Custom Dimensions in Polar Analytics let you segment, label, and transform your data using rule-based logic. They are useful when you want to clean up raw values, group records into meaningful categories, extract parts of text, or build date-based and numeric breakdowns for reporting. The current feature set includes comparison operators, text functions, date functions, math functions, and regex-based pattern matching.

In this article, readers will learn:

Before using operators or functions, it helps to understand the structure of a Custom Dimension. Every rule is built from three parts:

A single When block can contain multiple conditions. These conditions can be combined with:

Examples:

Use operators to compare a dimension against a condition:

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For Is in list and Is not in list, remove spaces after each comma when formatting the list.

Functions let you return transformed values instead of raw dimension values. They are especially useful for cleaning labels, extracting parts of strings, formatting dates, and making simple calculations directly inside a Custom Dimension.

Use text functions to shorten, standardize, combine, or extract text from fields like SKUs, titles, tags, or UTM values.

Example workflow:

Date functions help you extract or reshape dates for time-based reporting. When using them, you must specify the date field to use, such as the shared Date dimension or a field like Customer First Order Date or Order Processed Date.

When using date functions, you need to specify which date to use. This will typically be a dimension like:

Math functions let you calculate or standardize numeric values in Custom Dimensions.

For more advanced use cases, Custom Dimensions also support regex-based extraction through the Regexp(subject, pattern) function. This is helpful when values are embedded inside structured text, JSON-like strings, notes, or tags.

If you'd like to break down your Total Sales by the day of the week they were processed (to identify your highest sales days over a given period), you can use a Custom Dimension like this:

What this does:

Note: The specific date range rule applied to the Order Processed Date doesn’t matter much—what’s important is that your report covers a wide enough date range to include meaningful data.

Once this Custom Dimension is created, add it as a breakdown in a Custom Report alongside Total Sales. You’ll instantly see which days drive the most revenue.

Example Insight:
In the sample report below, Saturdays had the highest Total Sales, while Tuesdays had the lowest—helping you identify weekly patterns and plan accordingly.

If you want to track performance by week, labeling your weeks clearly can help you compare trends more easily across months or years.

For example, if you're analyzing sales or sessions on a weekly basis, adding a prefix like "W-" to your week numbers makes charts and tables easier to read and interpret at a glance.

You can do this using the Concat function:

What this does:

Tip: This kind of labeling is especially helpful when visualizing data by week in bar charts or tables—clearer labels make insights easier to spot.

Custom Dimensions in Polar Analytics give you a powerful way to turn raw data into meaningful, actionable insights. By combining When / Then / Else logic with operators and functions, you can clean, categorize, and transform your data directly within your reports—without needing external tools.

To get the most value, start with simple rules, validate your outputs, and gradually layer in more advanced functions like date formatting, math calculations, or regex extraction. Small improvements in how your data is structured can lead to much clearer reporting and better decision-making.

As you build more Custom Dimensions, you’ll be able to tailor your analytics to match your exact business needs—making your dashboards more intuitive, scalable, and insightful.