Filtered Views

Creating Filtered Views is the new way of analyzing your data. You can now apply the filters you want to the entire page, making your analysis easier.

Let's assume that you are trying to analyze your campaigns per country, now that is very easy to do just by creating a new Filtered View.

Steps:

Click on the premade Filtered View to activate/deactivate the filtering function, and click on the edit option to adjust your settings if necessary. You can also delete the Filtered View entirely.

It is also possible to filter by metrics. To do this, select Metric in the field, choose the desired metric from the dropdown, and set the level at which the filtered view should apply. This level can be channel, medium, campaign, ad set, or ad.

The Device Type filter allows you to segment data based on the category of device. This can be used to differentiate between desktop, mobile, and tablet traffic. By filtering on device type, you can analyze how different devices impact campaign performance and adjust their strategies accordingly. For instance, you can optimize your website or ads for specific devices if you notice a higher conversion rate on, for example, mobile users.

The Device Brand filter enables the segmentation of traffic based on the brand of the device used by users. This includes popular brands such as Apple, Samsung, Google, etc. You can use this filter to understand user behavior across different device ecosystems. For example, if a particular brand's users are converting at a higher rate, marketers might consider tailoring their ads or landing pages for that brand's users to boost performance.

The Operating System filter allows you to segment your audience based on the operating system (OS) they are using, such as Windows, macOS, iOS, or Android. This filter is essential for evaluating how different platforms perform in terms of user engagement and conversion. If a marketing campaign is performing better on one OS (e.g., iOS), you can adjust your targeting or improve the experience on that platform to enhance results.

The IP Country filter helps you analyze data based on the geographic location of your users by detecting the IP address’s country. This is crucial for understanding regional performance and tailoring campaigns for specific countries. For instance, if a campaign is underperforming in one country but excels in another, you can adjust your messaging or offers to better align with local preferences and cultural nuances.

The Web/App filter enables segmentation based on whether users are accessing the content via a web browser or a mobile app. This is important for analyzing cross-platform performance and understanding where users are most engaged. For example, if you see better results from users on the app, you might prioritize app-based campaigns or optimize the app experience to increase conversions.

This allows you to create more flexible and dynamic filters using text patterns. For example, instead of manually selecting every variation of a campaign or channel name (e.g., "Spring2025", "Spring-25", "SP25"), you can now use a single regex pattern like Spring.* to capture all relevant entries at once. New campaigns with that naming convention will also automatically be included.
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Regex is especially helpful when dealing with inconsistent naming conventions or when you want to include or exclude a range of values with precision. Simply select the regex option when creating or editing a filter, and enter your desired pattern. We support commonly used regex patterns such as

Note: For security and performance reasons, some characters and patterns are not supported. Specifically, the following strings are disallowed: ;, --, /*, */, xp_, and sp_. If any of these are used, the regex will no longer function as expected.

How does the compare period work in metric filtered views?

If the compare period is activated and a metric filtered view is applied, the filtered views will include only the items from the current period that meet the filter criteria. In the example of a metric filter, the comparison metrics will then be calculated based on those same items from the previous period. For example:

Note: this behavior applies only to metric filters. Other types of filters (e.g., by channel or campaign) will still compare complete data sets for both periods.