Why Does the "Other" Row Appear in Google Analytics 4?

Seeing "(other)" pop up in your Google Analytics 4 reports can be frustrating and confusing. You're expecting to see clean data - like your top landing pages or campaigns - but instead, you get a mysterious bucket that lumps together a significant chunk of your traffic. Don't worry, your GA4 property isn't broken. This article explains exactly what the "(other)" row means, why it appears, and several practical ways you can work around it to get the clear insights you need.

What 'Other' in GA4 Actually Means

In Google Analytics 4, the "(other)" row is a catch-all category used to group infrequent or less common dimension values. Think of it as GA4’s way of saying, "There were many different values here, but none of them were significant enough to show individually, so I've bundled them together for you."

This happens when a report involves a high-cardinality dimension. In data terms, 'cardinality' simply means the number of unique values a dimension can have. For example:

The Device Category dimension has low cardinality because it only has three possible values: Desktop, Mobile, and Tablet.
The Page Path dimension, which shows the part of the URL after the domain name (e.g., /blog/my-post), can have very high cardinality if your website has thousands of unique pages.

When the number of unique values for a dimension in your report exceeds Google's processing limits, it lumps all the "long-tail" results - the values occurring least often - into the "(other)" row. It's a mechanism to maintain speed and performance in your standard reports, but it can hide valuable details if you don't know how to handle it.

High Cardinality: The Root Cause of the "(other)" Row

Google Analytics 4 processes your reports in two main ways: through pre-aggregated Standard Reports and on-demand Exploration Reports. The "(other)" row is a feature specifically of the Standard Reports.

To make your standard reports load quickly, GA4 pre-processes and stores common data combinations in daily aggregate tables. However, to keep this process manageable and cost-effective, Google sets limits on how many unique values it will process for a given dimension each day. If your property has more unique values than the limit for that dimension, the excess values are grouped under "(other)".

Imagine a coffee shop that makes a few popular drinks in big batches every morning (Pre-aggregated Standard Reports). They'll make lots of Lattes, Cappuccinos, and Americanos. But if a dozen customers throughout the day ask for an obscure, complex drink, the barista isn't going to make a huge batch. They'll just log those sales under a general "Specialty Drinks" category on their daily summary (The "other" row). If the shop owner wants to know exactly what those special drinks were, they'd have to go check the individual receipts (The Exploration Reports).

This is precisely why you run into the "(other)" row - the standard reports are designed for the most common trends, not every single detail.

Common Scenarios That Create High Cardinality

So what kind of data creates too many unique values? Here are the most frequent culprits that trigger the "(other)" row to appear in your GA4 reports.

Dynamically Generated URLs

One of the most common causes of high cardinality in the Page Path or Page Location dimensions is using dynamic URLs that include unique identifiers. Query parameters are the bits added to a URL after a question mark (?). If every user gets a unique parameter, you're creating a new page path for every single visit.

For example, look at these two URLs:

/product/running-shoe?session_id=123456789/product/running-shoe?session_id=987654321

Even though both users visited the same product page, GA4 sees them as two distinct page paths because of the unique session_id. If you have thousands of users, you will quickly have thousands of unique URLs, easily exceeding the cardinality limit and forcing GA4 to create an "(other)" row.

Using User IDs as Custom Dimensions

You should never send unique user-level identifiers like a User ID, client ID, or transaction ID as a standard custom dimension you plan to use in daily reporting. Each visitor has a unique ID, meaning the cardinality of that dimension will equal your number of visitors. This guarantees you'll hit the "(other)" threshold. While User ID has a designated field in GA4 for identifying unique users, putting it into a general-purpose dimension intended for segmentation will cause problems.

Large Product Catalogs and Item-Scoped Dimensions

If you run an e-commerce store with tens of thousands of products, dimensions like Item Name or Item ID naturally have high cardinality. A report showing performance by individual product SKU can easily surpass the processing limit for a standard GA4 property, resulting in many of your less popular items being grouped into the "(other)" row.

