# Profile

Use **Profile** to analyze user attributes and their values across your audience.

This is useful when you want to understand who your users are, not just what they did.

### What you can analyze

Profile analysis helps you answer questions like:

* Which cities or countries are most common?
* How are loyalty levels distributed?
* Which user traits changed during a release or campaign period?
* Which attribute values define a high-value audience?

### Choose the time period

You can analyze profile data for:

* **Last Week**
* **Last Month**
* **Last Year**
* **Dates Between**

Use a custom range when you want to focus on a launch, campaign, or seasonal window.

### Select attributes and values

Start by choosing the profile attribute you want to inspect.

Examples:

* city,
* membership level,
* preferred store,
* subscription status,
* or any custom profile attribute.

Then narrow the analysis with specific attribute values when needed.

This lets you focus on a smaller part of the user base and compare distributions more clearly.

<figure><img src="https://1642824329-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FX6uilbEAw42gqsudlclY%2Fuploads%2FeOzQnEvV9s7SU57B2Xd8%2FScreenshot%202023-08-25%20at%2015.31.25.png?alt=media&#x26;token=1f6dc4b1-74b2-4ee3-b501-43c8da82666b" alt=""><figcaption></figcaption></figure>

### How to use the results

Use the output to:

* build better segments,
* personalize campaigns,
* compare user groups,
* and validate whether profile enrichment is working as expected.

For example, if a loyalty tier is missing for a large share of users, that usually points to a data quality gap.

<figure><img src="https://1642824329-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FX6uilbEAw42gqsudlclY%2Fuploads%2Fa0gRcl2UMrAHcjg6wYvy%2FScreenshot%202023-08-16%20at%2016.54.16.png?alt=media&#x26;token=ab66eeb0-f765-4d44-a18d-1d0d3779f1ff" alt="" width="563"><figcaption></figcaption></figure>

### Best practices

* Start with one important attribute.
* Use a clean date range.
* Narrow by value only when you need a sharper view.
* Compare profile findings with [People Insight](https://user.netmera.com/netmera-user-guide/reports-and-analytics/analytics/people-insight) or [Event Insight](https://user.netmera.com/netmera-user-guide/reports-and-analytics/analytics/event-insight) when behavior matters too.