```

Then use [Review a page before testing](/academy/playbooks/cro-review) or [Decide what to test next](/academy/playbooks/what-to-test) to turn the observation into a clean experiment.

## Continue

* [Experience analytics](/analytics-reporting/experience-analytics)
* [Conversion-step performance](/analytics-reporting/funnel-step-performance)
* [Reading experiment results](/experiments/reading-results)
* [Filtering analytics](/analytics-reporting/filtering-experiences)
* [Metrics glossary](/data-dictionary/metrics-glossary)


## Related topics

- [Read Experiment Results](/experiments/reading-results.md)
- [Experiment and Experience Session Counts Do Not Match](/troubleshooting/experiment-experience-sessions.md)
- [Filtering Data on Experiences](/analytics-reporting/filtering-experiences.md)
- [How to Set Up the Jurni MCP Connector](/integrations/mcp-connector.md)
- [Conversion Step Performance](/analytics-reporting/funnel-step-performance.md)