Overuse of High-Cardinality Custom Dimensions

Sometimes, the issue comes from well-intentioned but overly-granular tracking. For instance, you might create a custom dimension to capture the exact timestamp of an event (e.g., 'form_submission_timestamp'). Since every submission timestamp is unique, this dimension has nearly infinite cardinality and is guaranteed to cause issues in your standard reports.

How to Investigate and Minimize the "(other)" Row

While you can't always eliminate the "(other)" row completely, especially on high-traffic sites, you can use smarter analysis techniques and better data hygiene to understand what's inside it and reduce its impact.

1. Use Exploration Reports (This is the #1 solution)

The single best way to bypass the limitations of standard reports is to use Explorations. Explorations query your raw, unaggregated event data and don't rely on the pre-built tables that cause the "(other)" issue. They are purpose-built for deep-dive analysis and handling high-cardinality data.

If you see a large "(other)" value in the "Pages and screens" report, for example, here's how you can investigate it with an Exploration:

In the left-hand navigation, go to Explore and create a new Blank exploration.
In the Variables column on the left, click the "+" sign next to "Dimensions." Search for and import "Page path and screen class" or a similar page dimension.
Click the "+" sign next to "Metrics." Search for and import metrics like "Views" and "Sessions."
Drag the "Page path and screen class" dimension to the Rows configuration box.
Drag the "Views" and "Sessions" metrics to the Values configuration box.

The report that appears in the main canvas will show you the raw traffic data for all your page paths, without creating an "(other)" row. You can now see the long-tail pages that were previously hidden.

2. Clean Your URL Data

If dynamic URLs are your main problem, the best fix is to stop sending the unique query parameters to Google Analytics in the first place.

Option A: Exclude URL Query Parameters in GA4 Admin

The easiest fix, which requires no code, is to tell GA4 to ignore certain parameters altogether.

Go to Admin → Data Streams → Click on your web data stream.
Underneath your events, click on "Configure tag settings."
Under "Settings," click "Show all," then select "List unwanted URL query parameters."
Enter the parameters you want to exclude (e.g., session_id, gclid, fbclid, etc.).

GA4 will now strip these parameters from all incoming 'page_view' events, preventing them from creating high cardinality.

Option B: Modify Your Tracking Implementation

For more advanced users, you can use Google Tag Manager to modify the page location field before sending the data to GA4, which gives you more control and flexibility.

3. Be Strategic With Your Custom Dimensions

Audit the custom dimensions you're collecting. For each one, ask yourself: "Do I absolutely need to use this specific value for audience building or daily reporting?"

Avoid capturing highly unique identifiers like timestamps or user-specific IDs as event- or session-scoped custom dimensions. Whenever possible, group values into lower-cardinality categories. For instance, instead of tracking a specific blog tag (which could have hundreds of unique values), you might track a broader "Blog Category" dimension (which might only have 10-15 values).

4. Upgrade to Google Analytics 360

For large enterprise businesses, upgrading to the paid version, Google Analytics 360, is an option. It significantly increases the cardinality limits for standard reports and provides access to other advanced features. This is a big step solely to solve the "(other)" issue, but for companies dealing with massive data volumes, it's often a necessity for other reasons as well.

Final Thoughts

In short, the "(other)" row in GA4 isn't a glitch, but rather a built-in feature designed to keep your standard reports snappy by grouping less common data. You can overcome this limitation by cleaning up your data collection or, most effectively, by shifting your deep-dive analysis to GA4's flexible and powerful Exploration reports.

Running detailed analyses like this is a manual process, jumping between different reports and tools. At Graphed, we simplify this entire workflow. By connecting directly to your Google Analytics account and other data sources, you can ask for the data you need using plain English instead of building complex Explorations. So instead of wrestling with GA4 to avoid the "(other)" row, you can simply ask a question like, "Show me my top 50 landing pages by sessions last month," and get a clean, live-updating dashboard instantly. Sign up and give Graphed a try to see how much faster you can get to meaningful answers.